20+ Free Questions on Databricks Certified Data Analyst Associate Certification

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Databricks Certified Data Analyst Associate Certification exam is designed to assess your comprehension of Databricks and its core data analysis services. We are pleased to offer an updated set of over 25+ free questions for the Databricks Certified Data Analyst Associate Certification exam. These questions closely resemble the ones you’ll encounter in both Databricks Certified Data Analyst Associate practice tests and the real exam.

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Top 20+ Free Questions on Databricks Certified Data Analyst Associate Certification

Here, we will share valuable Databricks Certified Data Analyst Associate Certification free questions that are specifically designed for you:

Domain: Databricks SQL

Question 1. A company needs to analyze a large amount of data stored in its Hadoop cluster. Which of the following best describes the benefit of using Databricks SQL with a Hadoop cluster?

  1. Databricks SQL provides faster query processing than traditional Hadoop tools.
  2. Databricks SQL allows users to store and analyze data directly in Hadoop.
  3. Databricks SQL provides more advanced security features than Hadoop.
  4. Databricks SQL provides better support for unstructured data than Hadoop.

Correct Answer: A

Explanation:

Users can query structured and semi-structured data stored in a variety of data sources, including Hadoop, cloud storage, and databases, using the cloud-based data warehousing system Databricks SQL. Databricks SQL offers a unified analytics platform that enables customers to execute advanced analytics, query data using SQL, and create machine learning models all from the same platform. Traditional Hadoop tools like Hive and Pig might not be the most effective choice when it comes to analyzing enormous amounts of data stored in Hadoop clusters. To get these technologies to work as expected, a lot of manual adjustment and optimization is often necessary.

On the other hand, Databricks SQL offers several performance-enhancing features like columnar storage, query optimization, and caching and is tailored for cloud-based data warehousing. These features allow Databricks SQL to process queries significantly more quickly than conventional Hadoop tools, which accelerates the time it takes to gain insights. Moreover, Databricks SQL enables direct Hadoop data analysis without the need for data migration, making it a more effective and economical solution. To safeguard sensitive data stored in Hadoop, Databricks SQL also offers cutting-edge security capabilities including column-level encryption and access control. In conclusion, Databricks SQL’s superior performance, effective and affordable data analysis, and cutting-edge security features are the advantages of implementing it in a Hadoop cluster.

Option A is correct. It effectively conveys the key advantage of integrating Databricks SQL with a Hadoop cluster. The greatest advantage of using Databricks SQL with a Hadoop cluster is that it processes queries more quickly than other Hadoop tools do. This is due to the distributed SQL query engine used by Databricks SQL, which was created particularly for huge data workloads and enables quicker data processing and analysis.

Option B is incorrect. It is not quite correct to say that Databricks SQL enables customers to store and analyze data directly in Hadoop. Even though Databricks SQL can analyze Hadoop data, it does not offer the opportunity to save data there directly.

Option C is incorrect. It implies that Hadoop has more sophisticated security measures than Databricks SQL, which is also not accurate. Although Hadoop offers a robust security paradigm that is widely used and trusted in commercial situations, Databricks SQL does include security features.

Option D is incorrect. It also claims that Databricks SQL accommodates unstructured data better than Hadoop. Hadoop is built to handle both structured and unstructured data, making it a more versatile tool for data analysis even if Databricks SQL does have significant features for working with unstructured data.

Reference:

https://databricks.com/product/databricks-sql

Domain: Databricks SQL

Question 2. A manufacturing company wants to use data from sensors installed on the machinery to continually monitor the performance of its production line. Which of the following Databricks SQL features would be most beneficial in this situation?

  1. Databricks SQL can be used to ingest streaming data in real-time
  2. Databricks SQL can be used to design and create visualizations using BI tools
  3. Databricks SQL can be used to query data across multiple data sources
  4. Databricks SQL can be used to handle unstructured data

Correct Answer: A

Explanation:

The capacity of Databricks SQL to ingest streaming data in real-time is its most helpful feature for tracking the performance of a production line in real-time. This function enables the manufacturing business to process and analyze data as soon as it is generated by the sensors on the equipment, enabling them to rapidly discover any problems or anomalies in the production process and take corrective action.

Option A is correct. It appropriately represents the most practical aspect of Databricks SQL for tracking a manufacturing line’s performance in real-time.

Option B is incorrect. It states that Databricks SQL may be used to design and produce visualizations using BI tools, yet this is not the feature that is most helpful for real-time production line performance monitoring. Visualizations are useful for data interpretation, but they are not required for real-time production line performance monitoring.

Option C is incorrect. It states that Databricks SQL may be used to query data from several sources, which is also not the feature that is most helpful for this task. Real-time monitoring of production line performance frequently necessitates quick processing of streaming data from sensors on the machinery, even though it can be useful to query data across numerous sources.

Option D is incorrect. It states that unstructured data can be handled by Databricks SQL, but this is not the feature that is most beneficial for real-time production line performance monitoring. The data produced by sensors on the equipment is often structured data, and Databricks SQL can handle both types of data fairly well.

Reference: 

https://databricks.com/product/databricks-sql

Domain: Databricks SQL

Question 3. A data analyst has been asked to create a Databricks SQL query that will summarize sales data by product category and month. Which SQL function can you use to accomplish this?

  1. AVG
  2. SUM
  3. GROUP BY
  4. ORDER BY

Correct Answer: C

Explanation:

With the help of the powerful SQL function GROUP BY, one can group rows with identical values in a certain column or columns and get back a single row that contains a summary of the data for each group. To examine the total sales for each product category broken down by month, we want to aggregate the sales data in this case by both product category and month.

Option A is incorrect. When combined with GROUP BY, the aggregation function AVG can be used to get the average of a specific column inside each group. The amount of information needed to enumerate the sales data by product category and month is not provided, though.

Option B is incorrect. The SUM function is an aggregation function that can be used with GROUP BY to calculate the sum of a particular column within each group. However, it does not provide the level of detail required to summarize the sales data by both product category and month.

Option C is correct. The correct SQL function to use in Databricks SQL to summarize sales data by product category and month is GROUP BY. The function returns a single row with a summary of the data for each group and organizes the data according to the supplied columns.

Option D is incorrect. The ORDER BY function is used to employ one or more columns to sort the results of a query in either ascending or descending order. The data is not grouped in the manner necessary to generate a summary of sales by product category and month, although sorting the summarized sales data by product category or month may be relevant.

Reference:

https://docs.databricks.com/sql/language-manual/sql-ref-syntax-qry-select-groupby.html

Domain: Databricks SQL

Question 4. A data analyst of a large online retailer wants to integrate Databricks SQL with Partner Connect to obtain real-time data on customer behavior from a social media platform. Which of the following steps would the data analyst take to achieve the desired outcome? 

  1. Use Databricks SQL to ingest the data from the social media platform and then connect it to Partner Connect.
  2. Use Partner Connect to ingest the data from the social media platform and then connect it to Databricks SQL.
  3. Use an ETL tool to ingest the data from the social media platform and then connect it to both Partner Connect and Databricks SQL.
  4. Use an API to ingest the data from the social media platform and then connect it to both Partner Connect and Databricks SQL.

Correct Answer: B

Explanation:

The correct step to integrate Databricks SQL with Partner Connect to obtain real-time data on customer behavior from a social media platform is to use Partner Connect to ingest the data from the social media platform and then connect it to Databricks SQL.

Without the use of intricate ETL procedures or APIs, Databricks’ Partner Connect cloud-based data integration service enables users to quickly and simply connect with a variety of data sources, including social media platforms. The data analyst can easily connect to the social media platform using Partner Connect and import real-time customer behavior data into Databricks SQL. After the data has been entered into Databricks SQL, it can be examined and used to better understand consumer behavior and develop marketing plans.

Option A is incorrect. Using Databricks SQL to ingest data from the social media platform would not allow for real-time data ingestion or integration with Partner Connect.

Option B is correct. Partner Connect should be used to ingest the data from the social media platform and then connect it to Databricks SQL.

Option C is incorrect. Using an ETL tool to ingest data from the social media platform would add complexity to the process and may not allow for real-time data ingestion or integration with Partner Connect.

Option D is incorrect. While using an API to ingest data from the social media platform is a possibility, it may not allow for real-time data ingestion or integration with Partner Connect.

Reference:

https://docs.databricks.com/partner-connect/index.html

Domain: Databricks SQL

Question 5. A Data analyst has been tasked with optimizing a Databricks SQL query for a large dataset. What should you consider when trying to improve query performance?

  1. Increasing the size of the cluster to handle the data
  2. Partitioning the data into smaller chunks
  3. Using a higher level of parallelism for the query
  4. Increasing the timeout for the query

Correct Answer: B

Explanation:

To enhance query performance, it may be useful to partition the data into smaller chunks. Due to its ability to process data in parallel, less data must be processed during a single operation. Processing time can be drastically decreased using this method, especially for large datasets. The best option for enhancing query performance is therefore Option B. Therefore, it is crucial to think about the best way to enhance query performance while reducing costs and maximizing resources when optimizing a Databricks SQL query for a large dataset.

Option A is incorrect. Increasing the size of the cluster can help with performance, but it may not be necessary or cost-effective for smaller datasets.

Option B is correct. Data partitioning into more manageable portions can greatly enhance query performance. Through the use of parallel processing, less data must be processed during a single operation, effectively cutting down on processing time.

Option C is incorrect. Even though adding more parallelism can improve query performance, it is not always the best choice and can result in higher costs.

Option D is incorrect. With queries that take longer to process, increasing the timeout can be helpful, but it won’t always result in better performance.

Reference:

https://docs.databricks.com/tables/partitions.html

Domain: Databricks SQL

Question 6. Which layer of the Medallion Architecture is responsible for providing a unified view of data from various sources?

  1. Bronze layer
  2. Silver layer
  3. Gold layer
  4. None of the above

Correct Answer: C

Explanation: 

In the data pipeline, each layer is responsible for a particular task. The ingestion, transformation, and storage of data in its unprocessed form fall under the purview of the Bronze layer. By carrying out data profiling, cleaning, and modeling, the Silver layer concentrates on further preparing and processing the data.

The Gold layer offers a single source of truth for the data and makes it available to end users via a variety of BI tools. Option C, the Gold layer, is the appropriate response to the question.

Option A is incorrect. The Bronze layer is responsible for data ingestion and processing. 

Option B is incorrect. The Silver layer is responsible for querying and transforming the data. 

Option C is correct. A unified view of data from various sources is provided by the Gold layer of the Medallion Architecture. This layer combines data from various sources, carries out advanced analytics, and gives users a unified view of the data. Building a data warehouse, dashboards, and reports for business users are all part of this layer.

Option D is incorrect. Since the gold layer in option C is the correct answer which is responsible for providing a unified view of data from various sources, this option cannot be true.

Reference:

https://www.databricks.com/glossary/medallion-architecture

Domain: Data Management

Question 7. A data analyst has created a Delta Lake table in Databricks and wants to optimize the performance of queries that filter on a specific column. Which Delta Lake feature should the data analyst use to improve query performance? 

  1. Indexing
  2. Partitioning
  3. Caching
  4. Z-Ordering

Correct Answer: D

Explanation:

Z-Ordering is a technique that reorders the data in a Delta Lake table based on the values of one or more columns. This is done in such a way that data with similar values in the specified column(s) are stored physically close to each other. As a result, when a query filters on the specified column(s), only the relevant data needs to be read from the disk, leading to significant improvements in query performance.

For example, consider a Delta Lake table that contains sales data for a retail company, with columns for date, store, product, and quantity sold. If most queries filter on the date column, Z-Ordering the data by date would ensure that all data for a specific date range is stored together, allowing for faster queries.

Option A is incorrect. Indexing is a feature that creates an index on a column or set of columns in a table. While indexing can improve query performance, it is not the best option for optimizing queries that filter on a specific column in a Delta Lake table.

Option B is incorrect. Partitioning is a technique where data is physically partitioned based on the values of one or more columns. This helps with query performance, but it is not as efficient as Z-Ordering when filtering on a specific column.

Option C is incorrect. Caching is a technique where data is stored in memory to improve query performance. While caching can help with query performance, it is not the best option for optimizing queries that filter on a specific column in a Delta Lake table.

Option D is correct. To optimize the performance of queries that filter on a specific column in a Delta Lake table, the data analyst should use the Z-Ordering feature.

Reference:

https://yousry.medium.com/delta-lake-z-ordering-from-a-to-z-315063a42031

Domain: Data Management

Question 8. What features does Data Explorer in Databricks offer to simplify the management of data, and how do they improve the data management process?

  1. Data Explorer provides a visual interface for creating and managing tables, making it easier to navigate and organize data.
  2. Data Explorer allows users to create and edit SQL queries directly within the interface, reducing the need to switch between different tools.
  3. Data Explorer offers data profiling and visualization tools that can help users better understand the structure and content of their data.
  4. All of the above.

Correct Answer: D

Explanation:

Option A is incorrect. This option is partially correct as it only mentions the visual interface for creating and managing tables. It does not mention the other features, such as the built-in SQL editor and data profiling and visualization tools, which also simplify data management in Data Explorer.

Option B is incorrect. This option is also partially correct as it only mentions the built-in SQL editor. It does not mention the visual interface for creating and managing tables or the data profiling and visualization tools, which are also important features of Data Explorer.

Option C is incorrect. This option is also partially correct as it only mentions the data profiling and visualization tools. It does not mention the visual interface for creating and managing tables or the built-in SQL editor, which are also important features of Data Explorer.

Option D is correct. Since Options A, B, and C all are correct features of Data Explorer, All of the above is correct.

Reference:

https://docs.databricks.com/data/index.html#discover-and-manage-data-using-data-explorer

Domain: Data Management

Question 9. A data analyst has created a view in Databricks that references multiple tables in different databases. The data analyst wants to ensure that the view is always up to date with the latest data in the underlying tables. Which of the following Databricks feature should the data analyst use to achieve this?

  1. Materialized views
  2. Delta caches
  3. Databricks Delta streams
  4. Databricks SQL Analytics

Correct Answer: C

Explanation:

Delta streams provide a mechanism for doing this in real-time, by continuously updating the data as it arrives. This means that data analysts can analyze data as it is generated, without worrying about data inconsistencies or delays in processing. Delta streams also provide low-latency access to data changes, enabling data analysts to analyze data as it arrives in real-time. This ensures that downstream analytics and views are always up to date with the latest data, and enables data analysts to quickly and easily identify and respond to changes in the data.

Option A is incorrect. Materialized views are precomputed views that store the results of a query and are used to speed up the performance of repeated queries. However, they do not ensure that the view is always up to date with the latest data in the underlying tables.

Option B is incorrect. Delta caches are an in-memory caching mechanism that stores frequently accessed data to improve query performance. However, they do not ensure that the view is always up to date with the latest data in the underlying tables.

Option C is correct. This is because Delta streams provide a continuous stream of updates to data as it arrives, ensuring that downstream analytics and views are always up to date with the latest data.

Option D is incorrect. Databricks SQL Analytics is a service in Databricks that provides a collaborative SQL workspace for data analysts. However, it does not provide a specific feature that ensures that the view is always up to date with the latest data in the underlying tables.

Reference:

https://docs.databricks.com/structured-streaming/delta-lake.html

Domain: Data Management

Question 10. A data analyst at a healthcare company is tasked with managing a Databricks table containing personally identifiable information (PII) data, including patients’ names and medical histories. The analyst wants to ensure that only authorized personnel can access the table. Which of the following Databricks tools can the analyst use to enforce table ownership and restrict access to the PII data?

  1. Delta Lake
  2. Access Control Lists
  3. Apache Spark
  4. Structured Streaming

Correct Answer: B

Explanation: 

The data analyst can use ACLs to restrict access to the PII data by enforcing table ownership. Access control lists allow the data analyst to set up permissions and manage access to the table based on the user’s identity, group membership, or IP address. ACLs provide a flexible way to enforce security policies at the table or object level, and the data analyst can use them to prevent unauthorized access to the PII data.

Option A is incorrect. Delta Lake is a powerful tool for managing large-scale data lakes. It provides features such as version control, data quality, and schema enforcement. However, it does not directly address the issue of restricting access to PII data. While Delta Lake can be used to enforce data quality and schema consistency, it does not provide a built-in way to enforce table ownership or restrict access to specific users or groups.

Option B is correct. Access Control Lists or ACLs enable the data analyst to define access control policies based on individual user accounts or groups, which helps ensure that only authorized personnel can access the table containing sensitive PII data. With ACLs, the data analyst can also define permissions to control actions like read, write, and execute, thereby limiting access to the table and keeping it secure.

Option C is incorrect. Apache Spark is a distributed computing engine used for processing large datasets. While Spark can be used to build data pipelines and perform advanced analytics on the PII data, it does not provide any built-in security features to restrict access to the data.

Option D is incorrect. Structured Streaming is a high-level API for building scalable real-time processing applications. It allows data analysts to process streaming data in near-real-time and perform transformations on the data. However, like Spark, it does not provide any built-in security features to restrict access to PII data.

Reference:

https://docs.databricks.com/security/auth-authz/access-control/index.html

Domain: Data Management

Question 11. A data analyst is working with a Delta Lake table which includes changing the data types of a column. Which SQL statement should the data analyst use to modify the column data type?

  1. ALTER TABLE table_name ADD COLUMN column_name datatype
  2. ALTER TABLE table_name DROP COLUMN column_name
  3. ALTER TABLE table_name ALTER COLUMN column_name datatype
  4. ALTER TABLE table_name RENAME COLUMN column_name TO new_column_name

Correct Answer: C

Explanation: The ALTER COLUMN clause is used to modify the data type of an existing column in a table. Option A is incorrect because it adds a new column to the table rather than modifying an existing column. Option B is incorrect because it drops a column from the table rather than modifying its data type. Option D is incorrect because it renames a column rather than modifying its data type.

Option A is incorrect. It is not the correct statement to use when changing the data type of a column in a Delta Lake table. ALTER TABLE table_name ADD COLUMN column_name datatype, is used to add a new column to an existing table. This statement does not modify the data type of an existing column.

Option B is incorrect. It is not the correct statement to use when changing the data type of a column in a Delta Lake table. ALTER TABLE table_name DROP COLUMN column_name, is used to delete a column from an existing table. This statement does not modify the data type of an existing column. 

Option C is correct. This statement modifies the data type of the specified column in the table. ALTER TABLE table_name ALTER COLUMN column_name datatype, is the correct statement to use when changing the data type of a column in a Delta Lake table. 

Option D is incorrect. It is not the correct statement to use when changing the data type of a column in a Delta Lake table. ALTER TABLE table_name RENAME COLUMN column_name TO new_column_name, is used to rename a column in an existing table. This statement does not modify the data type of an existing column.

Reference:

https://docs.databricks.com/sql/language-manual/sql-ref-syntax-ddl-alter-table.html

Domain: Data Management

Question 12. A data analyst has been given a requirement of creating a Delta Lake table in Databricks that can be efficiently queried using a specific column as the partitioning column. Which data format and partitioning strategy should the data analyst choose?

  1. Parquet file format and partition by hash
  2. Delta file format and partition by range
  3. ORC file format and partition by list
  4. CSV file format and partition by round-robin

Correct Answer: A

Explanation: 

The Parquet file format is a columnar storage format that provides efficient compression and encoding techniques, which makes it ideal for storing and querying large datasets. In addition, partitioning by hash provides an even distribution of data across partitions based on the hash value of the partitioning column. This method ensures that data is distributed evenly across all partitions, which in turn helps to reduce the query time and improve the overall performance of the table with efficient filtering and aggregation.

Option A is correct. This option suggests using the Parquet file format and partitioning by hash for creating a Delta Lake table in Databricks that can be efficiently queried using a specific column as the partitioning column. Parquet is a columnar file format that provides efficient compression and encoding of data. Partitioning by hash distributes the data evenly across partitions based on the values in the partitioning column.

Option B is incorrect. This option suggests using the Delta file format and partitioning by range. Although the Delta file format provides additional functionality such as ACID compliance and transaction management, partitioning by range will not be the best option for this scenario as it requires defining ranges based on the partitioning column, which can lead to uneven data distribution and may impact query performance.

Option C is incorrect. This option suggests using the ORC file format and partitioning by list. Although the ORC file format provides efficient compression and encoding techniques, partitioning by list requires defining specific values for the partitioning column, which can lead to uneven data distribution and may impact query performance.

Option D is incorrect. This option suggests using the CSV file format and partitioning by round-robin. Although partitioning by round-robin can provide an even distribution of data across partitions, using the CSV file format can be inefficient for querying large datasets as it requires scanning the entire file to extract the required information.

Reference:

https://bigdataprogrammers.com/delta-vs-parquet-in-databricks/

Domain: SQL

Question 13. A data analyst needs to find out the top 5 customers based on the total amount they spent on purchases in the last 30 days from the sales table. Which of the following Databricks SQL statements will yield the correct result?

  1. SELECT TOP 5 customer_id, SUM(price) as total_spent FROM sales WHERE date >= DATEADD(day, -30, GETDATE()) GROUP BY customer_id ORDER BY total_spent DESC;
  2. SELECT  customer_id, SUM(price) as total_spent FROM sales WHERE date >= DATEADD(day, -30, GETDATE()) GROUP BY customer_id ORDER BY total_spent DESC LIMIT 5;
  3. SELECT customer_id, SUM(price) as total_spent FROM sales WHERE date >= DATEADD(day, -30, GETDATE()) GROUP BY customer_id HAVING total_spent > 0 ORDER BY total_spent DESC LIMIT 5;
  4. SELECT customer_id, SUM(price) as total_spent FROM sales WHERE date BETWEEN DATEADD(day, -30, GETDATE()) AND GETDATE() GROUP BY customer_id ORDER BY total_spent DESC LIMIT 5;

Correct Answer: B

Explanation:

The SQL statement in option B is the correct one. The top 5 customers are chosen based on the sum of their purchases over the previous 30 days using the LIMIT keyword. Results are filtered based on the date using the WHERE clause, and results are grouped based on customer ID using the GROUP BY clause. The ORDER BY clause is used to sort the results by the amount spent in total in descending order after the SELECT statement has selected the customer ID and the total amount spent on purchases. This statement, therefore, produces the desired outcome.

Option A is incorrect. It makes use of the TOP keyword, which Databricks SQL cannot handle. Instead, Databricks SQL limits the number of rows returned by a query using the LIMIT keyword. Additionally, Option A’s WHERE clause makes use of the GETDATE() function, which not only returns the date but also the time of day. The DATEADD method should be used to deduct 30 days from the current date to properly filter for the previous 30 days.

Option B is correct. The LIMIT keyword is used to restrict the results to the top 5 customers according to their cumulative spending over the previous 30 days. Using the DATEADD function, the WHERE clause appropriately filters for purchases done within the previous 30 days, and the GROUP BY clause groups the sales data by customer id. The ORDER BY clause ensures that the most spending customers appear first in the results by sorting the results in descending order by total spent.

Option C is incorrect. For this query, the HAVING clause is not required. A GROUP BY query’s output can be filtered using the aggregate functions SUM, AVG, or COUNT using the HAVING clause. There is no need for a second filter in the HAVING clause, as the WHERE clause in this query already limits the results to those made during the last 30 days.

Option D is incorrect. The BETWEEN operator, which includes the end points, is used in the WHERE clause. This indicates that contrary to the question’s specification, the query will return results for purchases made up to and including the present date. Using the DATEADD function in the WHERE clause, the right date range should be from 30 days ago to the present time.

Reference:

https://docs.databricks.com/sql/language-manual/functions/dateadd.html

Domain: SQL 

Question 14. A large retail company has a Lakehouse that stores data on purchase table made by their stores. The data analyst needs to find the total revenue generated by each store for January. Which of the following SQL statements will return the correct results?

Option.

  1. SELECT store_id, SUM(total_sales) as revenue FROM purchase WHERE date >= ‘2023-01-01’ AND date <= ‘2023-01-31’ GROUP BY store_id ORDER BY revenue DESC;
  2. SELECT store_id, SUM(total_sales) as revenue FROM purchase WHERE date BETWEEN ‘2023-01-01’ AND ‘2023-01-31’ GROUP BY store_id ORDER BY revenue DESC LIMIT 5;
  3. SELECT store_id, SUM(total_sales) as revenue FROM purchase WHERE date >= ‘2023-01-01’ AND date <= ‘2023-01-31’ GROUP BY store_id HAVING revenue > 0 ORDER BY revenue DESC;
  4. SELECT store_id, SUM(total_sales) as revenue FROM purchase WHERE date >= ‘2023-01-01’ AND date <= ‘2023-01-31’ GROUP BY store_id HAVING revenue > 0 ORDER BY revenue ASC;

Correct Answer: A

Explanation:

The provided SQL is option A since it employs the proper syntax to filter the data according to the date range and organizes the outcomes by store id to get the total income for each store. The query computes the total amount spent by each store using the “SUM” function and groups the results by store id using the “GROUP BY” clause. The “ORDER BY” clause is then used to organize the results by revenue in descending order.

With this strategy, the query will always return the total sales revenue for each shop throughout January. The other choices either use SQL clauses improperly or have syntax issues. As a result, Option A is the best decision in this case.

Option A is correct. Using the WHERE clause to filter the dates, it chooses the store ID and the total amount spent at each retailer during January. The data is then grouped by store ID using the GROUP BY clause, and the results are sorted using the ORDER BY clause in descending order. With the store that created the greatest money at the top, this will show the total revenue made by each store in descending order.

Option B is incorrect. To filter the dates, it substitutes the BETWEEN operator for the >= and = operators in the WHERE clause. Although the operators >= and = are also acceptable in SQL, >= and = are more frequently employed.

Option C is incorrect. In this situation, the HAVING condition is not necessary. Following the GROUP BY clause, the HAVING clause is used to filter results based on aggregate operations like SUM or COUNT. The HAVING clause is not required in this situation because there are no prerequisites for the aggregate function.

Option D is incorrect. Using the ORDER BY clause, it arranges the outcomes in ascending order. The answers should be arranged in descending order since the question asks for the stores with the largest revenue.

Reference:

https://docs.databricks.com/sql/language-manual/sql-ref-syntax-qry-select-orderby.html

Domain: SQL

Question 15. A healthcare organization has a Lakehouse that stores data on patient appointments. The data analyst needs to find the average duration of appointments for each doctor. Which of the following SQL statements will return the correct results?

  1. SELECT doctor_id, AVG(duration) as avg_duration FROM appointments GROUP BY doctor_id;
  2. SELECT doctor_id, AVG(duration) as avg_duration FROM appointments GROUP BY doctor_id HAVING avg_duration > 0;
  3. SELECT doctor_id, SUM(duration)/COUNT() as avg_duration FROM appointments GROUP BY doctor_id;
  4. SELECT doctor_id, duration/COUNT() as avg_duration FROM appointments GROUP BY doctor_id;

Correct Answer: A

Explanation:

The SQL in option A is accurate since it calculates the average appointment length for each doctor using the right syntax. The doctor id is chosen in the SELECT statement, and the AVG() method is used to get the average appointment length. The appointments are grouped by doctor id using the GROUP BY clause so that each doctor’s computation may be done individually.

Option A is correct. The average appointment length for each doctor is determined using the AVG() function, and the results are grouped by doctor.

Option B is incorrect. The average can be determined without the HAVING clause. In this scenario, we want to include all doctors and their average lengths of practice. The HAVING clause is used to filter results depending on criteria.

Option C is incorrect. The average duration is calculated incorrectly by dividing the total number of appointments by the sum of their durations. Not for all appointments, but for each doctor, we need to figure out the typical length of time.

Option D is incorrect. It is dividing the length by the total number of appointments, which, once more, will not get the right answer. Not for all appointments, but for each doctor, we need to figure out the typical length of time.

Reference:

https://docs.databricks.com/sql/language-manual/functions/avg.html

Domain: Databricks SQL

Question 16. A senior data analyst for a retail company that wants to create a dashboard to track sales performance. He is deciding whether the company should invest in Databricks SQL to aid with the requirement. Which of the following features of Databricks SQL would be most helpful to take the decision?

Option: 

  1. The ability to query data across multiple data sources
  2. The ability to ingest streaming data in real-time
  3. The ability to create visualizations using BI tools such as Tableau and Power BI
  4. The ability to analyze unstructured data such as customer reviews

Correct Answer: C

Explanation:

Developing a dashboard to monitor sales performance is an essential task for a senior data analyst. He is deciding whether the business should purchase Databricks SQL to help with the process.

Option A is incorrect. The ability to query data across multiple data sources is a useful feature, but it does not directly contribute to the creation of the dashboard. Moreover, this feature is not unique to Databricks SQL and can be found in many other database management systems.

Option B is incorrect. The ability to ingest streaming data in real-time is an essential feature for real-time data analysis but may not be necessary for creating a sales performance dashboard, which is often based on historical data. Moreover, this feature is not unique to Databricks SQL and can be found in many other real-time data processing systems.

Option C is correct. The ability to create visualizations using BI tools such as Tableau and Power BI is the most relevant feature for creating a sales performance dashboard. With this feature, the senior data analyst can create interactive dashboards and reports that provide insights into sales performance, identify trends, and make informed decisions. Moreover, Databricks SQL provides seamless integration with BI tools, making the process of creating dashboards and reports much more accessible and efficient.

Option D is incorrect. The ability to analyze unstructured data such as customer reviews is a valuable feature, but it may not be necessary for creating a sales performance dashboard. Moreover, this feature is not unique to Databricks SQL and can be found in many other text analysis tools.

Reference:

https://www.databricks.com/product/databricks-sql

Domain: Databricks SQL

Question 17. A data analyst of a large online retailer wants to integrate Databricks SQL with Partner Connect to obtain real-time data on customer behavior from a social media platform. Which of the following steps would the data analyst take to achieve the desired outcome?

Option:

  1. Use Databricks SQL to ingest the data from the social media platform and then connect it to Partner Connect.
  2. Use Partner Connect to ingest the data from the social media platform and then connect it to Databricks SQL.
  3. Use an ETL tool to ingest the data from the social media platform and then connect it to both Partner Connect and Databricks SQL.
  4. Use an API to ingest the data from the social media platform and then connect it to both Partner Connect and Databricks SQL.

Correct Answer: B

The data analyst of a large online retailer wants to integrate Databricks SQL with Partner Connect to obtain real-time data on customer behavior from a social media platform. Out of the given options, the correct step the data analyst would take to achieve this desired outcome is to use Partner Connect to ingest the data from the social media platform and then connect it to Databricks SQL.

Users of Databricks can quickly and easily connect to and ingest data from a variety of data sources, including cloud services, data platforms, and data providers, using the feature known as Partner Connect. Users can set up and customize data connections with Partner Connect to well-known data sources like Amazon S3, Microsoft Azure, Google Cloud Platform, and Snowflake, among others. Users can easily access the data they need for analysis and processing thanks to Partner Connect’s streamlined method of ingesting data into Databricks. Users can save time and effort by forgoing the need for intricate data pipelines and personalized integrations. Additionally, Partner Connect offers real-time data ingestion capabilities that let users ingest and analyze data almost instantly.

Option A is incorrect. It is not practical to ingest data from the social media platform using Databricks SQL and then connect it to Partner Connect. Databricks SQL is not a data ingestion tool; rather, it is a tool for data processing and analysis. Instead, data from various sources is ingested and connected to Databricks SQL using Partner Connect.

Option B is correct. The best method is to connect Databricks SQL to Partner Connect to ingest data from the social media platform. Users can connect to various data sources and ingest data into Databricks SQL in real-time using the real-time data ingestion tool called Partner Connect. The data analyst can analyze real-time customer behavior data from a social media platform by ingesting it into Databricks SQL using Partner Connect.

Option C is incorrect. It is not the best course of action, in this case, to connect Partner Connect and Databricks SQL using an ETL tool to ingest data from the social media platform. Partner Connect is a more effective tool for real-time data ingestion than ETL tools, which can extract, transform, and load data from a variety of sources.

Option D is incorrect. It is not the best course of action, in this case, to use an API to ingest data from the social media platform and then connect it to Partner Connect and Databricks SQL. Although there are many different sources from which data can be extracted using APIs, Partner Connect offers a more streamlined method for real-time data ingestion.

Reference:

https://www.databricks.com/partnerconnect

Domain: Databricks SQL

Question 18. A data analyst is working on a project to analyze a large dataset using Databricks SQL. The dataset is too large to fit in memory, so the analyst needs to use a distributed computing approach. Which Databricks SQL feature will best suit their needs?

Option:

  1. Dashboards
  2. Medallion architecture
  3. Compute
  4. Streaming data

Correct Answer: C

Explanation:

Users can perform distributed computing on sizable datasets that won’t fit in memory using Databricks SQL’s compute feature. The data analyst can efficiently process the large dataset and perform Databricks SQL analysis using distributed computing. A scalable distributed computing environment that can handle big, complex data sets is provided by Compute.

Option A is incorrect. Dashboards are graphic representations of data that offer insights into key performance indicators and metrics. Databricks SQL dashboards are useful for data exploration and analysis, but they are not intended for distributed computing. Dashboards should not be used to process data; rather, they should be used to analyze data after it has been processed.

Option B is incorrect. The Databricks Medallion architecture feature enables users to distribute SQL queries across various data sources. Data lakes, data warehouses, databases, and other sources are all integrated using this technique. Due to its emphasis on data integration and querying rather than computation, it is not the best option for distributed computing.

Option C is correct. Compute is the correct answer as it refers to the distributed computing resources available in Databricks. Compute provides a scalable distributed computing environment that can handle large and complex data sets. It allows users to allocate and scale computing resources dynamically to meet their data processing needs. Compute is a powerful feature in Databricks that allows users to run SQL queries on large datasets that are too large to fit into memory.

Option D is incorrect. Real-time data processing and analysis are made possible by Databricks’ streaming data feature. It is not intended for distributed computing, despite being a useful feature for real-time data processing. While streaming data is perfect for processing data as it is generated, it is not the best solution for large dataset analysis that requires distributed processing.

Reference:

https://www.databricks.com/product/databricks-sql

Domain: Databricks SQL

Question 19. Which of the following statements about the silver layer in the medallion architecture is true?

Option:

  1. The silver layer is where data is transformed and processed for analytics use
  2. The silver layer is where raw data is stored in its original format
  3. The silver layer is optimized for fast querying
  4. The silver layer is the largest of the three layers

Correct Answer: A

Explanation:

A framework for managing and analyzing massive amounts of data has been developed by Databricks called the medallion architecture. The bronze layer, the silver layer, and the gold layer are the three layers that make up architecture.

In the medallion architecture, the silver layer serves as an intermediary layer between the bronze layer, which stores raw data, and the gold layer, which houses data analysis. The silver layer’s function is to aggregate, filter, and transform the raw data so that analytics can be performed on it. Additionally, the silver layer may be used to organize, normalize, and clean up data. Data is sent to the gold layer for analysis after it has been transformed into the silver layer. Users can run queries, produce reports, and visualize data in the gold layer because it is optimized for fast querying and analysis.

Option A is correct. In the medallion architecture, data transformation and processing for analytics purposes take place in the silver layer. The silver layer is located between the bronze layer, which is used to store raw data, and the gold layer, which is used to analyze data. The silver layer’s function is to aggregate, filter, and transform the raw data so that analytics can be performed on it. Additionally, the silver layer may be used to organize, normalize, and clean up data. Data is sent to the gold layer for analysis after it has been transformed into the silver layer.

Option B is incorrect. The bronze layer, not the silver layer, is where raw data is kept. Large amounts of unprocessed data must be ingested and stored in their original format by the bronze layer. After being transformed and processed in the silver layer, the raw data is then examined in the gold layer.

Option C is incorrect. For fast querying, the silver layer is not optimized. The silver layer can be used for data aggregation and filtering, but it is not intended for fast querying. The gold layer is designed for fast analysis and querying. The main objective of the silver layer is to transform and process the data to use it for analysis in the gold layer.

Option D is incorrect. Of the three layers, the silver layer is not the largest. Due to its role in storing raw data, the bronze layer is typically the largest in the medallion architecture. Since it processes and transforms data rather than storing it, the silver layer is typically thinner than the bronze layer.

Reference:

https://www.databricks.com/glossary/medallion-architecture

Domain: Databricks SQL

Question 20. A data analyst in a healthcare company has recently started using Databricks SQL. Her team is struggling to optimize query performance on large datasets. What can the data analyst do to improve query performance in Databricks SQL?

Option:

  1. Use caching to store frequently used data in memory and reduce query execution time
  2. Use distributed query processing to parallelize query execution across multiple nodes
  3. Optimize table partitions and indexes to improve query performance
  4. All of the above

Correct Answer: D

Explanation:

As a data analyst working with Databricks SQL, there are various options to improve query performance. The correct answer is option D, which includes using caching, distributed query processing, and optimizing table partitions and indexes to enhance query execution time.

Option A is incorrect. It suggests using caching to store frequently used data in memory, which can help reduce query execution time. This is a valuable technique that can speed up query performance by minimizing disk I/O operations. However, it is important to note that caching should be used wisely and only for frequently accessed data, as storing too much data in memory can lead to memory pressure and affect query performance negatively. Since other options are also correct, choosing only this is not a correct approach.

Option B is incorrect. It suggests using distributed query processing to parallelize query execution across multiple nodes, which can speed up query execution on large datasets. This is because distributed processing can break down queries into smaller tasks and execute them simultaneously across multiple nodes. This approach is effective for improving query performance, especially when dealing with large datasets. Since other options are also correct, choosing only this is not a correct approach.

Option C is incorrect. It suggests optimizing table partitions and indexes to improve query performance. Partitioning data into smaller, manageable chunks can help to reduce the amount of data scanned during query execution, leading to faster query execution time. Additionally, creating appropriate indexes on frequently queried columns can help to speed up query execution by reducing the amount of data that needs to be scanned.

Option D is correct. Since all the above options are required to successfully optimize query performance in Databricks SQL.

Reference:

https://docs.databricks.com/optimizations/index.html

Domain: Databricks SQL

Question 21. Which of the following statements accurately describes the role of Delta Lake in the architecture of Databricks SQL?

Option:

A: Delta Lake provides data ingestion capabilities for Databricks SQL.

B: Delta Lake is a data storage layer that provides high-performance querying capabilities for Databricks SQL.

C: Delta Lake is a transactional storage layer that provides ACID compliance for data processing in Databricks SQL.

D: Delta Lake provides integration capabilities for Databricks SQL with other BI tools and platforms.

Correct Answer: C

Explanation:

Delta Lake is a powerful storage layer that is designed to provide ACID compliance and reliability for data processing in Databricks SQL. It achieves this by adding a transactional layer on top of cloud object storage, which ensures that data is always consistent and reliable. This allows users to ingest and query large volumes of data in real-time without having to worry about data consistency or reliability issues.

One of the primary benefits of Delta Lake is its ability to provide transactional storage and ACID compliance for data processing. This means that users can rely on the data stored in Delta Lake to be consistent and accurate, even in the face of complex data pipelines and processing scenarios. Additionally, Delta Lake provides high-performance querying capabilities, making it easy to access and analyze data in real-time.

Option A is incorrect. While Delta Lake does allow for data ingestion, this is not its primary role in the Databricks SQL architecture. Rather, Delta Lake’s primary role is to provide transactional storage and ACID compliance.

Option B is incorrect. While Delta Lake does provide high-performance querying capabilities, this is not its only role. In addition to querying, Delta Lake also provides transactional storage and ACID compliance.

Option C is correct. Delta Lake plays a crucial role in the architecture of Databricks SQL by providing transactional storage and ACID compliance for data processing. While it does allow for data ingestion and provides high-performance querying capabilities, these are not its primary roles.

Option D is incorrect. While Delta Lake can be integrated with other tools and platforms, this is not its primary role in the Databricks SQL architecture. Rather, its primary role is to provide transactional storage and ACID compliance.

Reference:

https://docs.databricks.com/lakehouse/acid.html

Domain: Data Management

Question 22. Delta Lake provides many benefits over traditional data lakes. In which of the following scenarios would Delta Lake not be the best choice?

Option:

  1. When data is mostly unstructured and does not require any schema enforcement
  2. When data is primarily accessed through batch processing
  3. When data is stored in a single file and does not require partitioning
  4. When data requires frequent updates and rollbacks

Correct Answer: B

Explanation:

A unified data management system called Delta Lake offers capabilities for ACID transactions, schema enforcement, and schema evolution on top of a data lake. In comparison to conventional data lakes, Delta Lake has several advantages, including reliability, performance, and scalability. Delta Lake, though, might not always be the best option for batch processing.

Option A is incorrect. It implies that Delta Lake might not be required if the majority of the data is unstructured and no schema enforcement is necessary. However, by inferring a schema from the data’s content, Delta Lake can handle unstructured data and even transform it into structured data. As a result, this choice is incorrect. 

Option B is correct. Even though it supports batch processing, Delta Lake is designed to handle streaming data and speed up query execution. Therefore, traditional data lakes may be a better option in scenarios where data is accessed primarily through batch processing.

Option C is incorrect. It implies that when data is stored in a single file and does not require partitioning, Delta Lake might not be required. However, Delta Lake’s key feature of partitioning offers performance and scalability for handling large amounts of data. This choice is also incorrect because storing data in a single file may restrict scalability and performance.

Option D is incorrect. It describes the support for transactional updates and data versioning, which is one of Delta Lake’s main advantages. Data consistency and dependability are ensured by transactional updates and rollbacks in Delta Lake. As a result, this choice is also incorrect.

Reference:

https://techcommunity.microsoft.com/t5/azure-synapse-analytics-blog/synapse-data-lake-vs-delta-lake-vs-data-lakehouse/ba-p/3673653

Domain: Data Management

Question 23. Delta Lake supports schema evolution, which allows for changes to the schema of a table without requiring a full rewrite of the table. Which of the following is not a supported schema evolution operation?

Option:

  1. Adding a new column
  2. Removing a column
  3. Renaming a column
  4. Changing the data type of a column

Correct Answer: B

Explanation: 

For big data processing, the storage layer known as Delta Lake offers ACID transactions and schema evolution. Schema evolution refers to the capacity to modify a table’s schema without necessitating a complete rewrite of the table. This can be helpful if the table’s schema needs to be updated to include new data sources or fix errors in the current schema. It is significant to note that only backward-compatible schema evolution is supported by Delta Lake. This means that data that was written using the old schema must be readable by the new schema. Delta Lake does not support breaking schema changes, such as changing the order of columns or changing the data type of a column in a way that makes it impossible to read the existing data.

Option A is incorrect. An existing table’s schema can be expanded with new columns without affecting the data already present. When new data sources are added to the table and extra columns are needed to be stored, this is helpful. This is a characteristic of schema evolution, so the suggested option is incorrect.

Option B is correct. The deletion of a column from a table that already exists is not supported by Delta Lake. For large datasets, removing a column would necessitate a complete rewrite of the table, which would be expensive and time-consuming. The suggested course of action is to create a new table with the updated schema and copy the pertinent data to it if a column needs to be removed.

Option C is incorrect. It is possible to rename columns in an existing table without having an impact on the data. When a column name conflicts with a reserved keyword or is not descriptive, this can be helpful. This is a characteristic of schema evolution, so the suggested option is incorrect.

Option D is incorrect. A column’s data type can be changed without affecting the data already present. A column with an integer data type, for instance, could be converted to a string data type. This is a characteristic of schema evolution, so the suggested option is incorrect.

Reference:

https://www.databricks.com/blog/2019/09/24/diving-into-delta-lake-schema-enforcement-evolution.html

Domain: Data Management

Question 24. A data analyst wants to create a view in Databricks that displays only the top 10% of customers based on their total spending. Which SQL query would achieve this goal?

Option: 

  1. SELECT * FROM customers ORDER BY total_spend DESC LIMIT 10%
  2. SELECT * FROM customers WHERE total_spend > PERCENTILE(total_spend, 90)
  3. SELECT * FROM customers WHERE total_spend > (SELECT PERCENTILE(total_spend, 90) FROM customers)
  4. SELECT * FROM customers ORDER BY total_spend DESC OFFSET 10%

Correct Answer: C

Explanation:

To guarantee that exactly 10% of customers are returned in the view, the query in option C uses the subquery to find the 90th percentile of total_spend and selects all customers whose total_spend is greater than this value. Finding the top 10% of clients based on total_spend is not possible using options A, B, or D.

Option A is incorrect. Based on total_spend, this query orders the customers’ table in descending order and only selects the top 10 rows. The number of rows returned will depend on the total number of customers in the table, so this does not provide the top 10% of customers.

Option B is incorrect. The 90th percentile of total_spend is determined using the PERCENTILE function, and all customers whose total_spend is higher than this value are then chosen. Although this may seem like a good strategy, it may not always result in the top 10% of customers. More than 10% of customers can have a total_spend that is higher than the 90th percentile value when there are customers with the same total_spend.

Option C is correct. The 90th percentile of total_spend is determined using a subquery, and all customers whose total_spend exceeds this value are then chosen. No matter how many customers are at the table, this makes sure that exactly 10% of them are returned in the view.

Option D is incorrect. This query skips the first 10 rows and sorts the customers’ table in descending order based on total_spend. The exact percentage of customers who will return to the view is not guaranteed by this strategy.

Reference:

https://docs.databricks.com/sql/language-manual/functions/percentile.html

Domain: Data Management

Question 25. A healthcare company stores patient information in a table in Databricks. The company needs to ensure that only authorized personnel can access the table. Which of the following actions would best address this security concern?

Option:

  1. Assigning table ownership to a generic company account
  2. Granting access to the table to all employees
  3. Implementing role-based access control with specific privileges assigned to individual users
  4. Storing the patient information in an unsecured Excel file

Correct Answer: C

Explanation:

Option A is incorrect. It would not be possible to limit access to only authorized personnel by giving a generic company account ownership of a table. The patient information table would be accessible to anyone with access to the generic account, which could result in unauthorized access and misuse of private information.

Option B is incorrect. All employees being given access to the table would make it impossible to guarantee that only those with authorization have access to the patient information. This would pose a serious security risk because it might result in data breaches and jeopardize patient privacy.

Option C is correct. The best option is to implement role-based access control with specific privileges assigned to individual users. This approach allows the creation of custom roles with specific privileges assigned to each role, which can then be assigned to individual users. This ensures that only authorized personnel have access to the patient information table and that they only have access to the specific data they need to perform their job duties. By implementing this security measure, the healthcare company can ensure that patient information is kept private and secure.

Option D is incorrect. Patient data would not be secure if it were kept in an unprotected Excel file, making it vulnerable to unauthorized access. Excel files are easily accessible to anyone with access to the file and are not intended to handle sensitive data. This might result in a data breach and jeopardize the patient’s privacy.

Reference:

https://docs.databricks.com/security/auth-authz/access-control/index.html

Conclusion

We hope that the collection of Databricks Certified Data Analyst Associate Certification practice questions provided above will prove invaluable to you. Taking this certification serves as an excellent entry point for individuals who are new to the field of data analysis using Databricks and are looking to kickstart their careers.

We encourage you to continue practicing until you feel fully prepared to tackle the Databricks Certified Data Analyst Associate actual exam.

For further assistance and enhanced preparation, consider exploring our updated practice tests, Video Course, Hands-on labs, and Sandbox designed specifically for the Databricks Certified Data Analyst Associate Certification exam.

About Karthikeyani Velusamy

Karthikeyani is an accomplished Technical Content Writer with 3 years of experience in the field where she holds Bachelor's degree in Electronics and Communication Engineering. She is well-versed in core skills such as creative writing, web publications, portfolio creation for articles. Committed to delivering quality work that meets deadlines, she is dedicated to achieving exemplary standards in all her writing projects. With her creative skills and technical understanding, she is able to create engaging and informative content that resonates with her audience.

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