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Databricks Databricks-Certified-Data-Engineer-Associate Sample Questions – Free Practice Test & Real Exam Prep
Question #1
Which of the following describes the type of workloads that are always compatible with Auto
Loader?
A. Dashboard workloads
B. Streaming workloads
C. Machine learning workloads
D. Serverless workloads
E. Batch workloads
Answer: B Explanation: Auto Loader is a Structured Streaming source that incrementally and efficiently processes new data files as they arrive in cloud storage. It supports both Python and SQL in Delta Live Tables, which are ideal for building streaming data pipelines. Auto Loader can handle near real-time ingestion of millions of files per hour and provide exactly-once guarantees when writing data into Delta Lake. Auto Loader is not designed for dashboard, machine learning, serverless, or batch workloads, which have different requirements and characteristics. Reference: What is Auto Loader?, Delta Live Tables
Question #2
Which of the following SQL keywords can be used to convert a table from a long format to a wide
format?
A. PIVOT
B. CONVERT
C. WHERE
D. TRANSFORM
E. SUM
Answer: A Explanation: The SQL keyword that can be used to convert a table from a long format to a wide format is PIVOT. The PIVOT clause is used to rotate the rows of a table into columns of a new table1. The PIVOT clause can aggregate the values of a column based on the distinct values of another column, and use those values as the column names of the new table1. The PIVOT clause can be useful for transforming data from a long format, where each row represents an observation with multiple attributes, to a wide format, where each row represents an observation with a single attribute and multiple values2. For example, the PIVOT clause can be used to convert a table that contains the sales of different products by different regions into a table that contains the sales of each product by each region as separate columns1. The other options are not suitable for converting a table from a long format to a wide format. CONVERT is a function that can be used to change the data type of an expression3. WHERE is a clause that can be used to filter the rows of a table based on a condition4. TRANSFORM is a keyword that can be used to apply a user-defined function to a group of rows in a table5. SUM is a function that can be used to calculate the total of a numeric column. Reference: 1: PIVOT | Databricks on AWS 2: Reshaping Data - Long vs Wide Format | Databricks on AWS 3: CONVERT | Databricks on AWS 4: WHERE | Databricks on AWS 5: TRANSFORM | Databricks on AWS : [SUM | Databricks on AWS]
Question #3
A data engineering team has noticed that their Databricks SQL queries are running too slowly when
they are submitted to a non-running SQL endpoint. The data engineering team wants this issue to be
resolved.
Which of the following approaches can the team use to reduce the time it takes to return results in
this scenario?
A. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to "Reliability Optimized."
B. They can turn on the Auto Stop feature for the SQL endpoint.
C. They can increase the cluster size of the SQL endpoint.
D. They can turn on the Serverless feature for the SQL endpoint.
E. They can increase the maximum bound of the SQL endpoint's scaling range
Answer: D Explanation: Option D is the correct answer because it enables the Serverless feature for the SQL endpoint, which allows the endpoint to automatically scale up and down based on the query load. This way, the endpoint can handle more concurrent queries and reduce the time it takes to return results. The Serverless feature also reduces the cold start time of the endpoint, which is the time it takes to start the cluster when a query is submitted to a non-running endpoint. The Serverless feature is available for both AWS and Azure Databricks platforms. Reference: Databricks SQL Serverless, Serverless SQL endpoints, New Performance Improvements in Databricks SQL
Question #4
A data engineer needs to use a Delta table as part of a data pipeline, but they do not know if they
have the appropriate permissions.
In which of the following locations can the data engineer review their permissions on the table?
A. Databricks Filesystem
B. Jobs
C. Dashboards
D. Repos
E. Data Explorer
Answer: E Explanation: Data Explorer is a graphical interface that allows you to browse, create, and manage data objects such as databases, tables, and views in your workspace. You can also review and modify the permissions on these data objects using Data Explorer. To access Data Explorer, you can click on the Data icon in the sidebar, or use the %sql magic command in a notebook. You can then select a database and a table, and click on the Permissions tab to view and edit the access control lists (ACLs) for the table. You can also use SQL commands such as SHOW GRANT and GRANT to query and modify the permissions on a Delta table. Reference: Data Explorer Access control for Delta tables SHOW GRANT [GRANT]
Question #5
A single Job runs two notebooks as two separate tasks. A data engineer has noticed that one of the
notebooks is running slowly in the Job's current run. The data engineer asks a tech lead for help in
identifying why this might be the case.
Which of the following approaches can the tech lead use to identify why the notebook is running
slowly as part of the Job?
A. They can navigate to the Runs tab in the Jobs UI to immediately review the processing notebook.
B. They can navigate to the Tasks tab in the Jobs UI and click on the active run to review the processing notebook.
C. They can navigate to the Runs tab in the Jobs UI and click on the active run to review the processing notebook.
D. There is no way to determine why a Job task is running slowly.
E. They can navigate to the Tasks tab in the Jobs UI to immediately review the processing notebook.
Answer: C Explanation:
Question #6
A data analysis team has noticed that their Databricks SQL queries are running too slowly when
connected to their always-on SQL endpoint. They claim that this issue is present when many
members of the team are running small queries simultaneously. They ask the data engineering team
for help. The data engineering team notices that each of the team's queries uses the same SQL
endpoint.
Which of the following approaches can the data engineering team use to improve the latency of the
team's queries?
A. They can increase the cluster size of the SQL endpoint.
B. They can increase the maximum bound of the SQL endpoint's scaling range.
C. They can turn on the Auto Stop feature for the SQL endpoint.
D. They can turn on the Serverless feature for the SQL endpoint.
E. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to œReliability Optimized.
An engineering manager wants to monitor the performance of a recent project using a Databricks
SQL query. For the first week following the project's release, the manager wants the query results to
be updated every minute. However, the manager is concerned that the compute resources used for
the query will be left running and cost the organization a lot of money beyond the first week of the
project's release.
Which of the following approaches can the engineering team use to ensure the query does not cost
the organization any money beyond the first week of the project's release?
A. They can set a limit to the number of DBUs that are consumed by the SQL Endpoint.
B. They can set the query's refresh schedule to end after a certain number of refreshes.
C. They cannot ensure the query does not cost the organization money beyond the first week of the project's release.
D. They can set a limit to the number of individuals that are able to manage the query's refresh schedule.
E. They can set the query's refresh schedule to end on a certain date in the query scheduler.
Answer: E Explanation: In Databricks SQL, you can use scheduled query executions to update your dashboards or enable routine alerts. By default, your queries do not have a schedule. To set the schedule, you can use the dropdown pickers to specify the frequency, period, starting time, and time zone. You can also choose to end the schedule on a certain date by selecting the End date checkbox and picking a date from the calendar. This way, you can ensure that the query does not run beyond the first week of the project's release and does not incur any additional cost. Option A is incorrect, as setting a limit to the number of DBUs does not stop the query from running. Option B is incorrect, as there is no option to end the schedule after a certain number of refreshes. Option C is incorrect, as there is a way to ensure the query does not cost the organization money beyond the first week of the project's release. Option D is incorrect, as setting a limit to the number of individuals who can manage the query's refresh schedule does not affect the query's execution or cost. Reference: Schedule a query, Schedule a query - Azure Databricks - Databricks SQL
Question #8
A data engineer only wants to execute the final block of a Python program if the Python variable
day_of_week is equal to 1 and the Python variable review_period is True.
Which of the following control flow statements should the data engineer use to begin this
conditionally executed code block?
A. if day_of_week = 1 and review_period:
B. if day_of_week = 1 and review_period = "True":
C. if day_of_week == 1 and review_period == "True": ]
D. if day_of_week == 1 and review_period:
E. if day_of_week = 1 & review_period: = "True":
Answer: D Explanation: In Python, the == operator is used to compare the values of two variables, while the = operator is used to assign a value to a variable. Therefore, option A and E are incorrect, as they use the = operator for comparison. Option B and C are also incorrect, as they compare the review_period variable to a string value "True", which is different from the boolean value True. Option D is the correct answer, as it uses the == operator to compare the day_of_week variable to the integer value 1, and the and operator to check if both conditions are true. If both conditions are true, then the final block of the Python program will be executed. Reference: [Python Operators], [Python If ¦ Else
Question #9
Which of the following benefits is provided by the array functions from Spark SQL?
A. An ability to work with data in a variety of types at once
B. An ability to work with data within certain partitions and windows
C. An ability to work with time-related data in specified intervals
D. An ability to work with complex, nested data ingested from JSON files
E. An ability to work with an array of tables for procedural automation
Answer: D Explanation: The array functions from Spark SQL are a subset of the collection functions that operate on array columns1. They provide an ability to work with complex, nested data ingested from JSON files or other sources2. For example, the explode function can be used to transform an array column into multiple rows, one for each element in the array3. The array_contains function can be used to check if a value is present in an array column4. The array_join function can be used to concatenate all elements of an array column with a delimiter. These functions can be useful for processing JSON data that may have nested arrays or objects. Reference: 1: Spark SQL, Built-in Functions - Apache Spark 2: Spark SQL Array Functions Complete List - Spark By Examples 3: Spark SQL Array Functions - Syntax and Examples - DWgeek.com 4: Spark SQL, Built-in Functions - Apache Spark : Spark SQL, Built-in Functions - Apache Spark : [Working with Nested Data Using Higher Order Functions in SQL on Databricks - The Databricks Blog]
Question #10
A data analyst has created a Delta table sales that is used by the entire data analysis team. They want
help from the data engineering team to implement a series of tests to ensure the data is clean.
However, the data engineering team uses Python for its tests rather than SQL. Which of the following commands could the data engineering team use to access sales in PySpark?
A. SELECT * FROM sales
B. There is no way to share data between PySpark and SQL.
C. spark.sql("sales")
D. spark.delta.table("sales")
E. spark.table("sales")
Answer: E Explanation: The data engineering team can use the spark.table method to access the Delta table sales in PySpark. This method returns a DataFrame representation of the Delta table, which can be used for further processing or testing. The spark.table method works for any table that is registered in the Hive metastore or the Spark catalog, regardless of the file format1. Alternatively, the data engineering team can also use the DeltaTable.forPath method to load the Delta table from its path2. Reference: 1: SparkSession | PySpark 3.2.0 documentation 2: Welcome to Delta Lake's Python documentation page ” delta-spark 2.4.0 documentation
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