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Databricks Certified Data Engineer Associate Exam Sample Questions (Q16-Q21):

NEW QUESTION # 16
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 turn on the Serverless feature for the SQL endpoint.
B. They can increase the cluster size of the SQL endpoint.
C. They can turn on the Auto Stop feature for the SQL endpoint.
D. They can increase the maximum bound of the SQL endpoint's scaling range.
E. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to "Reliability Optimized."

Answer: C

Explanation:
https://community.databricks.com/t5/data-engineering/sequential-vs-concurrency-optimization-questions-from-query/td-p/36696

NEW QUESTION # 17
Which tool is used by Auto Loader to process data incrementally?

A. Checkpointing
B. Databricks SQL
C. Spark Structured Streaming
D. Unity Catalog

Answer: C

Explanation:
Auto Loader in Databricks utilizes Spark Structured Streaming for processing data incrementally. This allows Auto Loader to efficiently ingest streaming or batch data at scale and to recognize new data as it arrives in cloud storage. Spark Structured Streaming provides the underlying engine that supports various incremental data loading capabilities like schema inference and file notification mode, which are crucial for the dynamic nature of data lakes.
Reference:
Databricks documentation on Auto Loader: Auto Loader Overview

NEW QUESTION # 18
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 managerwants 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 cannot ensure the query does not cost the organization money beyond the first week of the project's release.
B. They can set the query's refresh schedule to end on a certain date in the query scheduler.
C. They can set a limit to the number of DBUs that are consumed by the SQL Endpoint.
D. They can set the query's refresh schedule to end after a certain number of refreshes.
E. They can set a limit to the number of individuals that are able to manage the query's refresh schedule.

Answer: B

NEW QUESTION # 19
A data analyst has a series of queries in a SQL program. The data analyst wants this program to run every day. They only want the final query in the program to run on Sundays. They ask for help from the data engineering team to complete this task.
Which of the following approaches could be used by the data engineering team to complete this task?

A. They could wrap the queries using PySpark and use Python's control flow system to determine when to run the final query.
B. They could only run the entire program on Sundays.
C. They could submit a feature request with Databricks to add this functionality.
D. They could automatically restrict access to the source table in the final query so that it is only accessible on Sundays.
E. They could redesign the data model to separate the data used in the final query into a new table.

Answer: A

Explanation:
This approach would allow the data engineering team to use the existing SQL program and add some logic to control the execution of the final query based on the day of the week. They could use the datetime module in Python to get the current date and check if it is a Sunday. If so, they could run the final query, otherwise they could skip it. This way, they could schedule the program to run every day without changing the data model or the source table. Reference: PySpark SQL Module, Python datetime Module, Databricks Jobs

NEW QUESTION # 20
A data engineer is migrating pipeline tasks to reduce operational toil. The workspace uses Unity Catalog and is in a region that supports serverless. The engineer wants Databricks to auto-select instance types, manage scaling, apply Photon, and handle runtime upgrades automatically for job runs.
How should the data engineer meet this requirement while adhering to Databricks constraints?

A. Use a Pro SQL warehouse and schedule Python notebook tasks to execute as pipeline steps.
B. Run the job on a serverless compute for workflows configuration, ensuring Unity Catalog is enabled and regional support is available.
C. Create a job with a single-task job cluster and manually set the instance families and minimum
/maximum workers.
D. Use an all-purpose cluster with cluster policies to enforce standard sizes and enable autoscaling.

Answer: B

Explanation:
Serverless compute for workflows is designed to minimize operational overhead for Databricks Jobs while enforcing Unity Catalog governance. When a job is configured to run on serverless compute (in supported regions), Databricks automatically selects instance types, manages autoscaling, enables Photon where applicable, and handles runtime upgrades without user intervention. This directly satisfies the requirement to reduce operational toil. All-purpose clusters (option B) and traditional job clusters (option C) still require users to manage cluster sizing, instance families, and runtime versions, which contradicts the goal of automation. SQL warehouses (option A) are intended for SQL workloads and do not support running general Python notebook job tasks as workflow steps. Databricks documentation specifies that serverless workflows are compatible with Unity Catalog and are the recommended approach when users want fully managed compute for scheduled pipelines and tasks, provided the workspace is in a supported region. This makes serverless compute the correct and fully compliant solution.

NEW QUESTION # 21
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