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Databricks Certified Data Engineer Associate Exam Sample Questions (Q116-Q121):

NEW QUESTION # 116
A data engineer wants to schedule their Databricks SQL dashboard to refresh once per day, but they only want the associated SQL endpoint to be running when it is necessary.
Which of the following approaches can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

A. They can set up the dashboard's SQL endpoint to be serverless.
B. They can turn on the Auto Stop feature for the SQL endpoint.
C. They can reduce the cluster size of the SQL endpoint.
D. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.
E. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.

Answer: A

Explanation:
A serverless SQL endpoint is a compute resource that is automatically managed by Databricks and scales up or down based on the workload. A serverless SQL endpoint can be used to run queries and dashboards without requiring manual configuration or management. A serverless SQL endpoint is only active when it is needed and shuts down automatically when idle, minimizing the total running time and cost. A serverless SQL endpoint can be created and assigned to a dashboard using the Databricks SQL UI or the SQL Analytics API. References:
* Create a serverless SQL endpoint
* Assign a SQL endpoint to a dashboard
* SQL Analytics API

NEW QUESTION # 117
A dataset has been defined using Delta Live Tables and includes an expectations clause:
CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION FAIL UPDATE What is the expected behavior when a batch of data containing data that violates these constraints is processed?

A. Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset.
B. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.
C. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
D. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
E. Records that violate the expectation cause the job to fail.

Answer: E

Explanation:
Explanation
https://docs.databricks.com/en/delta-live-tables/expectations.html
Action
Result
warn (default)
Invalid records are written to the target; failure is reported as a metric for the dataset.
drop
Invalid records are dropped before data is written to the target; failure is reported as a metrics for the dataset.
fail
Invalid records prevent the update from succeeding. Manual intervention is required before re-processing.

NEW QUESTION # 118
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. Run the job on a serverless compute for workflows configuration, ensuring Unity Catalog is enabled and regional support is available.
B. Use a Pro SQL warehouse and schedule Python notebook tasks to execute as pipeline steps.
C. Use an all-purpose cluster with cluster policies to enforce standard sizes and enable autoscaling.
D. Create a job with a single-task job cluster and manually set the instance families and minimum
/maximum workers.

Answer: A

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 # 119
A data engineer is processing ingested streaming tables and needs to filter out NULL values in the order_datetime column from the raw streaming table orders_raw and store the results in a new table orders_valid using DLT.
Which code snippet should the data engineer use?

Answer: C

NEW QUESTION # 120
A data engineer is building a PySpark ingestion pipeline in a local IDE and must execute heavy DataFrame transformations on a remote Databricks cluster for scale while stepping through business logic locally. The workspace uses Unity Catalog and a cluster running Databricks Runtime 15.4 LTS. The engineer also plans to register UDFs.
What should the data engineer do to avoid runtime issues during development with Databricks Connect?

A. Match the local Python minor version to the cluster's Python minor version when using UDF's, and use a Databricks Connect package compatible with the cluster's Runtime version
B. Use any local Python version as long as the PySpark minor version matches, because the cluster's Python version is isolated from the client.
C. Disable Unity Catalog on the workspace because it conflicts with remote execution through Databricks Connect.
D. Use serverless, because Databricks Connect does not support assigned or shared clusters.

Answer: A

Explanation:
Databricks Connect requires tight compatibility between the local development environment and the remote cluster. When registering UDFs, the local Python minor version must match the cluster's Python minor version, and the Databricks Connect package must align with the Databricks Runtime version to prevent serialization and runtime incompatibilities.

NEW QUESTION # 121
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