Azure Databricks Data Engineer Associate Exam (DP-750) - Microsoft

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Question #3 Verified Q&A
You want a Lakeflow Job to send an alert when a run exceeds its expected completion time, before it is timed out. Which configuration is required? Deploy and maintain data pipelines and workloads
A Set only a Timeout value and select Failure in the job notification
B Increase the maximum concurrent runs for the job
C Select Start and Success in the job notification
D Set a Run duration Warning threshold and select Duration Warning in the job notification
E Set a Streaming backlog threshold and select Failure in the job notification
Correct Answer: Set a Run duration Warning threshold and select Duration Warning in the job notification
Explanation:
A slow-job alert requires an expected duration threshold and a Duration Warning notification. A Timeout value marks the run as Timed Out if it exceeds the maximum completion time, which is different from warning on a slow run. Streaming backlog applies to streaming metrics, and concurrent runs do not control alerting behavior.<br />
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Related learning material: https://learn.microsoft.com/azure/databricks/jobs/notifications
Question #4 Verified Q&A
A Unity Catalog managed Delta table uses automatic liquid clustering. A deployment runs CREATE OR REPLACE TABLE sales without CLUSTER BY AUTO. What happens? Prepare and process data
A Automatic liquid clustering stays enabled but historical workload is cleared.
B Automatic liquid clustering stays enabled and previous clustering columns are preserved.
C Automatic liquid clustering is disabled and previous clustering columns are not preserved.
D The table switches to manual clustering on the last selected columns.
Correct Answer: Automatic liquid clustering is disabled and previous clustering columns are not preserved.
Explanation:
Databricks states that running CREATE OR REPLACE TABLE without CLUSTER BY AUTO disables automatic liquid clustering on a table that previously had it enabled, and the existing clustering columns are not preserved. To keep automatic clustering and the prior selection state, you must include CLUSTER BY AUTO in the replace statement. Predictive optimization does not silently restore the setting, and Databricks does not convert the table to manual clustering on the old keys.<br />
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Related learning material: https://learn.microsoft.com/azure/databricks/delta/clustering
Question #5 Verified Q&A
You already set delta. logRetentionDuration to 30 days on main.curated.orders, but analysts still cannot time travel beyond 7 days. Which SQL statement fixes the retention configuration? Secure and govern data with Unity Catalog
A ALTER TABLE main.curated.orders SET TBLPROPERTIES ('delta.logRetentionDuration' = 'interval 30 days')
B ALTER TABLE main.curated.orders SET TBLPROPERTIES ('delta.vacuumRetentionDuration' = 'interval 30 days')
C ALTER TABLE main.curated.orders SET TBLPROPERTIES ('delta.timeTravelRetentionDuration' = 'interval 30 days')
D ALTER TABLE main.curated.orders SET TBLPROPERTIES ('delta.deletedFileRetentionDuration' = 'interval 30 days')
E ALTER TABLE main.curated.orders SET TBLPROPERTIES ('delta.historyRetentionDuration' = 'interval 30 days')
Correct Answer: ALTER TABLE main.curated.orders SET TBLPROPERTIES ('delta.deletedFileRetentionDuration' = 'interval 30 days')
Explanation:
Time travel requires both retained log history and retained data files. VACUUM uses delta. deletedFileRetentionDuration, which defaults to 7 days, so increasing only delta.logRetentionDuration does not preserve older data files. The other property names are invalid or affect only history, not the data files needed for time travel.<br />
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Related learning material: https://learn.microsoft.com/azure/databricks/delta/history
Question #6 Verified Q&A
You need to share data with a partner that does not use Databricks. Their analysts will access the data through Power BI, MFA must be enforced by the partner's identity provider, and you must avoid distributing long-lived shared credentials. Which two actions should you take? (Select all that apply)<br />
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Select all correct answers:<br />
Secure and govern data with Unity Catalog
A Configure an OIDC federation policy with the partner IdP issuer URL, subject claim, subject, and audience
B Create a token-based open recipient and send the activation link over a secure channel
C Set a short token lifetime on a bearer-token recipient so it behaves like federated authentication
D Create an open recipient that uses OIDC federation
E Ask the partner for a Databricks sharing identifier and use Databricks-to-Databricks sharing
Correct Answer: Configure an OIDC federation policy with the partner IdP issuer URL, subject claim, subject, and audience, Create an open recipient that uses OIDC federation
Explanation:
Creating an open recipient that uses OIDC federation is the correct sharing model for a non-Databricks partner when you want authentication handled through the partner's IdP. Configuring the OIDC federation policy with issuer URL, subject claim, subject, and audience is required so Databricks can validate the partner's JWTs and honor IdP- controlled MFA and token lifetime. A token-based open recipient still relies on Databricks- issued bearer credentials, even if sent securely or given a short lifetime. Databricks-to- Databricks sharing requires the recipient to have a Unity Catalog-enabled Databricks workspace and a sharing identifier.<br />
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Related learning material: https://learn.microsoft.com/azure/databricks/delta-sharing/create-recipient-oidc-fed
Question #7 Verified Q&A
A Lakeflow Job has two notebook tasks that both reference the same remote Git repository. A team member suggests setting branch main on one task and release- hotfix on the other. What is true about this configuration? Deploy and maintain data pipelines and workloads
A Configure a single Git reference for the job because all tasks must run from the same snapshot commit.
B Use a branch for one task and a commit hash for the other to avoid conflicts.
C Add two Git providers to the job and assign one provider per task.
D Set a different branch on each task because Git settings are task-specific.
E Store one notebook in the workspace and the other in Git so each task can use a different version.
Correct Answer: Configure a single Git reference for the job because all tasks must run from the same snapshot commit.
Explanation:
Lakeflow Jobs that use a remote Git repository require all tasks in the job to reference the same commit. You specify only one Git reference type, branch, tag, or commit, and Databricks snapshots that repository state at job start. Per-task branches or mixed Git references do not satisfy this rule.

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