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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deploy and maintain data pipelines and workloads | 30–35% | - Monitor, troubleshoot, and maintain workloads
|
| Topic 2: Secure and govern Unity Catalog objects | 15–20% | - Manage data sharing and permissions
|
| Topic 3: Set up and configure an Azure Databricks environment | 15–20% | - Integrate with Azure services
|
| Topic 4: Prepare and process data | 30–35% | - Optimize and manage data storage
|
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NEW QUESTION # 68
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job! runs every hour.
Occasionally, the job run takes longer than one hour to complete. Overlapping runs must be prevented to avoid data corruption.
You need to configure the job scheduling behavior.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Two settings address the overlapping-run problem:
Concurrent Runs policy set to ' Skip ' (or ' Allow only one concurrent run ' ). When a new scheduled trigger fires while the previous run is still in progress, the new run is skipped rather than starting alongside the ongoing one. This prevents two runs from writing to the same tables at the same time - which is the data corruption risk the question highlights.
Cron-based schedule for the hourly trigger. A cron expression defines the regular execution cadence.
Combined with the concurrency setting, the job runs hourly but never overlaps.
An alternative to ' Skip ' is ' Wait ' (queue the new run), which ensures every scheduled run eventually executes - but for this scenario where overlapping is the primary concern, skipping the missed run is typically preferable to building up a queue of back-to-back executions.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/configure-jobs#concurrent-runs
NEW QUESTION # 69
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to create an external volume named Volume1 in an existing schema. Volume1 must expose files from an Azure Storage container. The solution must meet the following requirements:
* Ensure that authentication does NOT require storing credentials in Databricks
* Ensure that users can access the files, but NOT modify the files.
* Follow the principle of least privilege
Which type of authentication should you configure, and which permission should you grant to the users? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
For authentication, a Managed Identity (via a Databricks Access Connector) is the right choice. The Access Connector wraps an Azure-managed identity so Databricks can authenticate to Azure Storage without any credentials being stored in the workspace. The cloud security team controls the identity through Azure RBAC
- there are no secrets to rotate or leak inside Databricks.
For the permission, READ FILES on the volume is exactly right. It allows users to read and list files through the volume path while blocking writes, deletes, and modifications. This is the minimum necessary access, honouring the principle of least privilege.
WRITE FILES would allow modifications, contradicting 'users can access but NOT modify.' ALL PRIVILEGES grants far more than needed. Service principals with stored client secrets would mean credentials inside Databricks, violating the 'does not require storing credentials' requirement.
Reference: https://learn.microsoft.com/en-us/azure/databricks/connect/unity-catalog/volumes
NEW QUESTION # 70
What does the VACUUM command do in Delta Lake?
Answer: A
Explanation:
VACUUM removes old, unreferenced files that are no longer needed after updates or deletes. It helps manage storage but does not improve query performance directly. OPTIMIZE handles performance improvements. Schema changes are unrelated.
NEW QUESTION # 71
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that writes numerical data to a table named Table1 by using a data quality validation rule named rule1.
You need to modify rule1 to meet the following requirements:
- Ensure that amount is always greater than 0.
- Prevent an update to Table1 from being committed when data that
violates rule1 is detected.
Which statement should you execute?
Answer: C
Explanation:
To ensure the data validation rule forces the pipeline update to abort and roll back transactions when data violates the condition, you must use a "fail" expectation operator. In Databricks Lakeflow Spark Declarative Pipelines (SDP), the command/syntax depends on whether your pipeline is written in Python or SQL.
Python Implementation
If your pipeline uses Python, apply the @dp.expect_or_fail decorator above your table definition (note: dp is the standard alias for the databricks.pipelines module in Lakeflow SDP):
dp.expect_or_fail("amount_greater_than_zero", "amount > 0")
Reference:
https://docs.databricks.com/aws/en/ldp/expectations
NEW QUESTION # 72
You have an Azure Databricks workspace that is enabled for Unity Catalog You have an Apache Spark Structured Streaming job that writes data to a Delta table.
After the cluster restarts, the streaming job reprocesses previously ingested data You need to prevent the streaming job from reprocessing the data after the cluster restarts.
What should you do?
Answer: A
Explanation:
The correct answer is B - configure a checkpoint location.
A checkpoint is the Structured Streaming mechanism for fault tolerance. Databricks writes the committed offset (i.e., how far through the source stream the job has successfully read and processed) to a durable path in ADLS Gen2 or DBFS after each micro-batch. When the cluster restarts, the engine reads that offset and resumes from the next unprocessed record - nothing is reprocessed, nothing is skipped.
Option A (increase trigger interval) affects how frequently micro-batches run but does nothing to record progress between runs. Option C (watermark) handles late-arriving events in event-time windows but doesn't control source offset tracking. Option D (enable CDF on the target table) tracks changes made to a Delta table for downstream consumers - it has no bearing on the streaming job's own fault tolerance or offset management.
Checkpointing is a required configuration for any production streaming job. Without it, every cluster restart triggers a full replay from the source.
Reference: https://learn.microsoft.com/en-us/azure/databricks/structured-streaming/query-recovery
NEW QUESTION # 73
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