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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prepare and process data | 30-35% | - Data ingestion
|
| Topic 2: Secure and govern data using Unity Catalog | 15-20% | - Data governance fundamentals
|
| Topic 3: Configure and manage Azure Databricks environments | 15-20% | - Security and authentication setup
|
| Topic 4: Deploy and manage data pipelines and workloads | 30-35% | - Pipeline design and orchestration
|
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NEW QUESTION # 68
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have a complex job named Job1 that contains eight tasks. Job1 takes multiple hours to complete.
During the last job run, the final task fails due to a transient issue.
You need to retry the last task without rerunning tasks that have already completed.
What should you do?
Answer: A
Explanation:
Repairing the current job run allows Lakeflow Jobs to rerun the failed task while preserving the successful status of tasks that already completed. This prevents several hours of successful processing from being repeated and reduces both recovery time and compute consumption. If downstream tasks had been skipped because of the failure, they can also be included in the repair operation as appropriate. Updating job parameters changes configuration but does not retry the failed task in the existing run. Restarting Job1 creates a new run and ordinarily executes the workflow again from its beginning. Disabling and reenabling the schedule only changes whether future scheduled runs can start; it does not repair the failed execution. Repair Run is specifically designed for recovering selected failed or skipped tasks in a multi-task workflow.
NEW QUESTION # 69
You have an Azure Databricks workspace that contains multiple all-purpose clusters.
You discover that some clusters remain idle for long periods after users finish their work.
You need to reduce compute costs without affecting active workloads.
What should you do?
Answer: B
Explanation:
To reduce compute costs from idle clusters without impacting active workloads, you must configure Auto-Termination and use Cluster Policies.
Core Remedies
*-> Auto-Termination: Set a strict inactivity timeout (e.g., 20-30 minutes) on all-purpose clusters to automatically shut them down when idle.
Cluster Policies: Enforce maximum auto-termination limits across the workspace so users cannot disable or set excessively long idle timeouts.
Single User Access Mode: Use this mode where possible, as it tracks idleness more accurately than Shared mode by monitoring the specific user's activity.
Reference:
https://medium.com/@sujathamudadla1213/databricks-lakehouse-platform-describe-how- clusters-are-terminated-and-the-impact-of-terminating-a-b6236689fd2e
NEW QUESTION # 70
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to ensure that data lineage is captured and can be reviewed for tables accessed by Databricks notebooks and jobs. The solution must minimize administrative effort.
Which compute configuration should you use to capture the data lineage, and what should you use to review the data lineage? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Data lineage in Unity Catalog is captured automatically - but only when jobs and notebooks run on clusters that are Unity Catalog-aware. Specifically, clusters must use 'Shared' or 'Single User' access mode. Clusters set to 'No Isolation Shared' or legacy 'High Concurrency' mode do not emit lineage events to the Unity Catalog lineage service.
No instrumentation, logging code, or external tools are required. The lineage service operates transparently, intercepting read and write operations at the Spark plan level and recording the table-to-table and column-to- column relationships.
To review captured lineage, open Catalog Explorer, navigate to the table, and select the Lineage tab. This shows the upstream sources that populate the table and the downstream consumers that read from it - all as an interactive graph, with no additional tooling needed. This built-in visibility is one of the core governance benefits Unity Catalog provides.
Reference: https://learn.microsoft.com/en-us/azure/databricks/data-governance/unity-catalog/data-lineage
NEW QUESTION # 71
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have a complex job named Job1 that contains eight tasks. Job1 takes multiple hours to complete.
During the last job run, the final task fails due to a transient issue.
You need to retry the last task without rerunning tasks that have already completed.
What should you do?
Answer: A
Explanation:
You can retry only the failed final task by using the Repair Run feature in Azure Databricks. This allows you to rerun failed or skipped tasks without restarting the successful ones, preserving your Unity Catalog data lineage and saving hours of compute time.
Reference:
https://www.databricks.com/blog/2022/05/06/save-time-and-money-on-data-and-ml-workflows- with-repair-and-rerun.html
NEW QUESTION # 72
You need to recommend a compute type for the production ingestion workloads and BI workloads. The solution must meet the environment and compute requirements.
What should you recommend for each type of workload? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Production ingestion: Job compute
BI: Serverless SQL warehouse
Job compute is designed for automated production workloads executed through Lakeflow Jobs. Its lifecycle can be tied to the job run, providing workload isolation and avoiding the cost of maintaining an interactive all- purpose cluster continuously. It is therefore appropriate for scheduled ingestion and transformation processing. A serverless SQL warehouse is designed for BI and Databricks SQL workloads. It provides rapid startup, automatic infrastructure management, scaling, and optimized SQL-query execution for dashboards and reporting tools. All-purpose compute is intended primarily for interactive notebook development and exploration, while shared compute does not provide the same job-specific isolation or SQL-serving experience. Consequently, job compute should support production ingestion, and a serverless SQL warehouse should serve the BI workload.
NEW QUESTION # 73
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