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Microsoft DP-750 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Prepare and process data | 30-35% | - Data transformation and modeling
|
| Secure and govern data using Unity Catalog | 15-20% | - Access control and policies
|
| Configure and manage Azure Databricks environments | 15-20% | - Security and authentication setup
|
| Deploy and manage data pipelines and workloads | 30-35% | - Pipeline design and orchestration
|
Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:
You need to curate Unity Catalog objects that reference the ERP data. The solution must meet the governance requirements.
What should you do?
- A. In the managed analytics catalog, create views that reference foreign catalog tables that use three-level naming.
- B. Create a volume in the foreign catalog and store curated Delta tables in the volume.
- C. Create Delta tables directly inside the foreign catalog by running the CREATE TABLE AS SELECT (CTAS) command.
- D. Run the ALTER TABLE command on the foreign catalog tables to add new columns that are required for analytics.
Correct Answer: A 🗳️
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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.
Correct Answer:
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
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.
Correct Answer:
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
You have an Azure Databricks workspace.
Users report that a Databricks notebook that runs each day takes longer than expected to run.
When reading the Directed Acyclic Graph (DAG), you discover the following issues concerning the Apache Spark stage:
* Most tasks in the stage finish quickly.
* A few tasks in the stage run more slowly.
* The CPU is underutilized at the end of the stage.
* The slow tasks process many more input records.
* The stage is blocked while it waits for the few slow tasks.
What is the root cause of the issues?
- A. shuffling
- B. spilling
- C. skewing
- D. caching
Correct Answer: C 🗳️
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You have an Azure Databricks workspace that is enabled for Unity Catalog.
You plan to ingest data from CSV files stored in Azure Data Lake Storage Gen2. New rows are appended frequently.
You need to implement a data ingestion solution that meets the following requirements:
* New data must be available in near-real-time (NRT).
* The data must be stored in managed Delta tables.
* The solution must minimize custom code and maintenance effort.
What should you include in the solution?
- A. an Azure Data Factory pipeline
- B. scheduled Apache Spark batch jobs
- C. an external table that references the CSV files
- D. Auto Loader
Correct Answer: D 🗳️
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