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Microsoft DP-750 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Deploy and manage data pipelines and workloads | 30-35% | - Pipeline design and orchestration
|
| Secure and govern data using Unity Catalog | 15-20% | - Data governance fundamentals
|
| Prepare and process data | 30-35% | - Data quality and validation
|
| Configure and manage Azure Databricks environments | 15-20% | - Security and authentication setup
|
Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Sales_orders. Sales.orders stores historical sales data.
You receive a daily CSV file daily that contains new sales records only. The file does NOT contain updates to existing rows You need to load the daily data into Sales.orders. The solution must meet the following requirements:
* Preserve the existing data.
* Add only the new records.
* Minimize processing effort.
Which command should include in the loading strategy?
- A. INSERT INTO
- B. INSERT OVERWRITE
- C. UPDATE
Explanation: Only visible for TestPassKing members. You can sign-up / login (it's free).
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job! contains three tasks named Task1, Task2. and Task3.
If Task1 fails, Task2 and Task3 must be prevented from running. Successfully completed tasks must NOT rerun during recovery.
You need to configure Job1 to support controlled failure handling and recovery What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Explanation:
Two configurations are needed:
Task dependency with ' All succeeded ' run condition: Set Task2 and Task3 to depend on Task1. Change the run condition on Task2 and Task3 to ' All succeeded ' - this means they only execute when all their upstream dependencies (Task1) have succeeded. If Task1 fails, both downstream tasks are skipped automatically, not run with failed inputs.
Repair run for recovery: Lakeflow Jobs ' Repair Run feature lets you re-execute only the tasks that failed (Task1 in this case) and their dependents (Task2 and Task3 if they were skipped), while skipping Task1 and any other tasks that already completed successfully. Successfully completed tasks are never re-executed during repair - their results are reused as-is.
Together these provide both controlled failure propagation (nothing runs downstream of a failure) and efficient recovery.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/repair-job-failures
You need to deploy Declarative Automation Bundles to a development environment. The solution must support automated and repeatable deployments across environments.
What should you use?
- A. the Databricks SDK for Python
- B. the Jobs UI
- C. Git folders
- D. the Databricks CLI
Explanation: Only visible for TestPassKing members. You can sign-up / login (it's free).
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. skewing
- B. shuffling
- C. spilling
- D. caching
Explanation: Only visible for TestPassKing members. You can sign-up / login (it's free).
You have an Azure Databricks workspace that uses Unity Catalog.
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that ingests data into a managed Delta table named Table1. Table1 is used for analytics.
New columns are added to the source data, causing pipeline failures during writes to Table1.
You need to prevent the pipeline failures. The solution must ensure that schema changes are detected and handled.
What should you do?
- A. Use row filters to exclude records that have new columns.
- B. Create a separate table for each schema version.
- C. Enable schema evolution.
- D. Disable schema enforcement for Table1.
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