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Microsoft DP-750 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Set up and configure an Azure Databricks environment | 15–20% | - Select and configure compute resources
|
| Topic 2: Secure and govern Unity Catalog objects | 15–20% | - Manage data sharing and permissions
|
| Topic 3: Deploy and maintain data pipelines and workloads | 30–35% | - Monitor, troubleshoot, and maintain workloads
|
| Topic 4: Prepare and process data | 30–35% | - Optimize and manage data storage
|
Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:
You have an Azure Databricks workspace that is attached to a Unity Catalog metastore named metastore1.
Metastore1 contains a catalog named catalog 1.
You need to create a new schema named schema2 that meets the following requirements:
* Is contained in catalog1
* Uses abfss://containergstorageaccount.dfs.core.windows.net/data as the Managed location Which SQL statement should you execute?
- A. CREATE CATALOG schema2
MANAGED LOCATION ' abfss://[email protected]/data ' ; - B. CREATE SCHEMA catalog1.schema2
LOCATION ' abfss://[email protected]/data ' ; - C. CREATE SCHEMA catalog1.schema2
WITH DBPROPERTIES (LOCATION= ' abfss://[email protected]/data
' ); - D. CREATE SCHEMA catalog1.schema2
MANAGED LOCATION ' abfss://[email protected]/data ' ;
Correct Answer: D 🗳️
Explanation: Only visible for VCE4Plus members. You can sign-up / login (it's free).
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named CatalogV Catalog1 contains a schema named Schema! and a table named Table1.
You need to ensure that access to the data in Table1 is controlled by using attribute based access control (ABAC).
What should you apply to Table1, and how should you control access for users? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Attribute-based access control (ABAC) in Unity Catalog is implemented through row filters. A row filter is a SQL function registered on a table that evaluates the identity or group membership of the querying user and returns only the rows they ' re entitled to see.
The key functions are CURRENT_USER() (returns the logged-in user ' s email) and IS_ACCOUNT_GROUP_MEMBER() (returns true if the user belongs to a specified group). By building filter logic around these, you create access rules that are data-driven - a user in the ' EMEA ' group sees EMEA rows, a user in ' APAC ' sees APAC rows - without maintaining separate table-level grants per data segment.
This is what distinguishes ABAC from role-based access control: decisions are based on the user ' s attributes evaluated at query time, not on static grant lists. The filter is transparent to end users - they query the table normally and only receive rows the policy allows.
Reference: https://learn.microsoft.com/en-us/azure/databricks/data-governance/unity-catalog/row-and-column- filters
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. spilling
- B. skewing
- C. caching
- D. shuffling
Correct Answer: B 🗳️
Explanation: Only visible for VCE4Plus 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.
Correct Answer:
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 have an Azure Databricks workspace that contains a Delta table named Table 1. Table 1 has accumulated obsolete files.
You need to reduce storage costs. The solution must preserve 30 days of time travel history. Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. Set the delta. logRetentionDuration table property to 30 days.
- B. Run the OPTIMIZE command on Table1
- C. Reduce the deleted file retention period to one day.
- D. Set the delta.deletedFileRetentionDuration table property to 10 days
- E. Run the vacuum command on Table1.
Correct Answer: D,E 🗳️
Explanation: Only visible for VCE4Plus members. You can sign-up / login (it's free).
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