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Microsoft DP-750 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Deploy and maintain data pipelines and workloads30-35%- Manage production workloads
  • 1. Monitor and troubleshoot pipelines
  • 2. Maintain production data engineering solutions
  • 3. Integrate Git-based development workflows
  • 4. Implement CI/CD processes
  • 5. Deploy workloads using Databricks Asset Bundles
  • 6. Create and manage Lakeflow Jobs
  • 7. Optimize workload performance and reliability
Topic 2: Set up and configure an Azure Databricks environment15-20%- Create and configure Azure Databricks workspaces
  • 1. Configure workspace settings
  • 2. Manage Databricks runtimes
  • 3. Configure compute resources and clusters
  • 4. Configure networking and connectivity
Topic 3: Prepare and process data30-35%- Ingest and transform data
  • 1. Apply medallion architecture patterns
  • 2. Transform data using SQL and Python
  • 3. Implement Delta Lake tables
  • 4. Implement streaming data processing
  • 5. Use Auto Loader and batch ingestion
  • 6. Optimize storage and table performance
  • 7. Implement data quality controls
  • 8. Model and partition data
Topic 4: Secure and govern Unity Catalog objects15-20%- Implement governance and security
  • 1. Manage data lineage and auditing
  • 2. Configure Unity Catalog
  • 3. Implement data-sharing capabilities
  • 4. Implement access control and permissions
  • 5. Manage catalogs, schemas, and tables

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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions (Q55-Q60):

NEW QUESTION # 55
You have an Azure Databricks workspace named Workspace1 that contains a Git folder linked to a remote Git repository. The Git folder contains a Databricks notebook named Notebook1.
From the main branch, you create a feature branch named Branch1 and commit changes to Notebook1.
Another user commits changes to Notebook1 in main.
When you attempt to merge Branch1 into main, a merge conflict occurs.
You need to ensure that Notebook1 in main includes the changes from both branches.
What should you do?

Answer: C

Explanation:
Branch1 must first incorporate the current changes from main. During that merge, the conflicting sections of Notebook1 can be reviewed and resolved so that the resulting feature-branch version contains the required work from both branches. After committing the resolution, Branch1 can be merged into main normally.
Cloning either branch into another Git folder creates another working copy but does not resolve the conflicting histories. Applying changes directly to main bypasses the controlled feature-branch workflow and risks omitting or overwriting one contributor's work. Resolving the conflict on Branch1 also allows the combined notebook to be tested before updating the shared main branch. Therefore, merging main into Branch1, resolving the conflict, and completing the final merge is the correct workflow.


NEW QUESTION # 56
Which layer contains cleaned and conformed data in Databricks Lakehouse architecture?

Answer: A

Explanation:
Silver layer contains cleaned, validated, and enriched data ready for analytics. Bronze stores raw ingested data. Gold contains aggregated business-level data. Raw is not part of formal medallion architecture naming.


NEW QUESTION # 57
Which operation guarantees ACID compliance in Delta Lake?

Answer: B

Explanation:
Delta Lake ensures ACID compliance through its transaction log (Delta log). It tracks all changes, enabling consistency, isolation, and rollback capabilities. File append operations alone are not transactional. RDD transformations are low-level and not ACID-aware.


NEW QUESTION # 58
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1. Job1 contains multiple tasks.
Failures of non-critical tasks must be logged but must NOT trigger notifications. Notifications must be triggered only when critical tasks have failed, and Job1 has completed You need to configure the job alerting behavior.
What should trigger a notification?

Answer: D


NEW QUESTION # 59
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two Delta tables named Table1 and Table2 of the same data type.
Table1 contains a column named Columnl. Table2 contains a column named Column2. You run the following query.
SELECT Column1
FROM Table1
GROUP BY Column1
HAVING COUNT(") > 1
INTERSECT
SELECT C0lumn2
FROM Table2
GROUP BY Column2
HAVING COUNT(') > 1;
What occurs when you run the query?

Answer: D

Explanation:
The correct answer is B - values appear in both tables more than once.
Reading the query from the inside out: each subquery identifies values that appear more than once within their own table. The first subquery returns Column1 values that are duplicated in Table1. The second returns Column2 values that are duplicated in Table2. INTERSECT then returns only the values that appear in both result sets - meaning values that are duplicated in Table1 AND also duplicated in Table2.
Option A describes a UNION result (values in either table), not INTERSECT. Option C only considers duplicates in Table1, ignoring the Table2 condition - that would be the first subquery in isolation. Option D describes values exclusive to Table2 (EXCEPT or MINUS), the opposite of INTERSECT's requirement that values appear in both sets.
INTERSECT always requires membership in both operands - that's its fundamental definition in standard SQL.
Reference: https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/sql-ref-syntax-qry-select- setops


NEW QUESTION # 60
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