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Microsoft DP-750 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Deploy and maintain data pipelines and workloads30–35%- Monitor, troubleshoot, and maintain workloads
  • 1. Apply SDLC practices and version control
  • 2. Troubleshoot failures, repair and restart jobs
  • 3. Monitor performance, logs, and execution metrics
- Build and orchestrate pipelines
  • 1. Implement CI/CD with Git, Databricks Asset Bundles, CLI, and APIs
  • 2. Design and implement Lakeflow Spark Declarative Pipelines
  • 3. Configure Lakeflow Jobs: schedules, triggers, alerts, retries
Topic 2: Set up and configure an Azure Databricks environment15–20%- Select and configure compute resources
  • 1. Choose compute types: serverless, job compute, SQL warehouse, classic compute
  • 2. Manage workspace settings, permissions, and networking
  • 3. Configure cluster policies, instance pools, and libraries
- Integrate with Azure services
  • 1. Connect to Azure Data Lake Storage, Azure Data Factory, Microsoft Entra ID
  • 2. Configure monitoring with Azure Monitor and diagnostic settings
Topic 3: Prepare and process data30–35%- Ingest and transform data
  • 1. Ingest batch and streaming data from multiple sources
  • 2. Implement schema enforcement, schema drift, and slowly changing dimensions
  • 3. Transform using Spark SQL, PySpark, Scala, and Delta Lake
- Optimize and manage data storage
  • 1. Optimize Delta tables: partitioning, Z-ordering, vacuum, optimize
  • 2. Handle structured, semi-structured, and unstructured data
  • 3. Implement lakehouse architecture and manage table versions
Topic 4: Secure and govern Unity Catalog objects15–20%- Manage data sharing and permissions
  • 1. Set up external locations and storage credentials
  • 2. Grant and revoke permissions, manage groups and service principals
- Implement data governance and security
  • 1. Enforce data quality, lineage, and auditing
  • 2. Manage catalogs, schemas, tables, views, and volumes
  • 3. Configure access control: row-level, column-level, attribute-based security

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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions (Q73-Q78):

NEW QUESTION # 73
You have an Azure Databricks workspace that contains a Git folder and uses Azure Repos as the Git provider.
From the main branch, you create a branch named Branch1. You commit changes to Branch1.
You need to incorporate the changes from Branch1 into main. The solution must preserve the commit history in the repository.
Which command should you run?

Answer: A

Explanation:
To incorporate changes from your feature branch into the main branch while keeping every individual commit intact, you must use a fast-forward merge or a standard merge commit.
In the Azure Databricks Git folders UI or via standard Git operations, use the following command:
The Correct Command: git merge <branch-name>
Preserves History: Unlike a squash merge, a standard merge keeps all individual commit messages, authors, and timestamps.
Maintains Timeline: It seamlessly integrates the exact commit graph from your feature branch into the main branch.
Reference:
https://docs.databricks.com/aws/en/repos/git-operations-with-repos


NEW QUESTION # 74
You have an Azure Databricks workspace that is enabled for Unity Catalog. You plan to run the following PySpark code.

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
For HOTSPOT questions, each statement must be evaluated against the actual PySpark code shown in the answer area. Key evaluation principles:
DataFrames are immutable - every transformation returns a new DataFrame; the original is unchanged.
Transformations (filter, select, groupBy, join) are lazy and only execute when an action (show, count, write) is called.
Null handling: df.filter(col != None) is incorrect in PySpark due to SQL null semantics; use col.isNotNull() or dropna() instead. Schema changes: using mergeSchema=true or schema evolution handles new columns.
Write modes: ' overwrite ' replaces existing data; ' append ' adds to it.
Always check whether the code uses the correct Delta format (.format( ' delta ' )), Unity Catalog three-part naming, and whether write operations include a checkpointLocation for streaming queries. Evaluate each statement strictly on what the code does, not on what it might intend to do.
Reference: https://learn.microsoft.com/en-us/azure/databricks/pyspark/basics


NEW QUESTION # 75
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two catalogs named Catalog1 and Catalog2.
An external application uses a service principal named SP1 to connect to a SQL warehouse.
You need to ensure that SP1 can query the data in Catalog1 and Catalog2. The solution must follow the principle of least privilege.
Which permissions should you grant to SP1 for the catalogs?

Answer: A


NEW QUESTION # 76
Which Azure service is best integrated with Databricks Unity Catalog for centralized data governance?

Answer: D

Explanation:
Microsoft Purview integrates with Unity Catalog to provide centralized data governance, classification, and lineage tracking. It helps organizations manage data compliance and discovery. Key Vault handles secrets, not governance. DevTest Labs is for testing environments.
Azure Automation is for workflow automation.


NEW QUESTION # 77
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to implement a data lifecycle and expiration solution that meets the following requirements
* Transaction logs and deleted data files that are older than 90 days must be removed from Delta tables to reclaim storage.
* All the tables must remain available for querying during the cleanup process.
* Administrative effort must be minimized
What should you do for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Two actions are needed to reclaim storage while keeping tables queryable:
Set delta.deletedFileRetentionDuration and delta.logRetentionDuration to 90 days on each table. These properties define the retention floor - VACUUM will not touch anything newer than this threshold, so no data needed for time travel within 90 days can be accidentally removed.
Run VACUUM on each table. VACUUM is the Delta Lake command that physically removes data files and transaction log entries older than the retention duration. Importantly, VACUUM runs as a background operation - it uses Delta Lake's MVCC (multi-version concurrency control) to ensure that concurrent reads against the table continue uninterrupted while cleanup happens. Tables are fully available throughout.
OPTIMIZE compacts small files for query performance but doesn't delete anything. Manually deleting files outside the Delta protocol would corrupt the table.
Reference: https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/delta-vacuum


NEW QUESTION # 78
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