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

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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions (Q106-Q111):

NEW QUESTION # 106
Hotspot Question
You have an Azure Databricks job named Job1 that contains an ingestion task named Task1 and transformation task named Task2.
You need to ensure that if Task1 fails, the task retries automatically, and Task2 is prevented from running.
How should you configure Job1? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 107
You have an Azure Databricks workspace that is enabled for Unity Catalog You have a complex job named Job1 that contains eight tasks. Job! takes multiple hours to complete During the last job run, the final task fails due to a transient issue.
You need to retry the last task without rerunning tasks that have already completed.
What should you do?

Answer: A

Explanation:
The correct answer is B - Repair the current job run.
Repair Run is designed for exactly this situation: a long-running job where most tasks succeeded but the final task failed due to a transient issue. Instead of restarting the entire eight-task job from the beginning - wasting hours of compute - Repair Run re-executes only the failed task and any dependents that were skipped as a result. All tasks that completed successfully are marked done and their outputs are reused.
Option A (update job parameters) changes configuration for future runs but doesn't re-execute the failed task in the current run. Option C (Restart Job1) re-runs every task from the start - precisely what the question says to avoid. Option D (disable and re-enable the schedule) creates a brand-new run on the next schedule trigger, again starting from the beginning rather than repairing the existing run.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/repair-job-failures


NEW QUESTION # 108
You have a Declarative Automation Bundle.
You plan to use the Databricks CLI to deploy the bundle to an environment named dev.
How should you complete the CLI command? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

The databricks bundle command group is used to validate, deploy, run, and manage Declarative Automation Bundles. The deploy subcommand deploys the bundle's defined resources-such as Lakeflow jobs, pipelines, dashboards, files, and notebooks-to the target Azure Databricks workspace.
The --target dev option selects the target named dev from the targets mapping in the bundle's databricks.yml configuration. Therefore, the complete command is databricks bundle deploy --target dev. The shorter -t dev syntax is also valid, but the answer area provides the full --target form. Microsoft Learn databricks workspace manages workspace objects and is not the command group for deploying bundles. az databricks workspace manages Azure Databricks workspace resources through the Azure CLI. The run dev option would attempt to execute a deployed bundle resource, while sync --target dev synchronizes local files with the workspace but does not deploy the complete bundle configuration.


NEW QUESTION # 109
Hotspot Question
You have an Azure Databricks workspace that contains an all-purpose cluster named Cluster1.
You discover that out-of-memory (OOM) errors intermittently cause jobs running on Cluster1 to fail.
You need to identify the root cause of the failures by analyzing the runtime execution behavior.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 110
Which ingestion option should you recommend for each data source? To answer, drag the appropriate options to the correct data sources. Each option may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
The right ingestion tool depends on the source characteristics:
File-based telemetry and maintenance data # Auto Loader (cloudFiles). It monitors ADLS Gen2 for new arrivals, handles schema inference and evolution for the frequent schema drift, and requires minimal operational effort.
Real-time telemetry from Event Hubs # Spark Structured Streaming with the azure-eventhubs-spark connector. This provides exactly-once semantics and checkpoint-based recovery, satisfying 'resume processing after failures without reprocessing.' Structured maintenance data from PostgreSQL # JDBC connector. Databricks supports direct JDBC reads from relational databases with pushdown predicates.
Daily CSV ERP extracts (50-100 GB) # COPY INTO or Auto Loader. Both support idempotent incremental batch loading into Delta tables with minimal code.
Reference: https://learn.microsoft.com/en-us/azure/databricks/ingestion/auto-loader/


NEW QUESTION # 111
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