Latest Microsoft DP-750 Test Prep | Test DP-750 Simulator

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Microsoft DP-750 Exam Syllabus Topics:

SectionWeightObjectives
Set up and configure an Azure Databricks environment15–20%- 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
- Select and configure compute resources
  • 1. Configure cluster policies, instance pools, and libraries
  • 2. Choose compute types: serverless, job compute, SQL warehouse, classic compute
  • 3. Manage workspace settings, permissions, and networking
Deploy and maintain data pipelines and workloads30–35%- Monitor, troubleshoot, and maintain workloads
  • 1. Troubleshoot failures, repair and restart jobs
  • 2. Apply SDLC practices and version control
  • 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
Prepare and process data30–35%- Optimize and manage data storage
  • 1. Implement lakehouse architecture and manage table versions
  • 2. Optimize Delta tables: partitioning, Z-ordering, vacuum, optimize
  • 3. Handle structured, semi-structured, and unstructured data
- Ingest and transform data
  • 1. Ingest batch and streaming data from multiple sources
  • 2. Transform using Spark SQL, PySpark, Scala, and Delta Lake
  • 3. Implement schema enforcement, schema drift, and slowly changing dimensions
Secure and govern Unity Catalog objects15–20%- Implement data governance and security
  • 1. Manage catalogs, schemas, tables, views, and volumes
  • 2. Configure access control: row-level, column-level, attribute-based security
  • 3. Enforce data quality, lineage, and auditing
- Manage data sharing and permissions
  • 1. Grant and revoke permissions, manage groups and service principals
  • 2. Set up external locations and storage credentials

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DP-750 Exam Questions and Implementing Data Engineering Solutions Using Azure Databricks Torrent Prep - DP-750 Test Guide

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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions (Q98-Q103):

NEW QUESTION # 98
Hotspot Question
You have an Azure Databricks workspace.
You need to ingest streaming data from Azure Event Hubs by using Apache Spark Structured Streaming. The solution must authenticate to Event Hubs and read the event payload.
How should you complete the PySpark code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 99
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 # 100
You have an Azure Databricks workspace and a remote Git repository named Repo1. Repo1 contains two branches named main and Branch1.
You are on a development team that works in Repo1.
You commit changes to Branch1 and must merge the changes into main.
Before completing the merge, you need to meet the following requirements:
* Ensure that Branch1 includes the changes committed to main since Branch1 was created.
* Ensure that merge conflicts are detected and resolved.
What should you do first?

Answer: C

Explanation:
The latest remote changes from main must first be retrieved so that the development environment has the current main-branch state. After pulling those updates, main can be merged into Branch1, and any conflicts can be detected and resolved before Branch1 is proposed for integration into main. Immediately merging a stale local copy of main into Branch1 could omit commits added remotely after Branch1 was created. Pulling Branch1 only synchronizes the feature branch and does not retrieve the required main-branch changes.
Creating a pull request before updating and testing Branch1 would defer conflict discovery until later in the integration process. Pulling the latest main changes is therefore the correct first operation in the sequence.
Microsoft Learn


NEW QUESTION # 101
What does the VACUUM command do in Delta Lake?

Answer: A

Explanation:
VACUUM removes old, unreferenced files that are no longer needed after updates or deletes. It helps manage storage but does not improve query performance directly. OPTIMIZE handles performance improvements. Schema changes are unrelated.


NEW QUESTION # 102
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Table1. Table1 stores customer data.
You need to implement a data lifecycle solution that meets the following requirements:
* Deleted data must be recoverable for 30 days to support audits.
* Data that is older than 30 days must be removed permanently.
* The solution must minimize administrative effort.
* The solution must be enforced consistently.
What should you configure?

Answer: A

Explanation:
Data retention for Table1 should be configured to retain logically deleted Delta data files for 30 days. The delta.deletedFileRetentionDuration table property determines how long deleted or replaced files remain available before they become eligible for permanent removal by VACUUM. Configuring this property with a
30-day interval allows deleted data to remain recoverable through Delta Lake time travel during the required audit period. After the retention interval expires, VACUUM can permanently remove the unreferenced files.
Predictive optimization can automatically run VACUUM for Unity Catalog managed tables, minimizing administrative effort and applying the configured retention behavior consistently. learn.microsoft.com Scheduled DELETE operations logically remove records but do not independently delete their historical data files. Data lineage tracks relationships and transformations between data objects; it does not enforce lifecycle rules. Azure Storage lifecycle management is unaware of Delta transaction-log dependencies and must not be used to delete Delta files directly. Therefore, configuring table-level data retention is the correct solution.


NEW QUESTION # 103
......

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