Quiz 2026 DP-750: Implementing Data Engineering Solutions Using Azure Databricks Authoritative Latest Exam Cram

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Microsoft DP-750 Exam Syllabus Topics:

SectionWeightObjectives
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
Prepare and process data30–35%- 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
- Ingest and transform data
  • 1. Transform using Spark SQL, PySpark, Scala, and Delta Lake
  • 2. Ingest batch and streaming data from multiple sources
  • 3. Implement schema enforcement, schema drift, and slowly changing dimensions
Set up and configure an Azure Databricks environment15–20%- Integrate with Azure services
  • 1. Configure monitoring with Azure Monitor and diagnostic settings
  • 2. Connect to Azure Data Lake Storage, Azure Data Factory, Microsoft Entra ID
- Select and configure compute resources
  • 1. Configure cluster policies, instance pools, and libraries
  • 2. Manage workspace settings, permissions, and networking
  • 3. Choose compute types: serverless, job compute, SQL warehouse, classic compute
Deploy and maintain data pipelines and workloads30–35%- Monitor, troubleshoot, and maintain workloads
  • 1. Apply SDLC practices and version control
  • 2. Monitor performance, logs, and execution metrics
  • 3. Troubleshoot failures, repair and restart jobs
- 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

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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions (Q102-Q107):

NEW QUESTION # 102
You need to configure compute for the ingestion of telemetry data. The solution must meet the data ingestion and processing requirements.
What should you do?

Answer: D

Explanation:
The correct answer is A. Photon is Azure Databricks ' native vectorized query engine, written in C++, designed to accelerate data ingestion and SQL-heavy workloads significantly over the standard Spark JVM path. Enabling it on a job compute cluster directly addresses Contoso ' s requirement for ' fast and consistent performance for BI workloads ' and ' production ingestion workloads that can scale automatically during telemetry spikes. ' Photon integrates transparently - no code changes are needed - and pairs well with autoscaling job clusters to handle the bursty 40,000-sensor telemetry load.
Option B contradicts the isolation requirement: Contoso explicitly needs production and development separated, not merged onto shared compute. Option C with a fixed large node gives peak capacity at all times, driving up costs even during quiet periods. Option D disabling autoscaling is the opposite of what ' s needed
- telemetry spikes require elastic scaling, not a locked node count.
Reference: https://learn.microsoft.com/en-us/azure/databricks/compute/photon


NEW QUESTION # 103
You have an Azure Databricks workspace that contains multiple all-purpose clusters.
You discover that some clusters remain idle for long periods after users finish their work.
You need to reduce compute costs without affecting active workloads.
What should you do?

Answer: B

Explanation:
To reduce compute costs from idle clusters without impacting active workloads, you must configure Auto-Termination and use Cluster Policies.
Core Remedies
*-> Auto-Termination: Set a strict inactivity timeout (e.g., 20-30 minutes) on all-purpose clusters to automatically shut them down when idle.
Cluster Policies: Enforce maximum auto-termination limits across the workspace so users cannot disable or set excessively long idle timeouts.
Single User Access Mode: Use this mode where possible, as it tracks idleness more accurately than Shared mode by monitoring the specific user's activity.
Reference:
https://medium.com/@sujathamudadla1213/databricks-lakehouse-platform-describe-how- clusters-are-terminated-and-the-impact-of-terminating-a-b6236689fd2e


NEW QUESTION # 104
What improves join performance for small lookup tables?

Answer: A


NEW QUESTION # 105
You have an Azure Databricks workspace that contains multiple all-purpose clusters. You discover that some clusters remain idle for long periods after users finish their work. You need to reduce compute costs without affecting active workloads. What should you do?

Answer: B

Explanation:
The correct answer is D - configure automatic termination.
The problem is specific: clusters sit idle after users finish working but nobody manually shuts them down.
Automatic termination solves this directly - once a cluster has been idle for the configured period (no running commands, no attached notebooks with active execution), it shuts itself down. You eliminate the idle cost without any manual intervention and without affecting any workload that is actually running.
Option A (convert to job clusters) would force users off interactive all-purpose clusters, disrupting their development workflow. Option B (spot instances) reduces the hourly rate while a cluster is running but does nothing about the idle-time problem - a cheaper idle cluster is still waste. Option C (enable autoscaling) reduces the number of workers during light load but keeps the cluster alive at the minimum node count. It saves some cost but doesn't fully eliminate idle spend the way auto-termination does.
Reference: https://learn.microsoft.com/en-us/azure/databricks/compute/configure#auto-termination


NEW QUESTION # 106
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 # 107
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