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Microsoft DP-750 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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
Topic 2: Prepare and process data30–35%- Optimize and manage data storage
  • 1. Handle structured, semi-structured, and unstructured data
  • 2. Implement lakehouse architecture and manage table versions
  • 3. Optimize Delta tables: partitioning, Z-ordering, vacuum, optimize
- Ingest and transform data
  • 1. Implement schema enforcement, schema drift, and slowly changing dimensions
  • 2. Transform using Spark SQL, PySpark, Scala, and Delta Lake
  • 3. Ingest batch and streaming data from multiple sources
Topic 3: 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
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. Configure access control: row-level, column-level, attribute-based security
  • 2. Manage catalogs, schemas, tables, views, and volumes
  • 3. Enforce data quality, lineage, and auditing

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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions (Q53-Q58):

NEW QUESTION # 53
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 # 54
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 # 55
Hotspot Question
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:


NEW QUESTION # 56
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: C

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 # 57
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two managed Delta tables named sales.schema1.table1 and sales.schema1.table2.
sales.schema1.table1 contains sales data from the current year.
sales.schema1 .table2 contains historical data.
You need to load all the rows from sales.schema1.table1 into sales.schema1.table2. The solution must preserve any existing data in sales.schema1.table2 and minimize processing effort.
Which command should you run?

Answer: C

Explanation:
To load all rows from one table into the other while preserving existing data and minimizing processing effort, you should use the SQL INSERT INTO statement.
Preserves Data: INSERT INTO appends new rows to the target table without modifying or deleting the existing data.
Lowest Processing Effort: It performs a direct data append at the storage level. Unlike MERGE INTO, it does not scan the target table for matches, saving significant compute time and costs.
Delta Lake Optimization: Because these are Delta tables, appending data simply writes new parquet files and commits them to the transaction log, making the operation fast and efficient.
Reference:
https://medium.com/@gema.correa/handling-schema-evolution-and-schema-compensation-in-databricks-lessons-from-the-field-7af8d915beef


NEW QUESTION # 58
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