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Microsoft DP-750 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Deploy and maintain data pipelines and workloads30–35%- Build and orchestrate pipelines
  • 1. Design and implement Lakeflow Spark Declarative Pipelines
  • 2. Configure Lakeflow Jobs: schedules, triggers, alerts, retries
  • 3. Implement CI/CD with Git, Databricks Asset Bundles, CLI, and APIs
- Monitor, troubleshoot, and maintain workloads
  • 1. Monitor performance, logs, and execution metrics
  • 2. Troubleshoot failures, repair and restart jobs
  • 3. Apply SDLC practices and version control
Topic 2: 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. 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
Topic 3: 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
Topic 4: Prepare and process data30–35%- Optimize and manage data storage
  • 1. Handle structured, semi-structured, and unstructured data
  • 2. Optimize Delta tables: partitioning, Z-ordering, vacuum, optimize
  • 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

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

1. You have an Azure Databricks workspace named Workspace1 that contains a cluster named Cluster1.
You need to enable a user named User1 to install a Maven package on all the nodes of Cluster1. The solution must follow the principle of least privilege.
What should you do?

A) Grant User1 the CAN RESTART permission for Cluster1.
B) Grant User1 the CAN MANAGE permission for Cluster1.
C) Assign User1 the Contributor role for the resource group that contains Workspace1.
D) Assign User1 the workspace admins role for Workspace1.


2. You have an Azure Databricks workspace.
Users report that a Databricks notebook that runs each day takes longer than expected to run.
When reading the Directed Acyclic Graph (DAG), you discover the following issues concerning the Apache Spark stage:
* Most tasks in the stage finish quickly.
* A few tasks in the stage run more slowly.
* The CPU is underutilized at the end of the stage.
* The slow tasks process many more input records.
* The stage is blocked while it waits for the few slow tasks.
What is the root cause of the issues?

A) shuffling
B) spilling
C) skewing
D) caching


3. You have an Azure Databricks workspace.
You have an Azure key vault named kv-secure that stores a secret named storageKey. The value of storageKey is managed and updated by the cloud security team at your company.
You need to enable a Databricks notebook named Notebook 1 to retrieve the value of storageKey securely at runtime. The solution must follow the principle of least privilege and always retrieve the latest value.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.


4. You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Payments.
Payments stores transaction data and contains a column named payment_amount of the Decimal data type.
You must enforce the following business rule:
payment_amount must be between 0 and 10,000, inclusive
You need to ensure that records that violate the rule are rejected when data is written to the Payments table.
What should you do?

A) Run SELECT statements to filter out records that have invalid values in payment_amount.
B) Add a CHECK constraint to payment_amount to enforce the allowed range.
C) Create a table update trigger.
D) Configure row-level security (RLS) to hide records that have invalid values.


5. You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two Delta tables named Table1 and Table2 of the same data type.
Table1 contains a column named Columnl. Table2 contains a column named Column2. You run the following query.
SELECT Column1
FROM Table1
GROUP BY Column1
HAVING COUNT( " ) > 1
INTERSECT
SELECT C0lumn2
FROM Table2
GROUP BY Column2
HAVING COUNT( ' ) > 1;
What occurs when you run the query?

A) Values appear in Table more than once.
B) Values appear in Table2 but NOT Table1.
C) Values appear in both tables more than once.
D) Values appear in either table more than once.


Solutions:

Question # 1
Answer: B
Question # 2
Answer: C
Question # 3
Answer: Only visible for members
Question # 4
Answer: B
Question # 5
Answer: C

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