Microsoft Implementing Data Engineering Solutions Using Azure Databricks : DP-750 exam dump

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Microsoft DP-750 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Secure and govern Unity Catalog objects	15–20%	- Manage data sharing and permissions 1. Grant and revoke permissions, manage groups and service principals 2. Set up external locations and storage credentials - 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
Topic 2: Set up and configure an Azure Databricks environment	15–20%	- 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. Manage workspace settings, permissions, and networking 3. Choose compute types: serverless, job compute, SQL warehouse, classic compute
Topic 3: Prepare and process data	30–35%	- Ingest and transform data 1. Implement schema enforcement, schema drift, and slowly changing dimensions 2. Ingest batch and streaming data from multiple sources 3. Transform using Spark SQL, PySpark, Scala, and Delta Lake - 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
Topic 4: Deploy and maintain data pipelines and workloads	30–35%	- Build and orchestrate pipelines 1. Implement CI/CD with Git, Databricks Asset Bundles, CLI, and APIs 2. Configure Lakeflow Jobs: schedules, triggers, alerts, retries 3. Design and implement Lakeflow Spark Declarative Pipelines - Monitor, troubleshoot, and maintain workloads 1. Troubleshoot failures, repair and restart jobs 2. Monitor performance, logs, and execution metrics 3. Apply SDLC practices and version control

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

1. You have an Azure Databricks workspace
You are creating a Lakeflow Spark Declarative Pipelines (SDP) pipeline that scales automatically. You need to configure compute for the pipeline. The solution must minimize operational costs and effort. What should you use?

A) a job cluster that uses autoscaling
B) a single-node, all-purpose cluster
C) an all-purpose cluster that uses autoscaling
D) the existing SQL warehouse

2. You have an Azure Databricks workspace.
You have a streaming table named sales_order that is populated by using a Lakeflow Spark Declarative Pipelines (SDP) pipeline.
You need to create a new streaming table named sales_order_by_city that summarizes sales by city and calculates the total sales per city.
How should you complete the SQL statement? To answer, drag the appropriate values to the correct targets.
Each value 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.

3. You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that writes records to a Delta table named Table1 by using a data quality rule named rule1 You need to meet the following requirements:
* Records that violate rule! must NOT be written to Table1. but the pipeline must continue processing valid records.
* Data engineers must be able to review expectation metrics by using minimal development effort.
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 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?

A) Restart Job!
B) Update the job parameters.
C) Disable and reenable the job schedule.
D) Repair the current job run.

5. You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job1 runs every hour.
Occasionally, Job1 takes longer than one hour to complete.
You need to configure the job scheduling behavior to meet the following requirements:
* Overlapping runs must be prevented to avoid data corruption.
* Scheduled runs must not be discarded when another run is already active.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Solutions:

Question # 1 Answer: A

Question # 2 Answer: Only visible for members

Question # 3 Answer: Only visible for members

Question # 4 Answer: D

Question # 5 Answer: Only visible for members