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Microsoft DP-750 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Deploy and maintain data pipelines and workloads | 30-35% | - Manage production workloads
|
| Set up and configure an Azure Databricks environment | 15-20% | - Create and configure Azure Databricks workspaces
|
| Prepare and process data | 30-35% | - Ingest and transform data
|
| Secure and govern Unity Catalog objects | 15-20% | - Implement governance and security
|
Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:
You have an Azure Databricks workspace that uses Unity Catalog.
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that ingests data into a managed Delta table named Table1. Table! is used for analytics.
New columns are added to the source data, causing pipeline failures during writes to Table!
You need to prevent the pipeline failures. The solution must ensure that schema changes are detected and handled.
What should you do?
- A. Create a separate table for each schema version.
- B. Use row filters to exclude records that have new columns.
- C. Enable schema evolution.
- D. Disable schema enforcement for Table1.
Correct Answer: C 🗳️
Explanation: Only visible for Prep4sureExam members. You can sign-up / login (it's free).
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that writes numerical data to a table named Table1 by using a data quality validation rule named rule1.
You need to modify rule1 to meet the following requirements:
Ensure that amount is always greater than 0.
Prevent an update to Table1 from being committed when data that violates rule1 is detected.
Which statement should you execute?
- A. @dlt.expect_all_or_drop({ " rule1 " : " amount > 0 " })
- B. @dlt.expect( " rule1 " , " amount > 0 " )
- C. @dlt.expect_or_fail( " rule1 " , " amount > 0 " )
- D. @dlt.expect_or_drop( " rule1 " , " amount > 0 " )
Correct Answer: C 🗳️
Explanation: Only visible for Prep4sureExam members. You can sign-up / login (it's free).
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. spilling
- B. shuffling
- C. skewing
- D. caching
Correct Answer: C 🗳️
You need to recommend a compute type for the production ingestion workloads and BI workloads. The solution must meet the environment and compute requirements.
What should you recommend for each type of workload? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Production ingestion: Job compute
BI: Serverless SQL warehouse
Job compute is designed for automated production workloads executed through Lakeflow Jobs. Its lifecycle can be tied to the job run, providing workload isolation and avoiding the cost of maintaining an interactive all- purpose cluster continuously. It is therefore appropriate for scheduled ingestion and transformation processing. A serverless SQL warehouse is designed for BI and Databricks SQL workloads. It provides rapid startup, automatic infrastructure management, scaling, and optimized SQL-query execution for dashboards and reporting tools. All-purpose compute is intended primarily for interactive notebook development and exploration, while shared compute does not provide the same job-specific isolation or SQL-serving experience. Consequently, job compute should support production ingestion, and a serverless SQL warehouse should serve the BI workload.
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job1 processes raw data files stored in Azure Storage.
New files arrive at unpredictable intervals.
You need to ensure that Job1 starts automatically when new files arrive and does NOT consume compute resources when no data is available.
Which type of job trigger should you use?
- A. continuous
- B. file arrival
- C. manual
- D. scheduled
Correct Answer: B 🗳️
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