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Microsoft DP-750 Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Implementing Data Engineering Solutions Using Azure Databricks |
| Exam Number: | DP-750 |
| Exam Format: | Interactive items, Scenario-based questions, Case studies, Multiple choice |
| Passing Score: | 700 |
| Exam Duration: | 100 minutes |
| Related Certifications: | Microsoft Certified: Fabric Data Engineer Associate (DP-700) Microsoft Certified: Azure Data Engineer Associate (DP-203) |
| Available Languages: | English |
| Recommended Training: | DP-750 Training Course (DP-750T00) Microsoft Learn DP-750 Study Guide |
| Exam Registration: | Official Microsoft Certification Page Pearson VUE Exam Scheduling |
| Sample Questions: | Microsoft DP-750 Sample Questions |
| Exam Way: | Online proctored exam via Pearson VUE |
| Pre Condition: | Recommended experience with Azure Databricks, SQL, Python, and basic Azure services (Entra ID, Data Factory, Key Vault, Azure Storage). |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/implementing-data-engineering-solutions-using-azure-databricks/ |
Microsoft DP-750 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Prepare and process data | 30-35% | - Data quality and validation
|
| Secure and govern data using Unity Catalog | 15-20% | - Access control and policies
|
| Configure and manage Azure Databricks environments | 15-20% | - Workspace and compute configuration
|
| Deploy and manage data pipelines and workloads | 30-35% | - Pipeline design and orchestration
|
Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:
Question 1
You have an Azure Databricks workspace that is enabled for Unity Catalog You have an Apache Spark Structured Streaming job that writes data to a Delta table.
After the cluster restarts, the streaming job reprocesses previously ingested data You need to prevent the streaming job from reprocessing the data after the cluster restarts.
What should you do?
A. Enable change data feed (CDF) for the target table.
B. Configure a checkpoint location for the streaming query.
C. Increase the trigger interval of the streaming query.
D. Configure a watermark for the streaming query.
Question 2
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. scheduled
D. manual
Question 3
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job! 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. scheduled
D. manual
Question 4
You need to develop the task logic for a new job in Lakeflow Jobs that processes telemetry data.
Each task must contain only the appropriate logic for its step in the pipeline. The solution must support the planned changes and meet the data ingestion and processing requirements.
What should you do?
A. Create separate tasks for ingestion, cleansing, and curation.
B. Use a single SQL task that performs ingestion, cleansing, and curation by running merge commands.
C. Use a single Databricks notebook task that performs ingestion, cleansing, and curation in one script.
D. Create three tasks that each contains the identical logic and use task retries.
Question 5
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. skewing
C. caching
D. spilling
Solutions:
| Question 1 Answer: B | Question 2 Answer: B | Question 3 Answer: B | Question 4 Answer: A | Question 5 Answer: B |
By Jim
