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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 Duration: | 100 minutes |
| Available Languages: | English |
| Related Certifications: | Microsoft Certified: Azure Data Engineer Associate (DP-203) Microsoft Certified: Fabric Data Engineer Associate (DP-700) |
| Passing Score: | 700 |
| Exam Format: | Interactive items, Scenario-based questions, Multiple choice, Case studies |
| Recommended Training: | DP-750 Training Course (DP-750T00) Microsoft Learn DP-750 Study Guide |
| Exam Registration: | Pearson VUE Exam Scheduling Official Microsoft Certification Page |
| 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 |
|---|---|---|
| Secure and govern data using Unity Catalog | 15-20% | - Access control and policies
|
| Deploy and manage data pipelines and workloads | 30-35% | - Lakehouse architecture operations
|
| Prepare and process data | 30-35% | - Data transformation and modeling
|
| Configure and manage Azure Databricks environments | 15-20% | - Security and authentication setup
|
Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:
You have an Azure Databricks workspace named Workspace1 that contains a takehouse and is enabled for Unity Catalog.
You have a connection to a Microsoft SQL Server database named DB1.
You need to expose the schemas and tables of DB1 to meet the following requirements:
* The schemas and tables can be queried in Databricks.
* The schemas and tables appear alongside other Unity Catalog objects.
* The data is NOT copied into Databricks-managed storage.
Solution: You create a new native catalog in Unity Catalog. Does this meet the goal?
- A. Yes
- B. No
Correct Answer: B 🗳️
Explanation: Only visible for Prep4King members. You can sign-up / login (it's free).
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. Repair the current job run.
- C. Disable and reenable the job schedule.
- D. Update the job parameters.
Correct Answer: B 🗳️
Explanation: Only visible for Prep4King members. You can sign-up / login (it's free).
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job! runs every hour.
Occasionally, the job run takes longer than one hour to complete. Overlapping runs must be prevented to avoid data corruption.
You need to configure the job scheduling behavior.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Two settings address the overlapping-run problem:
Concurrent Runs policy set to ' Skip ' (or ' Allow only one concurrent run ' ). When a new scheduled trigger fires while the previous run is still in progress, the new run is skipped rather than starting alongside the ongoing one. This prevents two runs from writing to the same tables at the same time - which is the data corruption risk the question highlights.
Cron-based schedule for the hourly trigger. A cron expression defines the regular execution cadence.
Combined with the concurrency setting, the job runs hourly but never overlaps.
An alternative to ' Skip ' is ' Wait ' (queue the new run), which ensures every scheduled run eventually executes - but for this scenario where overlapping is the primary concern, skipping the missed run is typically preferable to building up a queue of back-to-back executions.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/configure-jobs#concurrent-runs
You have an Azure Databricks job named Job1 that contains an ingestion task named Task1 and transformation task named Task2. You need to ensure that if Task1 fails, the task retries automatically, and Task2 is prevented from running How should you configure Job1? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Two task-level settings solve this:
Task1 retry policy: configure Task1 with a maximum number of retries and a retry interval. When Task1 fails, Lakeflow Jobs automatically re-runs it up to the retry limit without any manual intervention. This handles transient infrastructure failures transparently.
Task2 run condition set to ' All succeeded ' with Task1 as its dependency: this means Task2 only starts when Task1 has succeeded. If Task1 fails and exhausts all retries, Task2 remains blocked - it never runs on data from a failed upstream ingestion. The dependency is declared in Task2 ' s ' Depends on ' setting in the job configuration.
These two settings are independent and composable. Task1 ' s retry policy gives it multiple chances to recover. Task2 ' s dependency and run condition ensure the downstream transformation only runs on clean, successfully ingested data.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/configure-jobs#task-retries
You have an Azure Databricks workspace.
You have an Apache Spark Structured Streaming job named Job! that processes data continuously and fails periodically due to transient errors You need to ensure that Job! meets the following requirements
* Resumes processing from the point that Job1 failed
* Minimizes how long it takes to restart Job!
* Minimizes the costs to restart Job!
What should you do?
- A. Implement checkpointing.
- B. Decrease the retry interval.
- C. Increase the minimum number of nodes in the cluster
- D. Add an alert and manually restart Job1.
Correct Answer: A 🗳️
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