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Last Updated: Oct 14, 2026
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Microsoft DP-750 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Configure and manage Azure Databricks environments | 15-20% | - Workspace and compute configuration
|
| Prepare and process data | 30-35% | - Data ingestion
|
| Secure and govern data using Unity Catalog | 15-20% | - Data governance fundamentals
|
| Deploy and manage data pipelines and workloads | 30-35% | - Pipeline design and orchestration
|
Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:
You need to complete the PySpark code for the Spark Structured Streaming pipelines. The solution must meet the data ingestion and processing requirements.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
The solution requires spark.readStream with format( ' cloudFiles ' ) for Auto Loader, paired with .writeStream using mergeSchema=true and a checkpointLocation.
Auto Loader ' s cloudFiles source incrementally processes new JSON files without rescanning the entire directory. The mergeSchema option handles schema drift - when sensors add new fields, the target Delta table schema expands automatically instead of throwing a parse error. This directly addresses Contoso ' s requirement to ' support schema drift. ' The checkpointLocation is what gives the pipeline its resilience. Databricks writes the stream ' s committed offset and schema state to that path. If the cluster restarts, the engine reads the checkpoint and picks up exactly where it left off - no events are reprocessed, satisfying ' exactly-once semantics ' and ' resume processing after failures without reprocessing the data. ' Without a checkpoint, the stream would restart from the beginning on every cluster bounce, which is precisely the problem Contoso is trying to eliminate.
Reference: https://learn.microsoft.com/en-us/azure/databricks/ingestion/auto-loader/schema
You have an Azure Databticks workspace that contains an all-purpose compute cluster named Cluster1.
Cluser1 is used for
interactive development.
You need to configure Cluster1 to meet the following requirements:
* Automatically add and remove worker nodes based on workload demand
* Automatically shut down when the cluster has been idle for a specific period.
What should you configure for each requirement? To answer, drag the appropriate options to the correct requirements. Each option 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.
Correct Answer:
Explanation:
Two separate cluster settings address the two requirements:
Autoscaling handles the first requirement - dynamically adding workers when the workload is heavy and removing them when it lightens. You set a minimum and maximum node count, and Databricks adjusts the cluster size between those bounds based on task queue depth.
Auto-termination handles the second - the cluster shuts itself down after a configurable idle period (e.g., 30 minutes with no active queries), preventing wasted spend on a development cluster left running overnight.
These two settings are independent and complementary: autoscaling manages horizontal elasticity during active use, while auto-termination manages complete shutdown during inactivity. Both are configured in the cluster creation UI under the Compute section.
Reference: https://learn.microsoft.com/en-us/azure/databricks/compute/configure#autoscaling
You manage Declarative Automation Bundles by using the Databricks CLI.
You run the following command in a terminal window.
databricks bundle init
What occurs when you run the command?
- A. The bundle configuration is validated.
- B. Jobs or pipelines defined in the bundle are run.
- C. A new Declarative Automation Bundles project structure is created.
- D. Bundle-defined resources are deployed to the Databricks workspace.
Correct Answer: C 🗳️
Explanation: Only visible for Exam-Killer members. You can sign-up / login (it's free).
You have an Azure Databricks account that contains workspaces enabled for Unity Catalog.
You need to implement audit logging to meet the following requirements:
* Capture audit logs for all the workspaces in the account.
* Retain the audit logs for 90 days.
* Minimize storage and ingestion costs.
The logs will be reviewed only during security investigations and will NOT be queried regularly.
To where should you send the audit logs?
- A. Azure Event Hubs
- B. Azure Monitor metrics
- C. Log Analytics
- D. an Azure Storage account
Correct Answer: D 🗳️
Explanation: Only visible for Exam-Killer 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. shuffling
- B. spilling
- C. skewing
- D. caching
Correct Answer: C 🗳️
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