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

About Microsoft Implementing Data Engineering Solutions Using Azure Databricks : DP-750 Exam

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Microsoft DP-750 Exam Syllabus Topics:

Section	Weight	Objectives
Set up and configure an Azure Databricks environment	15-20%	- Create and configure Azure Databricks workspaces 1. Configure compute resources and clusters 2. Configure networking and connectivity 3. Manage Databricks runtimes 4. Configure workspace settings
Deploy and maintain data pipelines and workloads	30-35%	- Manage production workloads 1. Integrate Git-based development workflows 2. Monitor and troubleshoot pipelines 3. Implement CI/CD processes 4. Maintain production data engineering solutions 5. Create and manage Lakeflow Jobs 6. Deploy workloads using Databricks Asset Bundles 7. Optimize workload performance and reliability
Prepare and process data	30-35%	- Ingest and transform data 1. Model and partition data 2. Use Auto Loader and batch ingestion 3. Optimize storage and table performance 4. Implement Delta Lake tables 5. Apply medallion architecture patterns 6. Transform data using SQL and Python 7. Implement data quality controls 8. Implement streaming data processing
Secure and govern Unity Catalog objects	15-20%	- Implement governance and security 1. Implement data-sharing capabilities 2. Manage data lineage and auditing 3. Configure Unity Catalog 4. Implement access control and permissions 5. Manage catalogs, schemas, and tables

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

Question #1

Which SCD type should you use to support the planned data modeling changes? To answer, drag the appropriate types to the correct issues. Each type 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.

Answer:

Explanation:
The correct mapping is SCD Type 1 for equipment metadata and SCD Type 2 for IoT sensor ownership history.
SCD Type 1 overwrites the existing record whenever an attribute changes - no history is kept. Contoso ' s requirement for equipment metadata (name, manufacturer, model, commissioning date) states ' historical values are NOT required, ' which is the textbook definition of Type 1. A MERGE INTO with WHEN MATCHED THEN UPDATE handles this cleanly in Delta Lake.
SCD Type 2 creates a new row for each change, preserving the full history through effective-date or version columns. Contoso requires that ' analysts must track the full history of ownership ' as sensors change hands over time - that full audit trail is only possible with Type 2. Type 3 (keeping just the previous value in an extra column) would lose earlier ownership records, so it doesn ' t satisfy the ' full history ' requirement.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta/merge

Question #2

You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1. Job1 contains multiple tasks.
Failures of non-critical tasks must be logged but must NOT trigger notifications. Notifications must be triggered only when critical tasks have failed, and Job1 has completed You need to configure the job alerting behavior.
What should trigger a notification?

A. task success
B. job success
C. a task failure
D. a job failure

Answer: D

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Question #3

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.

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

Question #4

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

Answer: B

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Question #5

You have an Azure Databricks workspace named Workspace1. You create a compute cluster named Cluser1 that will be used to ingest data.
You need to install the required libraries on Cluster 1. The solution must use Unity Catalog for access control.
What should you do?

A. Install the libraries on Cluster1 and manually restart the cluster.
B. Install the libraries by using pip3.
C. Upload the libraries to Workspace1 and install the libraries on Cluster1.
D. Create a custom dependency management script and run the script from a Databricks notebook.

Answer: B

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

About Microsoft Implementing Data Engineering Solutions Using Azure Databricks : DP-750 Exam

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