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Microsoft DP-750 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Prepare and process data | 30β35% | - Ingest and transform data
|
| Set up and configure an Azure Databricks environment | 15β20% | - Select and configure compute resources
|
| Deploy and maintain data pipelines and workloads | 30β35% | - Build and orchestrate pipelines
|
| Secure and govern Unity Catalog objects | 15β20% | - Implement data governance and security
|
Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Table1. Table1 stores customer data.
You need to implement a data retention solution that meets the following requirements:
Deleted data must be retained for 30 days to support audits.
Deleted data that is older than 30 days must be removed permanently.
The solution must minimize administrative effort.
Which two properties should you configure? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. delta.logRetentionDuration
- B. delta.timeUntilArchived
- C. delta.deletedFileRetentionDuration
- D. delta.autoOptimize.autoCompact
- E. delta.enableDeletionVectors
Correct Answer: A,C π³οΈ
Explanation: Only visible for ExamCost members. You can sign-up / login (it's free).
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have 500 GB of sales data stored as multiple CSV files in cloud storage.
You plan to load the data into a Delta table.
You need to ingest the bulk data by using a solution that meets the following requirements:
* Minimize how long it takes to implement the solution.
* Minimize the amount of custom code required.
What should you use?
- A. Auto Loader
- B. Apache Spark read APIs
- C. file upload
- D. COPY INTO
Correct Answer: D π³οΈ
Explanation: Only visible for ExamCost members. You can sign-up / login (it's free).
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to implement a daily batch data process that requires complex and highly customized Python transformations. The solution must minimize additional complexity.
What should you include in the solution?
- A. a Lakeflow Spark Declarative Pipelines (SDP) pipeline
- B. a Databricks notebook triggered by a job
- C. a continuous job in Lakeflow Jobs
- D. an Azure Data Factory pipeline that has data flows
Correct Answer: B π³οΈ
Explanation: Only visible for ExamCost members. You can sign-up / login (it's free).
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Table1.
Table1 stores customer profile data.
Business users must analyze how customer profile records change over time. They must also be able to query earlier versions of the table.
You need to implement a solution that:
* Maintains persistent historical versions of customer profile records for long-term analysis.
* Allows users to query earlier versions of the Delta table.
* Minimizes maintenance effort.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
To record historical changes: Implement a Type 2 slowly changing dimension (SCD).
To support temporal analysis: Use Delta Lake time travel.
A Type 2 slowly changing dimension preserves customer-profile history by inserting a new record whenever a tracked attribute changes instead of overwriting the existing record. Effective dates, expiration dates, version values, or current-record indicators can identify which version applied during a particular period. This provides persistent business history for long-term analysis. Delta Lake time travel supports temporal analysis of the physical table by allowing users to query an earlier version with VERSION AS OF or TIMESTAMP AS OF. Time travel is useful for auditing and reproducing previous results, but its availability depends on retained Delta log entries and data files. Therefore, it should not replace a Type 2 SCD for permanent customer history. Together, the two features satisfy the historical-record and earlier-version requirements.
You have an Azure Databricks workspace.
You need to ingest streaming data from Azure Event Hubs by using Apache Spark Structured Streaming The solution must authenticate to Event Hubs and read the event payload.
How should you complete the PySpark code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Reading from Azure Event Hubs in Spark Structured Streaming requires three things:
An EventHubsConf object built with the Event Hubs connection string (eventhubs.connectionString). This object is then converted to a map with .toMap before being passed to Spark.
spark.readStream.format( ' eventhubs ' ).options(**ehConf).load() to create the streaming DataFrame. The ' eventhubs ' format is provided by the azure-eventhubs-spark connector library.
A cast( ' string ' ) on the body column to decode the binary payload. Event Hubs delivers messages with the raw event bytes in a column called body - without the cast, you get binary data rather than the readable JSON or text payload.
This is the standard, documented integration pattern for connecting Azure Databricks to Event Hubs with Structured Streaming, providing the checkpoint-based exactly-once semantics required by the Contoso telemetry pipeline.
Reference: https://learn.microsoft.com/en-us/azure/databricks/connect/storage/events/eventhubs
