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Microsoft DP-750 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prepare and process data | 30-35% | - Ingest and transform data
|
| Topic 2: Deploy and maintain data pipelines and workloads | 30-35% | - Manage production workloads
|
| Topic 3: Secure and govern Unity Catalog objects | 15-20% | - Implement governance and security
|
| Topic 4: Set up and configure an Azure Databricks environment | 15-20% | - Create and configure Azure Databricks workspaces
|
Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:
You have an Azure Databricks workspace that contains a Delta table named Customer.
A job named Job1 performs frequent upserts into Customer.
You discover that Job1 has created many small Parquet files in Customer, and the small files are degrading query performance.
You need to improve query performance for the current data already stored in Customer. The solution must not affect the travel for the Customer table.
What should you do?
- A. Run the VACUUM command on the Customer table.
- B. Set the delta.deletedFileRetentionDuration table property to 1 day.
- C. Run the OPTIMIZE command on the Customer table.
- D. Set the delta.autoOptimize.optimizeWrite Apache Spark configuration to true.
Correct Answer: C 🗳️
Explanation: Only visible for RealVCE members. You can sign-up / login (it's free).
You have an Azure Databricks workspace that contains the objects shown in the following table.
Name | Type
Catalog1 | Catalog
Schema1 | Schema
Sales1 | Table
Notebook1 | Notebook
Space1 | AI/BI Genie space
Users often use the following words to refer to a sale: transaction, event, order, and invoice.
You need to create a knowledge store. The solution must ensure that when the users use any of the words in Space1, Genie queries the Sales1 table. Any other Genie spaces must remain unaffected.
To which object should you add the instructions?
- A. Schema1
- B. Notebook1
- C. Space1
- D. Sales1
Correct Answer: C 🗳️
Explanation: Only visible for RealVCE members. You can sign-up / login (it's free).
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
You have an Azure Databricks workspace named Workspace1 that contains a lakehouse 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 Databricks access connector.
Does this meet the goal?
- A. Yes
- B. No
Correct Answer: B 🗳️
Explanation: Only visible for RealVCE members. You can sign-up / login (it's free).
You have an Azure Databricks workspace named Workspace! that uses a Git repository. The repository contains a Databricks notebook named Notebook1.
From the main branch, you create a feature branch named Branch! and commit changes to Notebooks Another user commits changes to Notebook1 in main.
When you attempt to merge Branch! into main, the merge fails due to conflicts.
You need to merge Branch! into the main branch. The solution must ensure that Notebook1 includes all the changes from both the branches.
What should you do?
- A. Apply the changes directly to the main branch.
- B. From Workspace1, clone the mam branch as a new repository.
- C. Apply the main branch changes to Branch! and resolve the conflicts.
- D. From Workspace1, clone Branch! as a new repository.
Correct Answer: C 🗳️
Explanation: Only visible for RealVCE members. You can sign-up / login (it's free).
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