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Microsoft DP-750 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Secure and govern Unity Catalog objects | 15-20% | - Implement governance and security
|
| Topic 2: Prepare and process data | 30-35% | - Ingest and transform data
|
| Topic 3: Deploy and maintain data pipelines and workloads | 30-35% | - Manage production workloads
|
| 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 use Databricks Asset Bundles to manage two jobs and an app.
You need to deploy the bundle to development and production environments. The solution must meet the following requirements
* Deploy the app to both environments.
* Deploy only one job to development.
* Minimize administrative effort.
What should you use?
- A. a variables node in a databricks.yml file
- B. separate databricks.yml files for each environment
- C. a resources node in a databricks.yml file
- D. a targets node in a databricks.yml file
Correct Answer: D 🗳️
Explanation: Only visible for TestPassed members. You can sign-up / login (it's free).
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named Catalog 1. Catalog 1 contains a table named Transactions. Transactions contains the following columns:
* transaction_id
* customet_name
* email address
* credit_card_number
* transaction_amount
You need to ensure that business analysts can query all the tows in the Transactions table. The solution must meet the following requirements:
* Prevent the analysts from seeing the full values in the email_address and credit_catd_number columns.
* Ensure that the analysts can see only the values after the @ character in each email address.
* Ensure that the analysts can see only the last four digits of each credit card number.
* Enable the analysts to query the table without errors.
* Follow the principle of least privilege.
What should you do?
- A. Grant the analysts the select permission for the Transactions table and apply column-level encryption
- B. Grant the analysts the select permission for columns that do NOT contain sensitive data.
- C. Grant the analysts the SELECT permission for the Transactions table and implement row-level filters.
- D. Grant the analysts the select permission for the Transactions table and apply column masks to email_address and credit_card_number
Correct Answer: D 🗳️
Explanation: Only visible for TestPassed members. You can sign-up / login (it's free).
You have an Azure Databricks workspace that uses Unity Catalog.
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that ingests data into a managed Delta table named Table1. Table1 is used for analytics.
New columns are added to the source data, causing pipeline failures during writes to Table1.
You need to prevent the pipeline failures. The solution must ensure that schema changes are detected and handled.
What should you do?
- A. Create a separate table for each schema version.
- B. Disable schema enforcement for Table1.
- C. Enable schema evolution.
- D. Use row filters to exclude records that have new columns.
Correct Answer: C 🗳️
Explanation: Only visible for TestPassed members. You can sign-up / login (it's free).
You have an Azure Databricks workspace.
A vendor sends order events to an Azure event hub continuously throughout the day. The events must be ingested with low latency into an orders Delta table as they are received.
You create a job in Lakeflow Jobs named Job1 to ingest data from the event hub stream.
You need to configure a trigger for Job1.
Which type of trigger should you use?
- A. Table update
- B. Continuous
- C. Scheduled
- D. File arrival
Correct Answer: B 🗳️
Explanation: Only visible for TestPassed members. You can sign-up / login (it's free).
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Orders.
You load the Orders table into an Apache Spark DataFrame named df.
You need to create a DataFrame that excludes rows where the order amount is null.
Solution: You run the following expression.
df.filter(df.order_amount != None)
Does this meet the goal?
- A. Yes
- B. No
Correct Answer: B 🗳️
Explanation: Only visible for TestPassed members. You can sign-up / login (it's free).
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