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Microsoft DP-750 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Set up and configure an Azure Databricks environment | 15–20% | - Select and configure compute resources
|
| Topic 2: Deploy and maintain data pipelines and workloads | 30–35% | - Monitor, troubleshoot, and maintain workloads
|
| Topic 3: Prepare and process data | 30–35% | - Ingest and transform data
|
| Topic 4: Secure and govern Unity Catalog objects | 15–20% | - Implement data governance and security
|
Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:
You have an Azure Databricks workspace that is attached to a Unity Catalog metastore named metastore1.
Metastore1 contains a catalog named catalog 1.
You need to create a new schema named schema2 that meets the following requirements:
* Is contained in catalog1
* Uses abfss://containergstorageaccount.dfs.core.windows.net/data as the Managed location Which SQL statement should you execute?
- A. CREATE SCHEMA catalog1.schema2
MANAGED LOCATION ' abfss://[email protected]/data ' ; - B. CREATE SCHEMA catalog1.schema2
LOCATION ' abfss://[email protected]/data ' ; - C. CREATE CATALOG schema2
MANAGED LOCATION ' abfss://[email protected]/data ' ; - D. CREATE SCHEMA catalog1.schema2
WITH DBPROPERTIES (LOCATION= ' abfss://[email protected]/data
' );
Correct Answer: A 🗳️
Explanation: Only visible for Prep4King members. You can sign-up / login (it's free).
You have an Azure Databricks workspace that contains a Delta table named Table 1. Table 1 has accumulated obsolete files.
You need to reduce storage costs. The solution must preserve 30 days of time travel history. Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. Run the vacuum command on Table1.
- B. Set the delta. logRetentionDuration table property to 30 days.
- C. Reduce the deleted file retention period to one day.
- D. Set the delta.deletedFileRetentionDuration table property to 10 days
- E. Run the OPTIMIZE command on Table1
Correct Answer: A,D 🗳️
Explanation: Only visible for Prep4King members. You can sign-up / login (it's free).
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that writes numerical data to a table named Table1 by using a data quality validation rule named rule1.
You need to modify rule1 to meet the following requirements:
* Ensure that amount is always greater than 0.
* Fail the pipeline update for Table1 when data that violates rule1 is detected.
Which statement should you execute?
- A. @dlt.expect( " rule1 " , " amount > 0 " )
- B. @dlt.expect_all_or_drop({ " rule1 " : " amount > 0 " })
- C. @dlt.expect_or_drop( " rule1 " , " amount > 0 " )
- D. @dlt.expect_or_fail( " rule1 " , " amount > 0 " )
Correct Answer: D 🗳️
Explanation: Only visible for Prep4King 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 Payments.
Payments stores transaction data and contains a column named payment_amount of the Decimal data type.
You must enforce the following business rule:
payment_amount must be between 0 and 10,000, inclusive
You need to ensure that records that violate the rule are rejected when data is written to the Payments table.
What should you do?
- A. Configure row-level security (RLS) to hide records that have invalid values.
- B. Create a table update trigger.
- C. Add a CHECK constraint to payment_amount to enforce the allowed range.
- D. Run SELECT statements to filter out records that have invalid values in payment_amount.
Correct Answer: C 🗳️
Explanation: Only visible for Prep4King 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 Sales. Sales stores transaction data and contains the following columns:
* transactionjd (string)
* transaction date (date)
* amount (decimal)
You need to implement the following data quality requirements by using table-level data quality enforcement:
* amount must be greater than 0.
* transaction id must never be null.
* Invalid records must be rejected when data is written to the Sales table.
What should you do?
- A. Create a view that filters out rows where transactionjd is null or amount is less than or equal to 0.
- B. Add a not null constraint to transactionjd and a check constraint to amount.
- C. Configure row-level security (RLS) where transactionjd is null or amount is less than or equal to 0.
- D. Use a select statement with where conditions to validate the data before querying.
Correct Answer: B 🗳️
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