Microsoft Implementing Data Engineering Solutions Using Azure Databricks (DP-750) Free Practice Test
Question 1
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to recommend a pipeline that ingests files from cloud storage, performs cleansing and enrichment transformations, and writes created Delta tables for analytics. The solution must minimize development effort and provide built-in monitoring and automatic retries.
What should you include in the recommendation?
You need to recommend a pipeline that ingests files from cloud storage, performs cleansing and enrichment transformations, and writes created Delta tables for analytics. The solution must minimize development effort and provide built-in monitoring and automatic retries.
What should you include in the recommendation?
Correct Answer: A
Explanation: Only visible for TestSimulate members. You can sign-up / login (it's free).
Question 2
You need to deploy Databricks Asset Bundles to a development environment. The solution must support automated and repeatable deployments across environments.
What should you use?
What should you use?
Correct Answer: A
Explanation: Only visible for TestSimulate members. You can sign-up / login (it's free).
Question 3
You have an Apache Spark DataFrame named salesDF that contains the following columns:
* Product
* Region
* Sales
* Date
You need to create a pivot table that shows the total sales by product for each region.
How should you complete the PySpark code segment? To answer, drag the appropriate values to the correct targets. Each value 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.
* Product
* Region
* Sales
* Date
You need to create a pivot table that shows the total sales by product for each region.
How should you complete the PySpark code segment? To answer, drag the appropriate values to the correct targets. Each value 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.
Correct Answer:
Explanation:
* groupBy
* pivot
The groupBy( " Region " ) operation establishes one aggregation group for each geographical region in the DataFrame. The subsequent pivot( " Product " ) operation converts every distinct Product value into a separate output column. Finally, agg(F.sum( " Sales " )) calculates the total sales for every product within each region. The resulting DataFrame contains one row per region and one column for each product, with the corresponding aggregated sales value. cogroup combines grouped datasets and is not used for creating a conventional pivot table. coalesce controls the number of DataFrame partitions or replaces null values, depending on its context. The sum function is already provided inside the aggregation expression and therefore is not required in either blank.
Question 4
You need to deploy Declarative Automation Bundles to a development environment. The solution must support automated and repeatable deployments across environments.
What should you use?
What should you use?
Correct Answer: A
Explanation: Only visible for TestSimulate members. You can sign-up / login (it's free).
Question 5
You need to recommend a compute type for the production ingestion workloads and BI workloads. The solution must meet the environment and compute requirements.
What should you recommend for each type of workload? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
What should you recommend for each type of workload? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Production ingestion: Job compute
BI: Serverless SQL warehouse
Job compute is designed for automated production workloads executed through Lakeflow Jobs. Its lifecycle can be tied to the job run, providing workload isolation and avoiding the cost of maintaining an interactive all- purpose cluster continuously. It is therefore appropriate for scheduled ingestion and transformation processing. A serverless SQL warehouse is designed for BI and Databricks SQL workloads. It provides rapid startup, automatic infrastructure management, scaling, and optimized SQL-query execution for dashboards and reporting tools. All-purpose compute is intended primarily for interactive notebook development and exploration, while shared compute does not provide the same job-specific isolation or SQL-serving experience. Consequently, job compute should support production ingestion, and a serverless SQL warehouse should serve the BI workload.
Question 6
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to implement a data lifecycle and expiration solution that meets the following requirements
* Transaction logs and deleted data files that are older than 90 days must be removed from Delta tables to reclaim storage.
* All the tables must remain available for querying during the cleanup process.
* Administrative effort must be minimized
What should you do for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
You need to implement a data lifecycle and expiration solution that meets the following requirements
* Transaction logs and deleted data files that are older than 90 days must be removed from Delta tables to reclaim storage.
* All the tables must remain available for querying during the cleanup process.
* Administrative effort must be minimized
What should you do for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Two actions are needed to reclaim storage while keeping tables queryable:
Set delta.deletedFileRetentionDuration and delta.logRetentionDuration to 90 days on each table. These properties define the retention floor - VACUUM will not touch anything newer than this threshold, so no data needed for time travel within 90 days can be accidentally removed.
Run VACUUM on each table. VACUUM is the Delta Lake command that physically removes data files and transaction log entries older than the retention duration. Importantly, VACUUM runs as a background operation - it uses Delta Lake ' s MVCC (multi-version concurrency control) to ensure that concurrent reads against the table continue uninterrupted while cleanup happens. Tables are fully available throughout.
OPTIMIZE compacts small files for query performance but doesn ' t delete anything. Manually deleting files outside the Delta protocol would corrupt the table.
Reference: https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/delta-vacuum
Question 7
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two Delta tables named Table1 and Table2 of the same data type.
Table1 contains a column named Columnl. Table2 contains a column named Column2. You run the following query.
SELECT Column1
FROM Table1
GROUP BY Column1
HAVING COUNT( " ) > 1
INTERSECT
SELECT C0lumn2
FROM Table2
GROUP BY Column2
HAVING COUNT( ' ) > 1;
What occurs when you run the query?
Table1 contains a column named Columnl. Table2 contains a column named Column2. You run the following query.
SELECT Column1
FROM Table1
GROUP BY Column1
HAVING COUNT( " ) > 1
INTERSECT
SELECT C0lumn2
FROM Table2
GROUP BY Column2
HAVING COUNT( ' ) > 1;
What occurs when you run the query?
Correct Answer: D
Explanation: Only visible for TestSimulate members. You can sign-up / login (it's free).
Question 8
You have an Azure Databricks job named Job1 that contains an ingestion task named Task1 and transformation task named Task2. You need to ensure that if Task1 fails, the task retries automatically, and Task2 is prevented from running How should you configure Job1? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Two task-level settings solve this:
Task1 retry policy: configure Task1 with a maximum number of retries and a retry interval. When Task1 fails, Lakeflow Jobs automatically re-runs it up to the retry limit without any manual intervention. This handles transient infrastructure failures transparently.
Task2 run condition set to ' All succeeded ' with Task1 as its dependency: this means Task2 only starts when Task1 has succeeded. If Task1 fails and exhausts all retries, Task2 remains blocked - it never runs on data from a failed upstream ingestion. The dependency is declared in Task2 ' s ' Depends on ' setting in the job configuration.
These two settings are independent and composable. Task1 ' s retry policy gives it multiple chances to recover. Task2 ' s dependency and run condition ensure the downstream transformation only runs on clean, successfully ingested data.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/configure-jobs#task-retries
Question 9
You manage Declarative Automation Bundles by using the Databricks CLI.
You run the following command in a terminal window.
databricks bundle init
What occurs when you run the command?
You run the following command in a terminal window.
databricks bundle init
What occurs when you run the command?
Correct Answer: B
Explanation: Only visible for TestSimulate members. You can sign-up / login (it's free).
Question 10
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You plan to create a job in Lakeflow Jobs named Job1 that:
* Ingests data from cloud storage
* Runs two independent transformation tasks
The transformation tasks must run only after the ingestion completes and must run in parallel.
You need to design the task logic for Job1.
What should you configure?
You plan to create a job in Lakeflow Jobs named Job1 that:
* Ingests data from cloud storage
* Runs two independent transformation tasks
The transformation tasks must run only after the ingestion completes and must run in parallel.
You need to design the task logic for Job1.
What should you configure?
Correct Answer: A
Explanation: Only visible for TestSimulate members. You can sign-up / login (it's free).