Microsoft Implementing Data Engineering Solutions Using Azure Databricks : DP-750

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Microsoft DP-750 Exam Syllabus Topics:

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

1. 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?

A) a single task that performs ingestion and transformations sequentially
B) independent tasks with no defined dependencies
C) One ingestion task with two parallel downstream transformation tasks
D) two ingestion tasks, each followed by a transformation task

2. You have a Lakeflow Spark Declarative Pipelines {SDP) pipeline in Azure Databricks. The pipeline ingests transaction data into a table named Table1.
You need to ensure that in the event of an invalid record, the pipeline continues to run. The solution must meet the following requirements:
* Invalid records must NOT be written to Table 1.
* Invalid records must be preserved for review.
* Minimize development effort
What should you do?

A) Implement advanced logic to quarantine the invalid records.
B) Add a check constraint to Table1
C) Define a pipeline expectation.
D) Run were clauses in downstream queries to filter out invalid records.

3. 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) a resources node in a databricks.yml file
C) separate databricks.yml files for each environment
D) a targets node in a databricks.yml file

4. 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-fillna(0, subset=[ ' order_amount ' ])
Does this meet the goal?

A) No
B) Yes

5. 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?

A) Values appear in either table more than once.
B) Values appear in Table2 but NOT Table1.
C) Values appear in both tables more than once.
D) Values appear in Table more than once.

Solutions: