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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

1. 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.
- Prevent an update to Table1 from being committed when data that
violates rule1 is detected.
Which statement should you execute?

A) @dlt.expect_all_or_drop({"rule1": "amount > 0"})
B) @dlt.expect("rule1", "amount > 0")
C) @dlt.expect_or_drop("rule1", "amount > 0")
D) @dlt.expect_or_fail("rule1", "amount > 0")

2. You have an Azure Databricks workspace.
You are creating a Lakeflow Spark Declarative Pipelines (SDP) pipeline that scales automatically.
You need to configure compute for the pipeline. The solution must minimize operational costs and effort.
What should you use?

A) an all-purpose cluster that uses autoscaling
B) the existing SQL warehouse
C) a job cluster that uses autoscaling
D) a single-node, all-purpose cluster

3. 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 Table1.
- Invalid records must be preserved for review.
- Minimize development effort.
What should you do?

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

4. 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 curated 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?

A) a Databricks notebook triggered by a scheduled job
B) a Lakeflow Spark Declarative Pipelines (SDP) pipeline
C) an Azure Data Factory pipeline that uses data flows
D) an Apache Spark Structured Streaming job

5. Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
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

Solutions: