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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

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

2. Which tool is best for continuous ingestion of files landing in Azure Data Lake?

A) Azure Data Factory Copy Activity
B) Azure Logic Apps
C) Databricks Jobs
D) Auto Loader

3. Hotspot Question
You have an Azure Databricks workspace.
You need to ingest streaming data from Azure Event Hubs by using Apache Spark Structured Streaming. The solution must authenticate to Event Hubs and read the event payload.
How should you complete the PySpark code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

4. What ensures failure recovery in Databricks Structured Streaming?

A) Partition pruning
B) Broadcast joins
C) Checkpointing
D) Auto scaling

5. You have an Azure Databricks workspace that is enabled for Unity Catalog.
You plan to ingest data from CSV files stored in Azure Data Lake Storage Gen2. New rows are appended frequently.
You need to implement a data ingestion solution that meets the following requirements:
- New data must be available in near-real-time (NRT).
- The data must be stored in managed Delta tables.
- The solution must minimize custom code and maintenance effort.
What should you include in the solution?

A) an external table that references the CSV files
B) an Azure Data Factory pipeline
C) Auto Loader
D) scheduled Apache Spark batch jobs

Solutions: