A data engineer is tasked with building a nightly batch ETL pipeline that processes very large volumes of raw JSON logs from a data lake into Delta tables for reporting. The data arrives in bulk once per day, and the pipeline takes several hours to complete. Cost efficiency is important, but performance and reliability of completing the pipeline are the highest priorities.
Which type of Databricks cluster should the data engineer configure?

• A.A lightweight single-node cluster with low worker node count to reduce costs.
• B.A high-concurrency cluster designed for interactive SQL workloads.
• C.An all-purpose cluster always kept running to ensure low-latency job startup times.
• D.A job cluster configured to autoscale across multiple workers during the pipeline run.

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A data engineer is writing Spark code to group sales data by region and calculate total revenue for each region. Which Spark DataFrame transformation performs grouping operations?

• A.orderBy
• B.select
• C.filter
• D.groupBy

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Which of the following data workloads will utilize a Gold table as its source?

• A.A job that enriches data by parsing its timestamps into a human-readable format
• B.A job that aggregates uncleaned data to create standard summary statistics
• C.A job that cleans data by removing malformatted records
• D.A job that queries aggregated data designed to feed into a dashboard
• E.A job that ingests raw data from a streaming source into the Lakehouse

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A Delta Live Table pipeline includes two datasets defined using streaming live table. Three datasets are defined against Delta Lake table sources using live table.
The table is configured to run in Production mode using the Continuous Pipeline Mode.
What is the expected outcome after clicking Start to update the pipeline assuming previously unprocessed data exists and all definitions are valid?

• A.All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
• B.All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
• C.All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
• D.All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped.

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Which of the following describes the type of workloads that are always compatible with Auto Loader?

• A.Dashboard workloads
• B.Streaming workloads
• C.Machine learning workloads
• D.Serverless workloads
• E.Batch workloads

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