A data analyst at an e-commerce company needs to process daily sales data. The data consists of approximately 50,000 records stored in a single CSV file, totaling about 20 MB. The analyst needs to perform aggregations and generate a summary report.
Which approach could the data analyst use in this situation?

• A. Use a local Python script with the pandas library to read and analyze the CSV file.
• B. Implement Apache Spark with a distributed cluster to process the data in parallel.
• C. Set up a Hadoop ecosystem with HDFS and MapReduce for distributed processing.
• D. Deploy a real-time streaming solution using Spark Streaming to process incoming data.

A data engineer wants to create a relational object by pulling data from two tables. The relational object does not need to be used by other data engineers in other sessions. In order to save on storage costs, the data engineer wants to avoid copying and storing physical data.
Which of the following relational objects should the data engineer create?

• A. Temporary view
• B. Database
• C. View
• D. Delta Table
• E. Spark SQL Table

A data analyst runs the following command:
INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers;
What is the result of running this command?

• A. The suppliers table now contains both the data it had before the command was run and the data from the new suppliers table, including any duplicate data.
• B. The suppliers table now contains both the data it had before the command was run and the data from the new suppliers table, and any duplicate data is deleted.
• C. The suppliers table now contains only the data from the new suppliers table.
• D. The suppliers table now contains the data from the new suppliers table, and the new suppliers table now contains the data from the suppliers table.
• E. The command fails because it is written incorrectly.

A data analysis team has noticed that their Databricks SQL queries are running too slowly when connected to their always-on SQL endpoint. They claim that this issue is present when many members of the team are running small queries simultaneously. They ask the data engineering team for help. The data engineering team notices that each of the team's queries uses the same SQL endpoint.
Which of the following approaches can the data engineering team use to improve the latency of the team's queries?

• A. They can increase the maximum bound of the SQL endpoint's scaling range.
• B. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to
  "Reliability Optimized."
• C. They can turn on the Serverless feature for the SQL endpoint.
• D. They can turn on the Auto Stop feature for the SQL endpoint.
• E. They can increase the cluster size of the SQL endpoint.

A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.
In which location can one review the timeline for cluster resizing events?

• A. Workspace audit logs
• B. Driver's log file
• C. Ganglia
• D. Executor's log file
• E. Cluster Event Log