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Cloudera CDP Data Engineer - Certification Sample Questions:

1. You're working with a complex data pipeline involving both Spark and Hive operations. How can you ensure data consistency and avoid data corruption across different stages?

A) Manually manage data consistency through custom code
B) Rely solely on Spark's checkpointing capabilities
C) Leverage ACID transactions in both Spark and Hive
D) Use separate clusters for Spark and Hive processing

2. You're given a DataFrame containing information about flights, including columns "origin", "destination", and "delay_minutes". How can you find the top 5 origin airports with the most delayed flights on average?

A) Use Spark's machine learning library (MLIiB. for ranking and classification
B) Leverage Spark SQL's RANK function along with windowing to identify top 5 origins
C) Use groupBy and avg on "delay_minutes", then sort by the average in descending order and limit to top 5
D) Implement a custom function to calculate average delays for each origin and then sort and filter

3. You have a DataFrame containing sales data with columns "product_id", "customer id", and "amount". How can you efficiently calculate the total sales per customer?

A) Use a loop to iterate through the DataFrame and accumulate the sales for each customer
B) Utilize spark SQL's GROUP BY and SUM functions
C) Leverage Spark's machine learning library (MLIiB. for aggregation
D) Implement a custom function to group and sum the sales

4. What impact does the Spark configuration parameter spark.network.timeout have on Spark streaming applications?

A) It specifies the timeout for network operations such as data shuffle and broadcast.
B) It determines the timeout for connections between executors and the Spark driver, impacting task retries and executor loss detection.
C) It sets the timeout for Akka-based network operations, crucial for Spark streaming's stability.
D) It controls the maximum time a Spark streaming application will wait for data before timing out.

5. How can you leverage the Cloudera Data Engineering service to monitor the performance and health of your data pipelines?

A) Manually review log files generated by individual pipeline steps.
B) Integrate the service with a separate external monitoring tool.
C) Utilize the built-in monitoring capabilities of the Cloudera Data Engineering service, including dashboards and alerts.
D) Utilize custom scripting to collect and analyze pipeline metrics.

Solutions: