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Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Data Processing and Transformation | - SQL analytics engines
|
| Data Governance and Security | - Security frameworks
|
| Data Storage and Modeling | - Data modeling
|
| Data Ingestion and Integration | - Batch and streaming ingestion
|
| Platform Operations | - Cluster and workload management
|
Cloudera CDP Data Engineer - Certification Sample Questions:
1. You are working with a large dataset consisting of multiple files. How can you efficiently load the data into Spark while considering efficient storage and processing?
A) Use spark.read.textFile("/path/to/directory/") to read all files at once
B) Leverage partitioning techniques like spark.read.textFile("/path/to/directory").repartition(n)
C) Load each file individually using spark.read.textFile("/path/to/file")
D) All of the above
2. What impact does the Spark configuration parameter spark.network.timeout have on Spark streaming applications?
A) It controls the maximum time a Spark streaming application will wait for data before timing out.
B) It sets the timeout for Akka-based network operations, crucial for Spark streaming's stability.
C) It specifies the timeout for network operations such as data shuffle and broadcast.
D) It determines the timeout for connections between executors and the Spark driver, impacting task retries and executor loss detection.
3. You're working with a complex DataFrame containing nested structures (e.g., arrays of structs). How can you access and manipulate data within these nested structures?
A) Directly access elements using their position within the nested structure
B) Leverage Spark SQL's built-in functions like explode and struct
C) Convert the nested data into a simpler format like a single-level DataFrame
D) Implement custom recursive functions to navigate through the nested structure
4. How can you implement a data quality check in Apache Airflow that verifies the row count of a table does not decrease from the previous DAG run?
A) Store the row count from the previous run in Airflow Variables and compare it using a Pythonoperator.
B) Use the BranchPythonOperator to branch the workflow based on the row count comparison logic.
C) Implement a custom SqlSensor that checks the row count against a stored value in XComs.
D) Utilize the PreviousDagRunSensor with a custom Python function for comparison.
5. You're building a complex Airflow DAG with numerous tasks and dependencies. How can you improve the DAG's readability and maintainability?
A) Use clear and descriptive names for tasks, operators, and variables throughout the DAG code.
B) Break down the DAG into smaller sub-DAGs with well-defined functionalities.
C) All of the above
D) Implement extensive logging within each task to capture detailed execution information.
Solutions:
| Question # 1 Answer: A,B | Question # 2 Answer: D | Question # 3 Answer: B | Question # 4 Answer: A | Question # 5 Answer: C |
