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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. You're working with a real-time streaming application using Spark Streaming. How can you ensure that your application gracefully handles late-arriving data and maintains data consistency?

A) Implement micro-batching with windowing and watermarking techniques
B) Recompute the entire stream from scratch for each late record
C) Use Spark's checkpointing functionality to recover from failures
D) Ignore late-arriving data altogether

2. 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) Implement custom recursive functions to navigate through the nested structure
B) Leverage Spark SQL's built-in functions like explode and struct
C) Directly access elements using their position within the nested structure
D) Convert the nested data into a simpler format like a single-level DataFrame

3. How does dynamic schema inference impact the processing of streaming data in real-time analytics platforms?

A) It requires streaming data to be stored before its schema can be inferred.
B) It significantly increases the latency of data processing operations.
C) It decreases the overall throughput of the analytics platform.
D) It enables the platform to adapt to changes in data format without downtime.

4. Consider the following code snippet:# Sample DataFrame (assuming it exists) df = spark.createDataFrame(...)
# Attempt to add a new column with a case-when expression (fix the error) df = df.withColumn("category", F.when(df["price"] ] 100, "Expensive").otherwise("Cheap")) df.show() What is the error in this code, and how can it be fixed?

A) The error is missing parentheses around the conditions in the when function. Fix: F.when((df["price"] ] 100), "Expensive").otherwise("Cheap")
B) The error is using the wrong syntax for case-when expressions. Fix: Use SQL-like syntax with CASE WHEN and END.
C) There is no error in the code snippet.
D) The error is attempting to modify the original DataFrame in-place. Fix: Use df.withColumn to create a new DataFrame with the added column.

5. You're tasked with optimizing the performance of your ETL pipeline in Airflow. What are some potential strategies to consider?

A) All of the above
B) Leverage partitioning and bucketing techniques in the data warehouse to improve query performance.
C) Increase the number of worker processes in Airflow to parallelize task execution.
D) Utilize efficient data structures and algorithms within your custom Python operators for data transformation.

Solutions: