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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. When leveraging Spark's DataFrame API for caching, what implicit optimization does Spark perform to enhance processing efficiency?

A) Predicate pushdown and other logical optimizations before physical execution
B) Inlining of functions to reduce the overhead of JVM method calls
C) Automatic selection of the optimal storage level based on the DataFrame's size
D) Automatic conversion of all operations to map-reduce jobs

2. What Airflow feature allows you to template parts of your DAG to dynamically change based on the execution context?

A) PythonOperator
B) Jinja Templating
C) BranchOperator
D) XComs

3. What is the key benefit of using the Cloudera Data Engineering service compared to building and managing data pipelines manually?

A) Increased scalability, reliability, and manageability.
B) Cost savings due to open-source technology.
C) Faster development time for simple data pipelines.
D) Improved data security through proprietary tools.

4. How can "Explain Plan" help in optimizing query performance regarding data partitioning?

A) By indicating whether the query is able to take advantage of partition pruning
B) By revealing the encryption method used for partitioned data
C) By showing the number of partitions created on the fly
D) By displaying the total size of all partitions

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

Solutions: