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Cloudera CDP Data Engineer - Certification Sample Questions:

1. You're working with a large DAG that contains numerous tasks and complex dependencies. How can you improve the DAG's readability and maintainability?

A) Utilize comments sparingly within the DAG code, as the logic should be self-explanatory.
B) Implement extensive logging within each task, regardless of its purpose, to capture detailed execution information.
C) Break down the DAG into smaller sub-DAGs with well-defined functionalities and clear naming conventions.
D) Use cryptic and abbreviated names for tasks and variables, assuming everyone understands the context.

2. Given a DataFrame containing product information with columns "product_id", "name", and "price", how can you filter and sort the DataFrame to only include products with a price greater than $50 and sort them by price in descending order?

A) Leverage chained conditions and sorting within the filter method
B) Use multiple filter and sort calls sequentially
C) Use spark SQL's WHERE clause and ORDER BY statement
D) Implement a custom function to filter and sort the data

3. When performing a bucketed join between two tables, what must be true for the join to be executed as a map-side join, thereby maximizing performance?

A) Both tables must be bucketed on the join columns with the same number of buckets and the same hash function.
B) Both tables must be partitioned, not bucketed, on the join columns.
C) Both tables must be bucketed on the join columns with a different number of buckets.
D) Only one table needs to be bucketed, regardless of the bucket count.

4. In Apache Spark, which of the following is the most effective strategy for minimizing data shuffling across nodes in a cluster?

A) Increasing the number of partitions
B) Decreasing the number of partitions
C) Filtering data after a wide transformation
D) Using broadcast variables for small data

5. You're debugging a slow-running Spark job writing a large Iceberg table. Which optimization techniques could improve performance? (Choose three.

A) Repartitioning the DataFrame before writing to Iceberg
B) Converting the DataFrame to RDD for Iceberg writes
C) Disabling Spark's adaptive query execution
D) Using the Z-Order clustering option in Iceberg
E) Filtering data as early as possible in the Spark transformation pipeline

Solutions: