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Cloudera CDP Data Engineer - Certification Sample Questions:

1. What mechanism does Apache Airflow provide to delay the execution of a task until a certain condition is met?
A The delay parameter in task definitions.

A) The execution_timeout attribute, which can be set to postpone task execution.
B) The wait_for_downstream setting in the DAG configuration.
C) Sensors, which are special operators that wait for a certain condition to be true.

2. What is the impact of setting the Spark configuration spark.sql.autoBroadcastJoinThreshold to -1?

A) It sets an unlimited threshold for broadcasting tables, which may cause out-of-memory errors.
B) It increases the threshold for choosing which table to broadcast in a join, potentially improving join performance.
C) It disables the broadcast join feature, forcing all joins to be shuffled joins.
D) It automatically selects the optimal threshold for broadcasting based on the cluster's current workload.

3. In the context of Cloudera's Optimization Framework, what is the purpose of dynamic partition pruning?

A) To increase the size of partitions dynamically based on data volume
B) To dynamically eliminate unnecessary partitions from a query plan based on runtime statistics
C) To update partition metadata in real-time
D) To partition data dynamically based on query execution plans

4. You're working with a CSV file containing missing dat
a. How can you efficiently handle missing values in a Spark DataFrame created from this file?

A) Ignore the rows with missing values during DataFrame creation
B) Convert the DataFrame to a different format that doesn't support missing values
C) Implement custom logic to identify and handle missing values individually
D) Use the fillna() function with an appropriate replacement value

5. 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) Use Spark's checkpointing functionality to recover from failures
C) Recompute the entire stream from scratch for each late record
D) Ignore late-arriving data altogether

Solutions: