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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. An Airflow DAG is designed to ingest data from multiple sources, transform it, and load it into a data warehouse. The transformation step is resource-intensive and should not run during peak hours (9 AM to 5 PM). How can you configure the DAG to meet this requirement?

A) Use the time_sensor operator to delay the transformation task until off-peak hours.
B) Set the max_active_runs parameter to limit executions during peak hours.
C) Configure the DAG's schedule interval and use the TimeDelta sensor for precise timing.
D) Utilize the BranchPythonOperator to dynamically skip the transformation task during peak hours.

2. You are setting up a high-availability Spark cluster and need to configure the master node for recovery using ZooKeeper.
Which configuration should be added to the 'spark-defaults.conf' file?

A)

B)

C)

D)

3. In a multi-tenant Hive environment, how can administrators mitigate the impact of skewed data distributions across bucketed tables to maintain consistent query performance?

A) Enforcing a uniform bucket size at the HDFS level, irrespective of the data distribution.
B) By periodically rebalancing data across buckets using custom scripts or tools.
C) Disabling bucketing features entirely to prevent skewed distributions.
D) Limiting the number of tenants allowed to create bucketed tables.

4. In Spark, what is the advantage of using the 'coalesce' method over the 'repartition' method when reducing the number of partitions in an RDD?

A) 'coalesce' triggers a full shuffle of the data, improving data distribution.
B) 'repartition' is incapable of reducing the number of partitions.
C) 'coalesce' reduces the number of partitions without a full data shuffle, enhancing performance.
D) 'coalesce' can increase the number of partitions without shuffling.

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

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

Solutions: