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Cloudera CDP Data Engineer - Certification Sample Questions:

1. You're experimenting with Iceberg table formats (vl and v2). Which of the following statements is true regarding their differences?

A) V2 uses manifest lists instead of manifest files for tracking data files.
B) V2 tables are generally less performant than V1 tables due to added metadata overhead.
C) V2 supports new data types like UUIDs, which are unavailable in V1.
D) V2 introduces mandatory partitioning, while V1 allows for unpartitioned tables.

2. You're tasked with deploying a new Airflow DAG to production. What are some key considerations for ensuring a smooth and successful deployment?

A) All of the above
B) Deploy the DAG directly to the production environment without any testing or staging phase.
C) Utilize infrastructure as code (laC. tools like Terraform to manage Airflow deployment and configuration in a consistent and repeatable manner.
D) Thoroughly test the DAG in a staging environment before deploying it to production.

3. Which technique in the Optimization Framework is primarily used to improve query performance by reducing the number of rows to be scanned in a table?

A) Indexing
B) Data replication
C) Data compaction
D) Predicate pushdown

4. How can you secure your data pipelines within the Cloudera Data Engineering service to ensure data privacy and compliance?

A) Implement encryption for data at rest and in transit but ignore user access control.
B) Employ a layered security approach combining access control, encryption, and audit logging.
C) Utilize Cloudera Manager security features without additional configuration within the Data Engineering service.
D) Rely solely on access control lists (ACLs) defined on individual data assets.

5. You're working with a large dataset stored in multiple Parquet files across different HDFS directories. How can you efficiently load and process this data using Spark, ensuring data locality and minimizing shuffle operations?

A) Leverage Spark SQL catalogs and partition discovery
B) Directly load all files using spark.read.parquet("/path/to/data/")
C) Use spark.read.parquet("/path/to/data/") with recursive directory listing
D) Implement a custom function to read each Parquet file individually

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