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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

Question 1

What challenge does schema inference aim to address when dealing with big data ecosystems?

A. The need for large storage spaces to hold data schemas
B. The variety and complexity of data formats and structures
C. Reducing the computational power required for data analysis
D. Ensuring that all data is encrypted according to its inferred schema

Question 2

Your ETL pipeline involves complex data transformations that require libraries not readily available in the Airflow environment. How can you ensure these libraries are accessible during pipeline execution?

A. Configure Airflow to use a virtual environment with pre-installed libraries.
B. Install the required libraries directly into the Airflow environment.
C. Utilize system-wide library installations, assuming they are accessible to the Airflow user.
D. Package the libraries with your DAG code and reference them within the Python operators.

Question 3

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

Question 4

You're provisioning a new Cloudera Data Engineering (CDE. virtual cluster. Which of the following factors should you consider when choosing an appropriate instance type for Iceberg workloads? (Choose two)

A. The complexity of your SQL queries and transformations
B. The type of storage (SSD vs. HDD. needed for optimal performance
C. Whether you require GPU acceleration for machine learning tasks integrated with Iceberg
D. The expected size of your Iceberg tables
E. The network bandwidth required by your Iceberg jobs

Question 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. Use spark.read.parquet("/path/to/data/") with recursive directory listing
C. Implement a custom function to read each Parquet file individually
D. Directly load all files using spark.read.parquet("/path/to/data/")

Solutions: