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Cloudera CDP Data Engineer - Certification Sample Questions:

1. You need to create a new Hive table from a Spark DataFrame. What are the different approaches you can consider?

A) All of the above
B) Convert the DataFrame to a temporary table and then use HiveQL commands to create a permanent table
C) Directly write the DataFrame to a directory in HDFS and define a corresponding Hive table schema
D) Use the DataFrame.write.saveAsTable("table_name") method with appropriate options

2. You have a PySpark application packaged as 'MyPySparkApp-0. I-py3-none-any.whl'. In your 'app.py', you utilize a function from an external library, 'numpy', listed in your 'requirements.txt'. How should you deploy this application to ensure 'numpy' is available at runtime?

A) Upload both 'app.py' and 'MyPySparkApp-0.1-py3-none-any.whl' and submit using 'spark-submit --py-files MyPySparkApp-0.1-py3-none- any.whl app.py'.
B) Upload 'app.py' and manually install 'numpy' on all nodes before submitting using 'spark-submit app.py'.
C) Upload 'app.py' only and submit using 'spark-submit app.py'.
D) Upload 'MyPySparkApp-0.1-py3-none-any.whl' only and submit using 'spark-submit --py-files MyPySparkApp-0.1-py3-none-any.whl'.

3. What does setting the Spark configuration parameter spark.sql.shuffle.partitions impact?

A) The serialization format of data
B) The compression codec used for shuffle files
C) The memory allocation for executor instances
D) The default level of parallelism for joins and aggregations

4. In the context of dynamic partitioning in Hive, what challenge does the use of too many dynamic partitions in a single load operation present?

A) It enhances data security by segmenting data into finer-grained partitions.
B) It automatically disables the execution of map-reduce jobs.
C) It simplifies the management of Hive metadata, reducing the load on the NameNode.
D) It can lead to an excessive number of small files, negatively impacting HDFS performance.

5. Your Airflow DAG performs data quality checks that involve complex data transformations and aggregations. How can you ensure these checks are executed efficiently and don't impact the performance of the entire pipeline?

A) Implement the data transformations and aggregations directly within the checking logic, regardless of efficiency.
B) Utilize libraries like Pandas or Spark for data manipulation within the PythonOperator, leveraging optimization features where available.
C) Offload complex data processing tasks to separate DAGs designed specifically for data preparation.
D) Use Airflow's built-in data manipulation operators like SQLExecuteOperator for basic checks, avoiding complex transformations.

Solutions: