Make a learning plan

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Correct your mistake

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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. In Apache Airflow, how can you dynamically generate tasks for each table in your database that needs a quality check?

A) Utilize the Dynamic Task Mapping feature to create a task for each table.
B) Implement a BranchPythonOperator to create branches for each table dynamically.
C) Use the SubDagOperator to create a sub-DAG for each table.
D) Use the Variable feature to store a list of tables and iterate over them with a PythonOperator.

2. What are the potential challenges associated with schema inference in data processing pipelines?

A) Handling complex nested structures and arrays
B) Increased storage costs for schema metadata
C) The need for manual schema updates
D) Performance overhead due to schema discovery
E) Inaccuracies in inferred schemas leading to data processing errors

3. You are working with a large, skewed dataset in Spark. How would you optimize processing to mitigate the impact of skew and improve performance?

A) Implement custom partitioners to evenly distribute skewed values.
B) Addressing skewed data requires
C) Broadcast the skewed data to all executors.
D) Use salting on the skewed column during data partitioning.

4. You want to perform an Iceberg table join in CDP using Spark SQL, but you notice it's much slower than expected. What could be some of the reasons? (Choose two)

A) Spark's dynamic query execution is enabled.
B) Iceberg version mismatch between Spark and CDP.
C) One of the tables isn't partitioned effectively.
D) Spark is using nested loop joins instead of broadcast hash joins due to table sizes.
E) You're joining on a column with low cardinality (few distinct values).

5. You're building an Airflow DAG to automate data quality checks on the output of your ETL pipeline. The checks involve performing various data validation tasks like checking for missing values, ensuring data type consistency, and verifying data integrity based on specific business rules. How can you implement these checks within Airflow?

A) Leverage dedicated Airflow operators like BigQueryCheckOperator or S3KeySensor (these operators are specific to certain data sources and not generally applicable for all data quality checks).
B) Use the PythonOperator to write custom Python scripts for each individual check and chain them together in the DAG.
C) Utilize Python libraries like Pandas or Spark for data manipulation and validation within the PythonOperator.
D) All of the above

Solutions: