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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q79-Q84):

NEW QUESTION # 79
Which of the following best describes the benefit of partition pruning in Spark SQL?

A. It increases the number of partitions to maximize parallelism.
B. It decreases the amount of data shuffled during join operations.
C. It dynamically adjusts partition sizes based on the query execution plan.
D. It selectively processes partitions that meet certain criteria, reducing 1/0.

Answer: D

Explanation:
Partition pruning is a performance optimization in Spark SQL that allows Spark to read only a subset of partitions from a DataFrame or table that meet certain criteria specified in the query. This significantly reduces the amount of data read from disk and processed, thereby decreasing I/O and improving query performance. Partition pruning is particularly effective for queries with filters that can be applied to partitioned columns. Options A and B describe different aspects of performance optimization but are not directly related to partition pruning. Option D suggests an adaptive behavior not specifically tied to the concept of partition pruning.

NEW QUESTION # 80
In Apache Airflow, how can you ensure that a data quality check task is only executed if a previous data ingestion task succeeded and the ingested data volume exceeds a certain threshold?

A. Implement a PythonOperator with a pre_execute hook for the condition check.
B. Use a BranchPythonOperator with a condition that checks the success of the ingestion task and the data volume.
C. Utilize the ShortCircuitOperator with a custom Python function to check both conditions.
D. Configure the trigger_rule parameter to all_success and use a PythonOperator to verify the data volume.

Answer: C

Explanation:
The ShortCircuitOperator is designed for situations where subsequent tasks should only run based on the outcome of a condition. By using this operator with a custom Python function that verifies both the success of the previous task and whether the data volume exceeds a specific threshold, you can effectively control the execution flow of your data quality checks.

NEW QUESTION # 81
In Apache Airflow, what is a DAG?

A. A collection of tasks with directional dependencies
B. A programming language used for creating data pipelines
C. A type of operator used for executing Python functions
D. A visualization tool for monitoring pipeline execution

Answer: A

Explanation:
A DAG (Directed Acyclic Graph) in Apache Airflow represents a collection of tasks organized to reflect their relationships and dependencies. It is not a programming language, an operator, or a visualization tool; it's a concept that defines the structure of a workflow.

NEW QUESTION # 82
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. Use the PythonOperator to write custom Python scripts for each individual check and chain them together in the DAG.
B. Utilize Python libraries like Pandas or Spark for data manipulation and validation within the PythonOperator.
C. All of the above
D. 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).

Answer: C

NEW QUESTION # 83
Which feature of Apache Avro facilitates dynamic schema inference during data serialization and deserialization?

A. Strong data typing
B. Schema versioning via external services
C. Manual schema declaration
D. Embedded schema within data files

Answer: D

Explanation:
Apache Avro facilitates dynamic schema inference through the embedding of schemas within data files. This feature allows each data file to carry its schema, enabling seamless serialization and deserialization processes without the need for external schema definitions. The embedded schema ensures that the data structure is always known, simplifying data processing and integration tasks.

NEW QUESTION # 84
......

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