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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q54-Q59):

NEW QUESTION # 54
You're building an Airflow DAG to extract data from a database table that is constantly updated. How can you implement incremental extraction to avoid processing the entire table each time the DAG runs?

A. Leverage the Previous execution operator to access the execution date of the previous DAG run and use it as a filter in the extraction query.
B. Configure the database connection to only retrieve newly added data.
C. Use the File sensor to check for the presence of a new data file and trigger the DAG only when the file appears.
D. Implement a custom Python script to track the last processed record and use it to filter the data during subsequent runs.

Answer: A

Explanation:
While other options might be applicable in certain scenarios, option B is the most common and efficient way to implement incremental extraction in Airflow for databases. The Previous execution operator allows you to access the execution date of the previous DAG run, which can be used as a filter in the extraction query to only retrieve data added or modified since the last run.

NEW QUESTION # 55
What is the impact of query vectorization in Cloudera's Optimization Framework?

A. It improves query performance by processing batches of rows together
B. It slows down query execution by adding complexity
C. It enables the execution of SQL commands
D. It encrypts query results for security

Answer: A

Explanation:
Query vectorization improves performance by allowing multiple rows to be processed together as a batch, instead of one row at a time. This reduces CPU usage and improves throughput for query operations-Explain Plans

NEW QUESTION # 56
When using Apache Airflow to schedule quality checks, which strategy helps ensure that checks are only run on the most recent data partition?
A Use the LatestOnlyOperator to skip tasks that are not the latest in a series of executions.

A. Utilize the execution_date in your SQL query to target the latest partition.
B. Apply the depends_on_past=True parameter to the quality check task.
C. Implement a custom PythonOperator that queries the database for the latest partition.

Answer: A

Explanation:
The LatestOnlyOperator in Apache Airflow allows you to ensure that certain tasks are only executed in the most recent DAG run. This is useful for quality checks that should only be applied to the latest data partition, avoiding unnecessary checks on historical data.

NEW QUESTION # 57
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. Utilize system-wide library installations, assuming they are accessible to the Airflow user.
B. Install the required libraries directly into the Airflow environment.
C. Package the libraries with your DAG code and reference them within the Python operators.
D. Configure Airflow to use a virtual environment with pre-installed libraries.

Answer: C

Explanation:
Option B provides isolation and avoids potential conflicts with other Airflow DAGs or system-wide installations. Packaging the required libraries with your DAG code ensures they are available specifically for your pipeline's execution.

NEW QUESTION # 58
In a PySpark application, you're writing a function that reads a CSV file and shows the first few rows. Which of the following code snippets correctly accomplishes this task?

A. Option D
B. Option A
C. Option C
D. Option B

Answer: D

Explanation:
The correct way to read a CSV file in a PySpark application is using the 'SparkSession.read.csv' method or 'SparkSession.read.format("csv").load'. After loading the data into a DataFrame, 'df.show(5)' is used to display the first five rows. Options A and C do not use the correct method to display the data, and option D uses Pandas, not PySpark.

NEW QUESTION # 59
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