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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q235-Q240):

NEW QUESTION # 235
Your ETL pipeline processes sensitive dat
a. How can you ensure data security within Airflow?

A. Use environment variables to store sensitive credentials for accessing data sources.
B. Store passwords and other sensitive data directly within your Airflow code.
C. Grant everyone access to the Airflow web UI to monitor and manage the pipeline.
D. Implement encryption for sensitive data at rest and in transit.

Answer: D

Explanation:
Option C highlights the crucial security practice of encrypting sensitive data at rest (e.g., in databases) and in transit (e.g., during network communication). This protects confidential information from unauthorized access.

NEW QUESTION # 236
For automating the deployment of Spark applications within a Cloudera Data Engineering (CDE. environment using the CDE CLI, what is the primary consideration to ensure seamless integration with existing CI/CD pipelines?

A. Converting all Spark code to be Kubernetes-native before deployment
B. Ensuring all Spark applications are containerized before deployment
C. Embedding CDE CLI commands within pipeline scripts and managing credentials securely
D. Utilizing the cde job create command with appropriate flags for version control integration

Answer: C

Explanation:
The primary consideration for automating the deployment of Spark applications within a CDE environment using the CDE CLI and ensuring seamless integration with existing CI/CD pipelines involves embedding CDE CLI commands within the pipeline scripts and managing credentials securely. This approach allows the automation process to programmatically interact with the CDE service to deploy or update Spark applications, while also emphasizing the importance of secure credential management to protect access to the CDE environment and maintain security best practices.

NEW QUESTION # 237
You're working with an ETL pipeline that extracts data from multiple sources. How can you ensure that the pipeline only processes the latest data and avoids re-processing already processed data?

A. Implement a custom mechanism to track the last processed record for each source and filter data accordingly.
B. Rely on Airflow's built-in mechanisms to handle data freshness automatically.
C. Use timestamps or versioning information provided by the data sources to identify new data.
D. Configure the data sources to only provide new data by default.

Answer: A,C

Explanation:
While option C might not always be feasible or reliable depending on the specific data source, both option A and B provide valid approaches. Utilizing timestamps, versioning, or custom tracking mechanisms allows the pipeline to identify and process only the latest data, avoiding unnecessary re-processing.

NEW QUESTION # 238
Which feature of Apache Avro facilitates dynamic schema inference during data serialization and deserialization?

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

Answer: A

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 # 239
What is the correct way to define a start date for a DAG in Apache Airflow, ensuring that the DAG does not trigger immediately upon deployment?

A. Leave the start date undefined.
B. Use datetime.now() as the start date.
C. Use ) to automatically set the start date to one day before the current date.
D. Set the start date to a future date using the datetime module.

Answer: D

Explanation:
Setting the start date to a future date using the datetime module ensures that the DAG will not trigger immediately upon deployment but will instead start at the specified future date. This allows for controlled execution and avoids unexpected runs during deployment.

NEW QUESTION # 240
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