Prepare With Top Rated High-quality CDP-3002 Dumps For Success in CDP-3002 Exam [Q85-Q100]

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Prepare With Top Rated High-quality CDP-3002 Dumps For Success in CDP-3002 Exam

CDP-3002 Free Certification Exam Easy to Download PDF Format 2026

QUESTION 85
You’re tasked with deploying a new Airflow DAG to production. What are some key considerations for ensuring a smooth and successful deployment?

Deploy the DAG directly to the production environment without any testing or staging phase.

Utilize infrastructure as code (laC. tools like Terraform to manage Airflow deployment and configuration in a consistent and repeatable manner.

Thoroughly test the DAG in a staging environment before deploying it to production.

All of the above

QUESTION 86
In Apache Airflow, which operator is best suited for running data quality checks on a Hive table after data ingestion?

BashOperator

PythonOperator

HiveOperator

ShortCircuitOperator

QUESTION 87
How can you utilize Spark SQL for complex data analysis involving joins and aggregations on large datasets?

By directly writing SQL queries against distributed data in Spark.

By implementing custom logic in RDDs for data manipulation.

By using Spark ML libraries for machine learning tasks on data.

Both A and B.

QUESTION 88
If a Spark Driver pod in Kubernetes is reaching its CPU limit and experiencing performance issues, what is the most appropriate first action?

Increase the CPU requests in the pod’s YAML configuration.

Decrease the memory limits in the pod’s YAML configuration.

Increase the CPU limits in the pod’s YAML configuration.

Restart the Kubernetes cluster.

QUESTION 89
Given a DataFrame containing product information with columns “product_id”, “name”, and “price”, how can you filter and sort the DataFrame to only include products with a price greater than $50 and sort them by price in descending order?

Use multiple filter and sort calls sequentially

Leverage chained conditions and sorting within the filter method

Implement a custom function to filter and sort the data

Use spark SQL’s WHERE clause and ORDER BY statement

QUESTION 90
You need to enable secure access to Iceberg tables in CDP, controlling permissions at the table, column, and row level. Which of the following approaches would you investigate?

Implement custom Spark IJDFs to enforce authorization logic.

Leverage Apache Ranger’s integration with Iceberg.

Configure role-based access control (RBA directly within Iceberg table properties.

Integrate with CDP’s Kerberos-based authentication system.

QUESTION 91
A data analyst is performing a join operation in PySpark between a large DataFrame Clarge_df) and a much smaller DataFrame (‘small_df). The process is very slow. What can the analyst do to optimize this join operation?

Use “key”).

Apply before the join.

Utilize the ‘broadcast’ function in the join operation.

Increase the number of partitions in ‘small_df’.

QUESTION 92
Which Airflow operator is best used for executing a Python function as part of a DAG?

BashOperator

PythonOperator

SimpleHttpOperator

Dockeroperator

QUESTION 93
A team is planning to use PySpark to read data from an Apache Cassandra database. Which of the following options correctly demonstrates how to load data from a Cassandra table named in the keyspace ‘sales’?

QUESTION 94
You need to optimize the performance of a Spark query that involves joining data from multiple Hive tables. What strategies can you employ to improve efficiency?

Increase the number of Spark executors without any further optimization

Use broadcast joins for small tables involved in the join operation

Pre-partition tables based on the join columns for faster data co-location

All of the above

QUESTION 95
What mechanism does Airflow provide to retry failed tasks?

Airflow Scheduler’s automatic rerun feature

The retry_delay and retries parameters in task definitions

Manual intervention and rerun via the Airflow Webserver

The on failure callback function in DAG definitions

QUESTION 96
You are deploying a Spark application in a Kubernetes environment. Your application is designed to process large datasets using Spark’s data frame API. You have created a Docker image for your Spark application. Which of the following ‘kubectl* commands should you use to deploy your Spark application onto the Kubernetes cluster?

*kubectl apply -f spark-app.yamr

‘kubectl create deployment my-spark-app –image=my-spark-app-image’

*kubectl config set-context -current -namespace=my-spark-app’

‘kubectl expose deployment my-spark-app –type=LoadBalancer -port=808ff

QUESTION 97
You’re deploying your Airflow ETL pipelines to a production environment. What are some best practices to ensure reliability and scalability?

Configure Airflow to run with high resource limits to handle unexpected spikes in workload.

Implement robust error handling and retry mechanisms within your DAGs.

Utilize version control for your DAG code and configuration files to track changes and facilitate rollbacks.

All of the above

QUESTION 98
You’re working with a CSV file containing missing dat
a. How can you efficiently handle missing values in a Spark DataFrame created from this file?

Ignore the rows with missing values during DataFrame creation

Use the fillna() function with an appropriate replacement value

Implement custom logic to identify and handle missing values individually

Convert the DataFrame to a different format that doesn’t support missing values

QUESTION 99
You’re working with a complex data pipeline involving Spark and Hive, and you need to monitor its performance and identify potential bottlenecks. Which tools and techniques can you employ for effective monitoring?

Manually analyze Spark and Hive logs after job completion

Leverage Spark’s web UI and Hive logs for basic information

Utilize YARN resource manager and Spark/Hive metrics for detailed monitoring

Implement custom instrumentation code within your Spark application

QUESTION 100
Your Airflow DAG involves tasks that require access to confidential data like passwords or API keys. How can you securely manage and access these credentials within the DAG?

Store the credentials directly within the DAG code, assuming limited access to the codebase.

Utilize environment variables to store the credentials and access them within the tasks.

Implement a custom secret management solution outside of Airflow.

All of the above