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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q33-Q38):

NEW QUESTION # 33
As a data engineer, you are working with PySpark to analyze data stored in an HDFS cluster. You need to read a CSV file into a Spark DataFrame. Which of the following code snippets correctly reads the data from HDFS into a DataFrame?

Answer: C

Explanation:
The correct way to read a CSV file from HDFS into a Spark DataFrame in PySpark is using 'spark.read.format("csv").load("hdfs:///path/to/data.csv")*. This method specifies the format as CSV and loads the file from the specified HDFS path.

NEW QUESTION # 34
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?

A. *kubectl apply -f spark-app.yamr
B. 'kubectl create deployment my-spark-app --image=my-spark-app-image'
C. *kubectl config set-context -current -namespace=my-spark-app'
D. 'kubectl expose deployment my-spark-app --type=LoadBalancer -port=808ff

Answer: A

Explanation:
To deploy a Spark application in Kubernetes, you should use a YAML configuration file that defines the SparkApplication resource. The correct command to apply this configuration is 'kubectl apply -f spark-app.yamr , as it will create or update resources in the cluster based on the YAML file.

NEW QUESTION # 35
What are the potential trade-offs to consider when using checkpointing in Spark applications?

A. All of the above
B. Checkpointing always improves performance and has no drawbacks
C. Checkpointing introduces overhead for storing and recovering data, impacting performance
D. Checkpointing requires manual configuration and can be error-prone

Answer: A,C

Explanation:
While checkpointing offers benefits, it comes with trade-offs. It adds overhead for storing and recovering data, potentially impacting performance B. Configuring checkpoints effectively is crucial to avoid unnecessary overhead. Option A is incorrect, and while configuration is involved, it's not inherently error-prone C when done carefully.

NEW QUESTION # 36
In Apache Spark, which storage level is recommended for caching data that is accessed frequently but is too large to fit in memory?

A. MEMORY ONLY
B. MEMORY AND DISK
C. OFF HEAP
D. DISK ONLY

Answer: B

Explanation:
The MEMORY_AND_DISK storage level is recommended for caching data that is frequently accessed but too large to fit entirely in memory. This level stores the data in memory as much as possible and spills the remainder to disk, providing a good balance between performance and resource utilization.

NEW QUESTION # 37
In the context of caching data for reuse in Spark, how does the Tungsten project contribute to enhancing memory management and execution efficiency?

A. By providing a graphical interface for memory management
B. By optimizing Spark's internal memory management and data processing operations
C. By rewriting Spark operations in a lower-level language for direct memory access
D. By enforcing strict data typing to reduce runtime type checking overhead

Answer: B

Explanation:
The Tungsten project contributes to enhancing memory management and execution efficiency in Spark by optimizing Spark's internal memory management and data processing operations. Tungsten introduces binary in-memory data representation and explicit control over memory allocation, reducing the overhead associated with garbage collection and improving cache efficiency. This optimization supports more efficient execution of operations and better utilization of system resources, particularly for tasks involving large datasets and complex computations.

NEW QUESTION # 38
......

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New CDP-3002 Dumps Files Exam Pass Certify | CDP-3002 Exam Topics Pdf

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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q33-Q38):

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