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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q226-Q231):

NEW QUESTION # 226
In Spark, what is the advantage of using the 'coalesce' method over the 'repartition' method when reducing the number of partitions in an RDD?

A. 'repartition' is incapable of reducing the number of partitions.
B. 'coalesce' reduces the number of partitions without a full data shuffle, enhancing performance.
C. 'coalesce' triggers a full shuffle of the data, improving data distribution.
D. 'coalesce' can increase the number of partitions without shuffling.

Answer: B

Explanation:
The 'coalesce' method is used to decrease the number of partitions in an RDD, and it does so without performing a full shuffle of the data. This makes it more efficient than 'repartition' for reducing the number of partitions because 'repartition' involves a full shuffle, which is more costly in terms of performance. 'coalesce' is particularly useful for optimization after filtering down a large dataset. Option A is incorrect as 'coalesce' is specifically designed for reducing partitions. Option B describes 'repartition', and Option D is factually incorrect.

NEW QUESTION # 227
You want to write the results of a Spark DataFrame operation to a Parquet file for efficient storage and retrieval. What approach can you achieve this efficiently?

A. Leverage the DataFrame.write.parquet() method with appropriate options
B. Use Spark SQL's CREATE TABLE statement with the Parquet format specified
C. Convert the DataFrame to a temporary table and then use HiveQL commands to write to Parquet
D. Use the DataFrame.write.text() method and specify the output format as Parquet

Answer: A

Explanation:
While text files A are not efficient for Parquet, option B involves unnecessary steps. Spark DataFrames provide a dedicated method write.parquet() C for efficient Parquet file generation. Option D is mainly used for creating Hive tables, not DataFrames within Spark.

NEW QUESTION # 228
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 expose deployment my-spark-app --type=LoadBalancer -port=808ff
D. *kubectl config set-context -current -namespace=my-spark-app'

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 # 229
What mechanism does Apache Airflow provide to delay the execution of a task until a certain condition is met?
A The delay parameter in task definitions.

A. Sensors, which are special operators that wait for a certain condition to be true.
B. The wait_for_downstream setting in the DAG configuration.
C. The execution_timeout attribute, which can be set to postpone task execution.

Answer: C

Explanation:
Sensors in Apache Airflow are a type of operator designed to delay the execution of tasks until a specified condition is met. They are commonly used to wait for data to be available in a database, a file to appear in a filesystem, or any other condition that must be true before proceeding with the downstream tasks.

NEW QUESTION # 230
Why is it recommended to use the DataFrame API over RDDs for most data processing tasks in Spark?

A. DataFrames require less memory and compute resources compared to RDDs.
B. RDDs are deprecated and will be removed in future versions of Spark.
C. DataFrames automatically optimize queries using the Catalyst optimizer and Tungsten execution engine.
D. DataFrames provide more fine-grained control over partitioning and parallelism.

Answer: C

Explanation:
The recommendation to use DataFrames (or Datasets) over RDDs is primarily due to the performance optimization benefits offered by the Catalyst optimizer and Tungsten execution engine. These components automatically optimize Spark SQL queries, improving execution efficiency and performance. DataFrames provide a higher-level abstraction with optimized storage and execution plans, which RDDs lack. Option A is incorrect as DataFrames and RDDs offer different levels of control over partitioning and parallelism. Option C is misleading; RDDs are not deprecated and continue to be a core feature of Spark for scenarios requiring fine-grained control over distributed computing. Option D oversimplifies the comparison; while DataFrames can be more efficient due to optimization, the primary advantage is not just reduced resource usage but also the automatic query optimization.

NEW QUESTION # 231
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