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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q69-Q74):
NEW QUESTION # 69
What command and parameters should be used to update an existing Spark job in the Cloudera Data Engineering (CDE. service to increase its executor memory using the CDE CLI?
- A. cde job run -edit my-spark-job -conf spark.executor.memory=6g
- B. cde job update -name my-spark-job --conf spark.executor.memory=6g
- C. cde spark submit -update -name my-spark-job -executor-memory 6G
- D. cde job config -name my-spark-job -set spark.executor.memory=6g
Answer: B
Explanation:
To update an existing Spark job in the Cloudera Data Engineering (CDE. service to increase its executor memory, the correct command is cde job update followed by the --name parameter to specify the job and the --conf flag to set the new configuration for spark.executor.memory. This command allows you to modify the job's configuration parameters directly via the CDE CLI, enabling adjustments to resource allocations such as executor memory.
NEW QUESTION # 70
What is the primary purpose of the Cost-Based Optimizer (CBO) in the context of Cloudera's Optimization Framework?
- A. To distribute data evenly across the cluster
- B. To choose the data storage format
- C. To minimize the amount of data processed
- D. To decide the best execution plan based on the cost of operations
Answer: D
Explanation:
The Cost-Based Optimizer (CBO) aims to select the most efficient execution plan for a query based on the cost associated with various operations. It considers factors like data size, data locality, and the computational cost of operations to optimize the query execution.
NEW QUESTION # 71
You're facing a schema mismatch between a Spark DataFrame and a Hive table when trying to write the DataFrame to the table. What are the potential causes and how can you address them?
- A. Use HiveQL's ALTER TABLE statement to modify the Hive table schema
- B. Modify the Spark DataFrame schema to match the Hive table schema manually
- C. Leverage Spark SQL's schema inference capabilities to automatically adjust the DataFrame schema
- D. Ignore the schema mismatch and write the data anyway, potentially leading to data corruption
Answer: C
Explanation:
Ignoring the mismatch A can lead to errors. Manually modifying the DataFrame schema B might be error-prone. Option D modifies the Hive table, potentially impacting other applications. Spark SQL can often infer the schema from the DataFrame's data types, providing a convenient and reliable way to address schema mismatches.
NEW QUESTION # 72
A data engineer is deploying a Spark application on a Kubernetes cluster. To minimize downtime during updates, which Kubernetes deployment strategy should be considered?
- A. Pod Autoscaling
- B. Node Affinity
- C. Persistent Volumes
- D. Rolling Updates
Answer: D
Explanation:
Rolling Updates is the correct Kubernetes deployment strategy to minimize downtime during updates. It ensures that the new version of the application is gradually rolled out while keeping the application available.
NEW QUESTION # 73
When using Cloudera's Command Line Interface (CLI), which of the following tasks can be performed?
- A. Only data ingestion tasks can be automated.
- B. Modifying the source code of Cloudera Manager.
- C. Cluster monitoring and diagnostics cannot be performed.
- D. Managing users and setting permissions on HDFS.
Answer: D
Explanation:
Using Cloudera's CLI allows for a wide range of administrative tasks, including managing users and setting permissions on HDFS. This capability is crucial for maintaining security and governance within a big data environment, enabling administrators to efficiently manage access controls and permissions from the command line without the need for direct interaction with the Cloudera Manager UI.
NEW QUESTION # 74
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