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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q120-Q125):
NEW QUESTION # 120
In a CI/CD pipeline, what is a key consideration when integrating Cloudera Data Engineering (CDE. service API calls for deploying Spark jobs, specifically regarding security?
- A. Disabling TLS for API endpoints to simplify integration
- B. Using generic Spark Docker images for all jobs to avoid API customization
- C. Incorporating API token rotation and secure secret management practices
- D. Hardcoding API keys in the pipeline scripts
Answer: C
Explanation:
A key consideration when integrating CDE service API calls for deploying Spark jobs in a CI/CD pipeline, specifically regarding security, involves incorporating API token rotation and secure secret management practices. This ensures that authentication credentials used in API calls are securely managed and rotated regularly to protect access to the CDE environment. Hardcoding API keys, using generic Docker images to avoid API customization, and disabling TLS for API endpoints would all undermine security best practices and potentially expose the environment to unauthorized access or other security risks.
NEW QUESTION # 121
In the context of Hive, what mechanism ensures that data is evenly distributed across buckets?
- A. External data balancing tools
- B. Manual data insertion scripts
- C. A hash function applied to the bucketing column
- D. Natural key distribution
Answer: C
Explanation:
In the context of Hive, a hash function applied to the bucketing column ensures that data is evenly distributed across buckets. When data is inserted into a bucketed table, Hive applies a hash function to the value of the bucketing column for each row to determine the bucket where the row should be stored. This mechanism helps achieve an even distribution of data across buckets, which is crucial for optimizing query performance and data management efficiency.
NEW QUESTION # 122
Which Spark component is responsible for managing the execution of tasks on worker nodes?
- A. Spark Executor
- B. Spark Core
- C. Spark Driver
- D. spark SQL
Answer: A
Explanation:
Each worker node runs an executor responsible for receiving tasks from the driver, fetching data from storage, and performing computations on assigned partitions.
NEW QUESTION # 123
Consider the following code snippet:# Sample DataFrame (assuming it exists) df = spark.createDataFrame(...)
# Attempt to explode a nested array column (fix the error)
df_exploded = df.withColumn("items", F.explode(df["items"]))
df_exploded.show()
What is the error in this code, and how can it be fixed?
- A. There is no error in the code snippet.
- B. The error is attempting to modify the original DataFrame in-place. Fix: Use a separate variable to store the exploded DataFrame.
- C. The error is missing parentheses around the column name in the explode function. Fix: F.explode(df("items"))
- D. The error is using withColumn instead of a dedicated method for exploding arrays. Fix: Use df.withColumnExploded. (This function doesn't exist in Spark)
Answer: B
NEW QUESTION # 124
When deploying a Spark application in Kubernetes, what is the purpose of specifying the '-conf spark.kubernetes.driver.pod.name=spark-driver-pod' in the 'spark-submit' command?
- A. To assign a specific node for the Driver pod.
- B. To specify the name of the Spark Driver pod.
- C. To define the Docker image for the Driver pod.
- D. To name the Executor pods.
Answer: B
Explanation:
The configuration 'spark.kubernetes.driver.pod.name' in the 'spark-submit' command is used to specify the name of the Kubernetes pod that will run the Spark Driver.
NEW QUESTION # 125
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