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[General] 100% Pass Quiz 2026 CDP-3002: Perfect CDP Data Engineer - Certification Exam Lat

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【General】 100% Pass Quiz 2026 CDP-3002: Perfect CDP Data Engineer - Certification Exam Lat

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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q151-Q156):NEW QUESTION # 151
You want to use Spark to perform aggregations on data stored in Hive tables. How can you achieve this efficiently and seamlessly?
  • A. Leverage Spark SQL's built-in aggregation functions like SUM and COUNT
  • B. Write custom aggregation logic using Spark functions and loop through the entire DataFrame
  • C. Use HiveQL's aggregation capabilities and then convert the results back to a Spark DataFrame
  • D. Implement custom UDFs (User-Defined Functions) in Spark for complex aggregations
Answer: A
Explanation:
Option B provides the most efficient and recommended approach. Spark SQL offers various built-in functions for aggregations like SUM, COUNT, AVG, etc., enabling concise and efficient processing of data directly from Hive tables.

NEW QUESTION # 152
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. Ignore the schema mismatch and write the data anyway, potentially leading to data corruption
  • B. Use HiveQL's ALTER TABLE statement to modify the Hive table schema
  • C. Modify the Spark DataFrame schema to match the Hive table schema manually
  • D. Leverage Spark SQL's schema inference capabilities to automatically adjust the DataFrame schema
Answer: D
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 # 153
You're facing slow performance in your Spark application due to frequent shuffles. What are some strategies you can employ to optimize shuffle operations and improve performance?
  • A. Reduce the number of partitions in your RDDs
  • B. Use a broadcast variable for small, frequently accessed data
  • C. Increase the number of executors in your cluster
  • D. All of the above
Answer: D
Explanation:
Shuffling can be a bottleneck. Increasing executors allows for parallel processing, reducing data movement per executor. Reducing partitions can minimize the need for data exchange between them. Broadcasting small, frequently used data avoids shuffling it for each task. Combining these strategies can significantly improve performance.

NEW QUESTION # 154
You're working with an ETL pipeline that extracts data from multiple sources. How can you ensure that the pipeline only processes the latest data and avoids re-processing already processed data?
  • A. Configure the data sources to only provide new data by default.
  • B. Rely on Airflow's built-in mechanisms to handle data freshness automatically.
  • C. Use timestamps or versioning information provided by the data sources to identify new data.
  • D. Implement a custom mechanism to track the last processed record for each source and filter data accordingly.
Answer: C,D
Explanation:
While option C might not always be feasible or reliable depending on the specific data source, both option A and B provide valid approaches. Utilizing timestamps, versioning, or custom tracking mechanisms allows the pipeline to identify and process only the latest data, avoiding unnecessary re-processing.

NEW QUESTION # 155
You need to join a Spark DataFrame with a Hive table. How can you achieve this efficiently?
  • A. Use Spark SQL syntax with the JOIN clause, specifying the join type and condition
  • B. Implement custom logic using Spark's RDD operations to join the data
  • C. Convert the Hive table to a temporary table and then perform the join with the DataFrame
  • D. Load the Hive table data into the DataFrame and then perform an in-memory join
Answer: A
Explanation:
Spark SQL provides seamless integration with Hive tables. Option A allows you to use familiar SQL syntax with the JOIN clause, specifying the join type (e.g., INNER, LEFT, RIGHT) and the join condition, offering an efficient and concise way to perform the join.

NEW QUESTION # 156
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