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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q162-Q167):

NEW QUESTION # 162
How does the Cloudera Data Engineering service integrate with cloud storage solutions like Amazon S3 or Azure Blob Storage?

A. Not directly supported, requires external tools.
B. Utilizes built-in connectors for seamless access.
C. Requires manual configuration for each storage bucket.
D. Requires custom scripting for each cloud storage provider.

Answer: B

Explanation:
The Cloudera Data Engineering service comes with pre-built connectors for various cloud storage solutions like Amazon S3 and Azure Blob Storage. These connectors simplify data movement and integration between the platform and cloud storage solutions.

NEW QUESTION # 163
Your Iceberg table experiences frequent schema evolution, with new columns being added regularly. Which of the following might you need to adjust to maintain optimal performance?

A. Spark shuffle partitions (spark.sql.shuffle.partitions)
B. Kubernetes resource requests for Spark executors
C. Iceberg metadata table pruning frequency
D. The Iceberg table's write format (vl or v2)

Answer: C

Explanation:
Frequent schema changes generate many metadata files. Iceberg's metadata tables may need more frequent pruning (expire_snapshots) to avoid performance issues during reads.

NEW QUESTION # 164
What is the impact of query vectorization in Cloudera's Optimization Framework?

A. It encrypts query results for security
B. It improves query performance by processing batches of rows together
C. It slows down query execution by adding complexity
D. It enables the execution of SQL commands

Answer: B

Explanation:
Query vectorization improves performance by allowing multiple rows to be processed together as a batch, instead of one row at a time. This reduces CPU usage and improves throughput for query operations-Explain Plans

NEW QUESTION # 165
You need to update several rows in a large Iceberg table. Which of the following approaches would likely be the most efficient?

A. Employ Iceberg's time travel feature to revert to an older snapshot and reapply changes.
B. Read the entire table, make changes in Spark, and overwrite the table.
C. Use the Iceberg DELETE and INSERT operations together.
D. Use the Iceberg MERGE INTO syntax for row-level updates.

Answer: C

Explanation:
Iceberg supports record-level DELETE and INSERT operations for targeted updates. Options A and C are very inefficient for large tables. MERGE INTO is available, but less efficient than direct deletes and inserts.

NEW QUESTION # 166
Given a DataFrame containing product information with columns "product_id", "name", and "price", how can you filter and sort the DataFrame to only include products with a price greater than $50 and sort them by price in descending order?

A. Use spark SQL's WHERE clause and ORDER BY statement
B. Implement a custom function to filter and sort the data
C. Use multiple filter and sort calls sequentially
D. Leverage chained conditions and sorting within the filter method

Answer: D

Explanation:
Chaining conditions within the filter method allows for concise and efficient filtering. Option B demonstrates this approach:filtered_sorted_df = ] 50).sort("price", ascending=FalsE.
This code first filters the DataFrame to only include rows where the "price" is greater than 50 and then sorts the resulting DataFrame by the "price" column in descending order (using ascending=FalsE.

NEW QUESTION # 167
......

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CDP-3002 Download Pdf - Reliable CDP-3002 Practice Materials

Quiz Unparalleled Cloudera - CDP-3002 - CDP Data Engineer - Certification Exam Download Pdf

Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q162-Q167):

100% Pass 2025 Authoritative CDP-3002: CDP Data Engineer - Certification Exam Download Pdf

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