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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q205-Q210):

NEW QUESTION # 205
In an Airflow DAG, you have tasks A, B, C, and D. Task A must complete before B and C can start, but B and C can run in parallel. Task D should only run once both B and C have completed. How do you set up these dependencies?

A. A]] B, C]] D
B. A.set_downstream([B, C]) and set_upstream([B, C])
C. [A ]] B, A]] C] and [B, C] ]] D
D. A]] B ]] C]] D

Answer: C

Explanation:

NEW QUESTION # 206
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. Iceberg metadata table pruning frequency
C. Kubernetes resource requests for Spark executors
D. The Iceberg table's write format (vl or v2)

Answer: B

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 # 207
You're developing a Spark application with multiple stages, and you want to ensure that later stages only start processing after all data from the previous stage is complete. How can you achieve this dependency management in Spark?

A. Use explicit rdd.persist() calls in each stage
B. Leverage Spark's lineage tracking and stage boundaries
C. Implement custom logic to track completion of each stage
D. Set appropriate configuration for spark.sql.shuffe.partitions

Answer: B

Explanation:
Spark automatically tracks the lineage of data across transformations. By defining stages in your application logic, Spark ensures that downstream stages only start processing once the upstream stage (and its lineagE. is complete, eliminating the need for manual dependency management.

NEW QUESTION # 208
You are deploying a Spark application on Kubernetes, which requires access to a web I-Jl. You decide to expose it using a Kubernetes service. Which type of service would you typically use to expose the Spark IJI to external traffic?

A. LoadBalancer
B. ClusteriP
C. NodePort
D. ExternalName

Answer: C

Explanation:
The NodePort service type is suitable for exposing the Spark IJI to external traffic. It makes the service accessible on each node's IP at a static port (the NodePort). ClusterlP is default and only accessible within the cluster, LoadBalancer is for cloud providers, and ExternalName maps a service to a DNS name, not suitable for this purpose.

NEW QUESTION # 209
You need to update several rows in a large Iceberg table. Which of the following approaches would likely be the most efficient?

A. Read the entire table, make changes in Spark, and overwrite the table.
B. Employ Iceberg's time travel feature to revert to an older snapshot and reapply changes.
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 # 210
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