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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. Which approach can help mitigate issues with schema inference for complex data types in a big data environment?

A) Using only traditional RDBMS systems that require explicit schema definitions
B) Combining schema inference with schema evolution and user-defined schemas for complex datasets
C) Ignoring schema inference and processing all data as plain text
D) Decreasing the frequency of data ingestion to reduce processing load

2. You are deploying a Spark application in a Kubernetes environment. Your application is designed to process large datasets using Spark's data frame API. You have created a Docker image for your Spark application. Which of the following 'kubectl* commands should you use to deploy your Spark application onto the Kubernetes cluster?

A) 'kubectl create deployment my-spark-app --image=my-spark-app-image'
B) *kubectl config set-context -current -namespace=my-spark-app'
C) *kubectl apply -f spark-app.yamr
D) 'kubectl expose deployment my-spark-app --type=LoadBalancer -port=808ff

3. You're using Iceberg for streaming data ingestion. A critical use case requires exactly-once processing guarantees. Which considerations are most important for achieving this? (Choose two)

A) Enabling Iceberg's transactional writes
B) Leveraging a CDC (Change Data Captur tool integrated with Iceberg.
C) Disabling Iceberg's snapshot isolation feature.
D) Carefully configuring the streaming source to handle retries and failures
E) Using a data format like JSON, which is less prone to corruption than Parquet.

4. You're working with a real-time streaming application using Spark Streaming. How can you ensure that your application gracefully handles late-arriving data and maintains data consistency?

A) Use Spark's checkpointing functionality to recover from failures
B) Recompute the entire stream from scratch for each late record
C) Implement micro-batching with windowing and watermarking techniques
D) Ignore late-arriving data altogether

5. In optimizing join operations, what role does the Catalyst optimizer in Spark play, specifically regarding join strategies?

A) It disables all optimizations by default to provide consistent performance across different datasets.
B) It dynamically selects the most appropriate join strategy based on the query execution plan.
C) It manually requires the developer to specify the join strategy for each operation.
D) It exclusively uses broadcast join for all operations to minimize execution time.

Solutions: