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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Pipeline Orchestration | 20% | - Pipeline Scheduling and Triggers - Workflow Dependencies - Error Handling and Retries - Apache Airflow on CDP |
| Topic 2: Data Processing with Spark | 30% | - Spark SQL and DataFrames - Spark Performance Optimization - DataFrame and Dataset APIs - Spark Core Concepts - Spark Structured Streaming |
| Topic 3: Data Ingestion and Integration | 20% | - Stream Data Ingestion - Batch Data Ingestion - CDC (Change Data Capture) - Data Federation - Data Transformation and ETL |
| Topic 4: CDP Platform Operations | 15% | - Data Lake and Storage - Cluster Management and Monitoring - Cloudera Data Platform Architecture - Cloudera Flow Management |
| Topic 5: Data Quality and Governance | 15% | - Data Validation and Cleansing - Data Lineage - Access Control and Security - Data Catalog and Metadata |
1. You have a PySpark application packaged as 'MyPySparkApp-0. I-py3-none-any.whl'. In your 'app.py', you utilize a function from an external library, 'numpy', listed in your 'requirements.txt'. How should you deploy this application to ensure 'numpy' is available at runtime?
A) Upload both 'app.py' and 'MyPySparkApp-0.1-py3-none-any.whl' and submit using 'spark-submit --py-files MyPySparkApp-0.1-py3-none- any.whl app.py'.
B) Upload 'app.py' only and submit using 'spark-submit app.py'.
C) Upload 'MyPySparkApp-0.1-py3-none-any.whl' only and submit using 'spark-submit --py-files MyPySparkApp-0.1-py3-none-any.whl'.
D) Upload 'app.py' and manually install 'numpy' on all nodes before submitting using 'spark-submit app.py'.
2. When monitoring a PySpark application in Kubernetes, you notice that Executor pods are frequently restarting. What is the most likely cause of this issue?
A) Insufficient memory allocation for Executor pods.
B) Dynamic resource allocation is disabled.
C) Executor pods are not configured to restart upon failure.
D) Too many tasks assigned to each Executor.
3. In the context of Cloudera's SQL engines, what does the presence of a "Broadcast Hash Join" in an Explain Plan suggest about query performance?
A) It suggests that the join operation might be a performance bottleneck for large datasets
B) It implies that no indexing is used in the join operation
C) It indicates an optimal use of network resources
D) It means that the query will execute faster than with any other join method
4. Which of the following best describes the benefit of partition pruning in Spark SQL?
A) It increases the number of partitions to maximize parallelism.
B) It decreases the amount of data shuffled during join operations.
C) It dynamically adjusts partition sizes based on the query execution plan.
D) It selectively processes partitions that meet certain criteria, reducing 1/0.
5. Why is it recommended to use the DataFrame API over RDDs for most data processing tasks in Spark?
A) DataFrames provide more fine-grained control over partitioning and parallelism.
B) DataFrames require less memory and compute resources compared to RDDs.
C) DataFrames automatically optimize queries using the Catalyst optimizer and Tungsten execution engine.
D) RDDs are deprecated and will be removed in future versions of Spark.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: A | Question # 3 Answer: A | Question # 4 Answer: D | Question # 5 Answer: C |
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