Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Platform Operations | - Cluster and workload management
|
| Topic 2: Data Governance and Security | - Security frameworks
|
| Topic 3: Data Ingestion and Integration | - Batch and streaming ingestion
|
| Topic 4: Data Storage and Modeling | - Data modeling
|
| Topic 5: Data Processing and Transformation | - SQL analytics engines
|
Cloudera CDP Data Engineer - Certification Sample Questions:
1. What is a potential downside of overly granular partitioning in Hive tables?
A) Increased data security
B) Reduced query performance due to metadata overhead
C) Simplified data management
D) Automatic data deduplication
2. How can you leverage the Cloudera Data Engineering service to monitor the performance and health of your data pipelines?
A) Utilize custom scripting to collect and analyze pipeline metrics.
B) Utilize the built-in monitoring capabilities of the Cloudera Data Engineering service, including dashboards and alerts.
C) Manually review log files generated by individual pipeline steps.
D) Integrate the service with a separate external monitoring tool.
3. How can you utilize Spark SQL for complex data analysis involving joins and aggregations on large datasets?
A) By directly writing SQL queries against distributed data in Spark.
B) By implementing custom logic in RDDs for data manipulation.
C) By using Spark ML libraries for machine learning tasks on data.
D) Both A and B.
4. Consider the following code snippet:# Sample DataFrame (assuming it exists) df = spark.createDataFrame(...)
# Attempt to explode a nested array column (fix the error)
df_exploded = df.withColumn("items", F.explode(df["items"]))
df_exploded.show()
What is the error in this code, and how can it be fixed?
A) The error is missing parentheses around the column name in the explode function. Fix: F.explode(df("items"))
B) The error is attempting to modify the original DataFrame in-place. Fix: Use a separate variable to store the exploded DataFrame.
C) The error is using withColumn instead of a dedicated method for exploding arrays. Fix: Use df.withColumnExploded. (This function doesn't exist in Spark)
D) There is no error in the code snippet.
5. What Airflow feature allows you to template parts of your DAG to dynamically change based on the execution context?
A) XComs
B) Jinja Templating
C) BranchOperator
D) PythonOperator
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: B | Question # 3 Answer: A | Question # 4 Answer: B | Question # 5 Answer: B |
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