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Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Integration & Optimization | 5% | - Troubleshooting
- 1. Resource management
- 2. Bottleneck identification
- Hive & Spark Integration
- 1. Cross-engine query execution
|
| Data Storage & Modeling | 22% | - Distributed Persistence
- 1. HDFS & cloud storage integration
- 2. Data locality & access patterns
- Apache Iceberg
- 1. Table management & ACID compliance
- 2. Partitioning & layout optimization
- Data Formats & Storage
- 1. Columnar formats (Parquet, ORC)
- 2. Schema design & evolution
|
| Apache Spark Development & Processing | 48% | - Performance Optimization
- 1. Job tuning, partitioning & bucketing
- 2. Caching & persistence strategies
- Spark Architecture & Execution Model
- 1. Distributed data processing concepts
- 2. Spark application lifecycle
- Spark SQL & DataFrames
- 1. DataFrame/Dataset API usage
- 2. Query optimization & execution plans
- Spark Streaming & Structured Streaming
- 1. Micro-batch & stream processing
- 2. Checkpointing & fault tolerance
|
| Deployment & Operations | 10% | - CDP Data Engineering Service
- 1. Environment configuration
- 2. API & CLI usage
- Security & Governance
- 1. Data lineage & compliance
- 2. Access control & authentication
|
| Workflow Orchestration | 15% | - Pipeline Development
- 1. End-to-end data pipeline design
- 2. Workflow monitoring & alerting
- Apache Airflow
- 1. DAG design & scheduling
- 2. Task dependencies & error handling
- 3. Incremental data extraction
|
Cloudera CDP Data Engineer - Certification Sample Questions:
1. You encounter an error message stating "Failed to find persisted data for RDD" in your Spark application. What are the potential causes and how can you troubleshoot them?
A) All of the above
B) Spark encountered a network issue and couldn't access the persisted data
C) The data might have been corrupted or deleted from the storage location
D) The RDD was never persisted, or the storage level was set incorrectly
2. In the context of Hive, what mechanism ensures that data is evenly distributed across buckets?
A) Manual data insertion scripts
B) Natural key distribution
C) External data balancing tools
D) A hash function applied to the bucketing column
3. You're facing a schema mismatch between a Spark DataFrame and a Hive table when trying to write the DataFrame to the table. What are the potential causes and how can you address them?
A) Modify the Spark DataFrame schema to match the Hive table schema manually
B) Use HiveQL's ALTER TABLE statement to modify the Hive table schema
C) Leverage Spark SQL's schema inference capabilities to automatically adjust the DataFrame schema
D) Ignore the schema mismatch and write the data anyway, potentially leading to data corruption
4. You're building a Spark application that involves complex iterative data processing. Which option allows you to efficiently access and update intermediate results between iterations?
A) Implement custom data structures for managing intermediate data
B) Leverage Spark's in-memory caching capabilities with rdd.cache()
C) Store intermediate results in temporary tables using Spark SQL
D) Use Spark's broadcast variables for frequently accessed data across iterations
5. Considering Hive's optimization mechanisms, under which scenario might partition pruning fail to improve query performance?
A) When querying data using the exact partition key in the WHERE clause
B) When the partitioned table contains a small number of partitions
C) When querying data using a non-partition column as a filter
D) When the table is partitioned on a column frequently used in query filters
Solutions:
Question # 1 Answer: A | Question # 2 Answer: D | Question # 3 Answer: C | Question # 4 Answer: B | Question # 5 Answer: C |