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Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Data Ingestion and Integration | - Batch and streaming ingestion
|
| Topic 2: Platform Operations | - Cluster and workload management
|
| Topic 3: Data Governance and Security | - Security frameworks
|
| Topic 4: Data Processing and Transformation | - SQL analytics engines
|
| Topic 5: Data Storage and Modeling | - Data lake architecture
|
Cloudera CDP Data Engineer - Certification Sample Questions:
You need to design your Airflow DAG for data quality checks to be scalable and manageable as the number of datasets and checks grows. How can you achieve this?
- A. Leverage external configuration files (e.g., YAML or JSON) to define data quality checks and associated parameters.
- B. Implement a modular design using sub-DAGs, where each sub-DAG encapsulates the data quality checks for a specific dataset.
- C. Utilize Airflow variables to store configuration details like data source paths and check thresholds.
- D. Hardcode all data quality checks and data sources directly within the DAG code.
Correct Answer: A,B 🗳️
Explanation: Only visible for Actual4Exams members. You can sign-up / login (it's free).
For a Hive table that is both partitioned and bucketed, what considerations must be taken into account to optimize a join query involving this table?
- A. Ensuring the join columns are neither partitioned nor bucketed as it may lead to increased complexity.
- B. Bucketing considerations are irrelevant in the context of join queries, with partitioning being the sole factor impacting performance.
- C. Both the partitioning and bucketing columns should align with the join columns where possible to maximize the efficiency of data retrieval.
- D. The join should exclusively rely on the partitioned columns, ignoring the bucketed columns for optimal performance.
Correct Answer: C 🗳️
Explanation: Only visible for Actual4Exams members. You can sign-up / login (it's free).
For scripting and automation purposes, how can Cloudera's CLI tools be integrated into administrative workflows?
- A. Using exclusively manual CLI commands for each task without automation.
- B. By creating custom plugins for web browsers to manage Cloudera services.
- C. Incorporating CLI commands into shell scripts or automation tools like Ansible, Chef, or Puppet.
- D. CLI tools cannot be integrated into administrative workflows; they are only for interactive use.
Correct Answer: C 🗳️
Explanation: Only visible for Actual4Exams members. You can sign-up / login (it's free).
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. RDDs are deprecated and will be removed in future versions of Spark.
- C. DataFrames require less memory and compute resources compared to RDDs.
- D. DataFrames automatically optimize queries using the Catalyst optimizer and Tungsten execution engine.
Correct Answer: D 🗳️
Explanation: Only visible for Actual4Exams members. You can sign-up / login (it's free).
What is a primary benefit of schema-on-read compared to schema-on-write in big data processing frameworks?
- A. It significantly reduces the computational overhead during data ingestion.
- B. It requires all data to conform to a rigid schema before storage.
- C. It allows for greater flexibility in data formats and structures at the time of analysis.
- D. It mandates manual intervention for each data read operation.
Correct Answer: C 🗳️
Explanation: Only visible for Actual4Exams members. You can sign-up / login (it's free).
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