Free Practice Questions for Cloudera Data Engineer (CLOUDERA-DATA-ENGINEER) Certification
Study with 349 exam-style practice questions designed to help you prepare for the Cloudera Data Engineer (CLOUDERA-DATA-ENGINEER).
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Exam Details
Key information about Cloudera Data Engineer (CLOUDERA-DATA-ENGINEER)
- Multiple choice
55%
online, proctored
Data Engineer professional, who knows how to work proficiently designing, developing and optimizing data workflows using Cloudera tools. Strong grasp of data modeling for efficient storage, including formats, partitioning and schema design, and Apache Iceberg. Expertise in performance optimization, bottleneck identification, query tuning and resource efficiency. Proficient in security configuration, monitoring, troubleshooting and cloud integration for Cloudera clusters using mainly Spark and Airflow.
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90
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Exam Topics & Skills Assessed
Skills measured (from the official study guide)
Domain 1: Spark
Subdomain 1.1: Fundamentals on Spark over Kubernetes
Understand the fundamentals of running Spark on Kubernetes, including architecture, deployment, and resource management.
Subdomain 1.2: Work with DataFrames
Proficiently work with DataFrames in Spark for data manipulation, transformations, and analysis.
Subdomain 1.3: Understand Distribute Processing
Understand distributed processing concepts in Spark, including partitioning, shuffling, and execution across clusters.
Subdomain 1.4: Implement Hive and Spark Integration
Implement integration between Hive and Spark, enabling seamless querying and data processing across both systems.
Subdomain 1.5: Understand Distributed Persistence
Understand distributed persistence mechanisms in Spark, including caching, checkpointing, and storage levels.
Domain 2: Airflow
Subdomain 2.1: Implement incremental extraction in Apache Airflow from source system
Implement incremental data extraction from source systems using Apache Airflow to efficiently capture only changed data.
Subdomain 2.2: Use Apache Airflow to schedule ETL pipelines
Use Apache Airflow to schedule and orchestrate ETL pipelines, ensuring reliable and timely data processing.
Subdomain 2.3: Use Apache Airflow to schedule quality checks
Use Apache Airflow to schedule data quality checks, integrating validation steps into workflows.
Subdomain 2.4: Work with DAGs
Work with Directed Acyclic Graphs (DAGs) in Airflow to define, manage, and monitor complex workflows.
Domain 3: Performance Tuning
Subdomain 3.1: Know Basic tools in (Spark) Performance Tuning
Know and utilize basic tools for performance tuning in Spark, such as Spark UI and logs, to identify bottlenecks.
Subdomain 3.2: Understand Optimization Framework and Explain plans
Understand the optimization framework in Spark, including Catalyst optimizer, and interpret explain plans to analyze query execution.
Subdomain 3.3: Understand Inferring Schemas
Understand schema inference in Spark, its impact on performance, and best practices for explicit schema definition.
Subdomain 3.4: Work with Improving Join Performance Leverage Caching Data for Reuse
Work on improving join performance by leveraging techniques such as broadcast joins and caching data for reuse across operations.
Subdomain 3.5: Work with Partitioned and Bucketed Tables
Work with partitioned and bucketed tables to optimize data layout and query performance in Spark.
Domain 4: Deployment
Subdomain 4.1: Use the API and CLI
Use the Cloudera Data Platform API and command-line interface (CLI) for managing and deploying data engineering workloads.
Subdomain 4.2: Work in the Data Engineering Service
Work within the Cloudera Data Engineering service to create, manage, and monitor Spark jobs and Airflow workflows.
Domain 5: Iceberg
Subdomain 5.1: Understand Iceberg
Understand Apache Iceberg, its table format, features like time travel and partitioning, and its role in data lakehouse architectures.
Techniques & products