Apache Flink Practice Exam
Apache Flink Practice Exam
About Apache Flink Exam
The Apache Flink Exam is designed for professionals and developers aiming to master real-time stream processing and data analytics using Apache Flink. Flink is a powerful, open-source stream-processing framework for distributed, high-performing, and always-available applications. This exam validates your ability to build, deploy, and optimize large-scale data pipelines using Flink’s core APIs, data streaming, and stateful computations.
Who should take the Exam?
This exam is ideal for:
- Data engineers working on real-time analytics and stream processing
- Big data professionals transitioning from batch to streaming architectures
- Software developers building fault-tolerant, scalable applications
- ETL and pipeline architects working in distributed data systems
- Professionals preparing for careers in real-time data engineering
Skills Required
- Familiarity with Java, Scala, or Python
- Understanding of distributed systems and stream processing
- Basic knowledge of Kafka, Hadoop, or similar big data technologies
Knowledge Gained
- Building real-time and batch data pipelines with Apache Flink
- Implementing time-windowed and event-driven data computations
- Managing application state and fault tolerance
- Deploying and monitoring Flink applications on clusters
Course Outline
The Apache Flink Exam covers the following topics -
Domain 1 – Introduction to Apache Flink
- Flink architecture and ecosystem
- Batch vs. stream processing
- Setting up the development environment
Domain 2 – Flink APIs and Programming Model
- Understanding the DataStream and DataSet APIs
- Using Flink with Java, Scala, and Python
- Functional transformations and operators
Domain 3 – Stream Processing Concepts
- Working with event time, processing time, and ingestion time
- Watermarks and out-of-order data handling
- Using windowing techniques: tumbling, sliding, session windows
Domain 4 – State Management
- Understanding keyed and operator state
- Using state backends and savepoints
- Ensuring consistency and checkpointing
Domain 5 – Connectors and Integrations
- Integrating with Kafka, JDBC, Hive, HDFS, Elasticsearch
- Consuming and producing data from/to external systems
- Using Flink SQL and Table API
Domain 6 – Fault Tolerance and Reliability
- Configuring checkpointing and recovery
- Exactly-once and at-least-once semantics
- Handling failure scenarios
Domain 7 – Flink SQL and Table API
- Declarative data processing with Flink SQL
- Using catalogs, views, and table environments
- Integrating with BI and visualization tools
Domain 8 – Deployment and Operations
- Deploying Flink on YARN, Kubernetes, and standalone clusters
- Scaling Flink applications and managing resources
- Using Flink Dashboard for monitoring and metrics
Domain 9 – Performance Optimization
- Memory management and garbage collection
- Task parallelism and resource tuning
- Best practices for efficient stream processing
Domain 10 – Use Cases and Industry Applications
- Real-time fraud detection
- Clickstream analysis and user behavior tracking
- ETL pipelines and log data processing
