MCQs on Enterprise-Level Applications and Extensions | Apache Flink MCQs Questions
Apache Flink is a powerful stream processing framework widely used for real-time analytics and big data applications. This guide provides 30 MCQs covering critical topics like building enterprise-level Flink applications, implementing machine learning pipelines, leveraging Flink SQL, connectors, and security best practices. Perfect for interview preparation and skill enhancement!
MCQs:
1. Building Flink Applications for Real-Time Analytics
- Which of the following is a key feature of Apache Flink for real-time analytics? a) Batch Processing
b) Stateful Stream Processing
c) Data Warehousing
d) OLAP Queries - In Flink, the default fault-tolerance mechanism is based on:
a) Kafka Offsets
b) Checkpointing
c) Data Skew
d) Task Recovery - What is the primary abstraction used in Flink for stream processing?
a) DataFrame
b) DataSet
c) DataStream
d) SQL Query - Flink’s event-time processing enables:
a) Processing data in any order
b) Processing data as it arrives
c) Time-zone adjustments in real time
d) Late data handling with watermarks - Which deployment mode is suitable for running Flink applications on clusters?
a) Standalone Mode
b) Docker Mode
c) Kubernetes Mode
d) Yarn Mode
2. Implementing Machine Learning Pipelines with FlinkML
- What is the primary purpose of FlinkML?
a) Batch Analytics
b) Machine Learning Pipelines
c) Graph Processing
d) File Storage - FlinkML allows integration with which machine learning library?
a) TensorFlow
b) Scikit-learn
c) Apache Spark MLlib
d) All of the above - In FlinkML, pipelines are typically composed of:
a) Preprocessors and Algorithms
b) SQL Queries
c) Flink Sinks
d) HDFS Files - Which API is used to implement FlinkML pipelines?
a) Table API
b) Core API
c) Machine Learning API
d) DataStream API - Flink’s iterative processing feature is used in ML for:
a) Model Deployment
b) Parallelism Tuning
c) Algorithm Training
d) Feature Extraction
3. Streaming SQL with Flink SQL and Table API
- Flink SQL supports which query language?
a) PostgreSQL Syntax
b) ANSI SQL
c) NoSQL
d) MySQL Queries - The Table API in Flink can operate on:
a) Only Static Tables
b) Only Dynamic Tables
c) Both Static and Dynamic Tables
d) None of the above - What is the purpose of a catalog in Flink SQL?
a) To store schema metadata
b) To manage data partitions
c) To handle user authentication
d) To optimize query performance - Which function handles late data in Flink SQL?
a) OUT_OF_ORDER()
b) EVENT_TIME()
c) WATERMARK()
d) TUMBLE() - Flink SQL Table API is written in:
a) Python
b) Java/Scala
c) C++
d) PHP
4. Working with Flink’s Connectors and Libraries
- Which connector is used for integrating Flink with Kafka?
a) KafkaSink
b) KafkaSource
c) FlinkKafkaConnector
d) KafkaConnect - Flink’s FileSystem connector supports:
a) Reading JSON files
b) Writing Parquet files
c) Reading and writing Avro files
d) All of the above - The JDBC connector in Flink enables:
a) Batch data ingestion
b) Real-time streaming to SQL databases
c) Query execution on HDFS
d) Data replication - What library supports graph analytics in Flink?
a) FlinkML
b) Gelly
c) Blink
d) ConnectX - Which Flink library helps with handling state in streams?
a) StateBackends
b) DataStream API
c) Checkpointing
d) Queryable State
5. Security Best Practices and Encryption
- Which encryption protocol is recommended for secure communication in Flink?
a) HTTPS
b) SSL/TLS
c) FTP
d) RSA - Flink’s security features support:
a) Authentication only
b) Authorization only
c) Both Authentication and Authorization
d) None of the above - Secure access to Flink’s REST API requires:
a) OAuth Tokens
b) API Keys
c) SSL Certificates
d) All of the above - Role-based access control in Flink is implemented using:
a) LDAP
b) OAuth
c) RBAC Modules
d) Kerberos - What is the purpose of Flink’s JobManager failover protection?
a) To encrypt state data
b) To ensure job reliability during failures
c) To manage key-value pairs
d) To authorize user queries
6. Extending Flink with Custom Plugins and Libraries
- Custom plugins in Flink are implemented to:
a) Add new functionalities
b) Fix bugs in core libraries
c) Replace connectors
d) Automate deployment - Flink plugins are typically written in:
a) Java
b) Scala
c) Python
d) Java or Scala - The core interface for developing custom sinks in Flink is:
a) SinkFunction
b) SinkProvider
c) OutputManager
d) StreamSink - Flink supports external plugin deployment via:
a) JAR files
b) Python Wheels
c) Bash Scripts
d) Cloud Functions - What is the advantage of extending Flink with custom libraries?
a) Reduced latency
b) Higher scalability
c) Tailored processing needs
d) All of the above
Answers:
| QNo | Answer (Option with text) |
|---|---|
| 1 | b) Stateful Stream Processing |
| 2 | b) Checkpointing |
| 3 | c) DataStream |
| 4 | d) Late data handling with watermarks |
| 5 | d) Yarn Mode |
| 6 | b) Machine Learning Pipelines |
| 7 | d) All of the above |
| 8 | a) Preprocessors and Algorithms |
| 9 | b) Core API |
| 10 | c) Algorithm Training |
| 11 | b) ANSI SQL |
| 12 | c) Both Static and Dynamic Tables |
| 13 | a) To store schema metadata |
| 14 | c) WATERMARK() |
| 15 | b) Java/Scala |
| 16 | d) KafkaConnect |
| 17 | d) All of the above |
| 18 | b) Real-time streaming to SQL databases |
| 19 | b) Gelly |
| 20 | d) Queryable State |
| 21 | b) SSL/TLS |
| 22 | c) Both Authentication and Authorization |
| 23 | d) All of the above |
| 24 | d) Kerberos |
| 25 | b) To ensure job reliability during failures |
| 26 | a) Add new functionalities |
| 27 | d) Java or Scala |
| 28 | a) SinkFunction |
| 29 | a) JAR files |
| 30 | d) All of the above |