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| Section | Weight | Objectives |
|---|---|---|
| Developing with Kafka Producers | 15-20% | - Serialization (JSON, Avro, Protobuf) - Partitioning Strategies - Producer Configuration and Tuning - Producer Interceptors - Exactly-Once Semantics - Idempotent Producers - Error Handling and Retry Logic |
| Installing and Configuring Kafka | 5-10% | - Partition Assignment Strategies - ZooKeeper vs KRaft Mode - Topic Creation and Configuration - Broker Configuration Parameters |
| Developing with Kafka Consumers | 15-20% | - Consumer Groups and Rebalancing - Commit Strategies (Auto, Manual, Cooperative) - Consumer Configuration and Tuning - Multi-threaded Consumers - Offset Management - Deserialization - Consumer Interceptors |
| Schema Management | 5-10% | - Protobuf and JSON Schemas - Schema Registration and Retrieval - Schema Registry Architecture - Avro Schemas - Schema Evolution and Compatibility |
| Kafka Fundamentals | 10-15% | - Producer and Consumer Basics - Topics, Partitions, and Offsets - Replication Factor and ISR - Log Segments and Retention - Brokers and Clusters |
| Kafka Streams | 20-25% | - Interactive Queries - Exactly-Once Processing - Windowed Operations (Tumbling, Hopping, Session) - Testing Kafka Streams Applications - State Stores and Processors - Transformation Operations (map, filter, join, aggregate) - Streams Architecture and Topology |
| Monitoring and Operations | 5-10% | - Log Compaction - Cross-region Replication - Consumer Lag and Health Checks - Kafka Metrics and Monitoring - Disaster Recovery |
| Kafka Connect | 10-15% | - Converters and Transforms - Offset Management in Connect - Connect Architecture (Workers, Connectors, Tasks) - Single Message Transformations (SMT) - Connector Configuration - Source and Sink Connectors |
| Security | 5-10% | - Securing Schema Registry - Authorization (ACLs) - Authentication (SASL, SSL/TLS) - Encryption in Transit |
>> Valid CCDAK Test Syllabus <<
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NEW QUESTION # 91
You use Kafka Connect with the JDBC source connector to extract data from a large database and push it into Kafka.
The database contains tens of tables, and the current connector is unable to process the data fast enough.
You add more Kafka Connect workers, but throughput doesn't improve.
What should you do next?
Answer: D
Explanation:
Increasing tasks.max allows the connector to create multiple tasks, each responsible for processing different tables or table partitions. If only one task is used, adding more workers has no effect.
From Kafka Connect JDBC Connector Docs:
"Use tasks.max to configure how many tasks are created for the connector. Each task can process a subset of the data." A and C help only if the bottleneck is in Kafka, not Connect.
D is a large architectural change, not the first step.
Reference: Kafka Connect JDBC Connector Configuration
NEW QUESTION # 92
(You have a Kafka consumer in production actively reading from a critical topic from which multiple other applications are consuming.
You have a new requirement to update the offset of your consumer to start reading from the beginning of the topic.
Which action would you take?)
Answer: D
Explanation:
The Apache Kafka documentation states that consumer offsets are stored per consumer group and must be explicitly reset if an application needs to reprocess data from an earlier position. The officially supported and safest way to reset offsets for an existing consumer group is by using the kafka-consumer-groups CLI tool with the --reset-offsets option and the --to-earliest flag.
Setting auto.offset.reset=earliest (Option D) only applies when no committed offset exists for a partition.
Since this consumer is already running in production, offsets are already committed, making this option ineffective. Option C does not change offsets because Kafka identifies consumers by group ID, not application instance. Option B is extremely dangerous and incorrect, as setting retention.ms=0 deletes data for all consumers and impacts other applications.
Therefore, using the CLI to reset offsets for the specific consumer group ensures controlled, auditable, and targeted offset management without affecting other consumers or the topic itself. This approach is explicitly documented in Kafka's consumer group administration tools.
NEW QUESTION # 93
(You are configuring a source connector that writes records to an Orders topic.
You need to send some of the records to a different topic.
Which Single Message Transform (SMT) is best suited for this requirement?)
Answer: C
Explanation:
According to the official Apache Kafka Connect documentation, RegexRouter is the SMT specifically designed to dynamically change the destination topic name of records produced by a connector. It works by applying a regular expression to the original topic name and rewriting it to a new topic name.
This makes RegexRouter the correct choice when some records must be routed to a different topic, typically in combination with connector-level logic, predicates, or multiple connectors. It is commonly used for topic renaming, topic versioning, or routing records to alternate topics.
InsertField (Option B) only adds metadata fields (such as topic, partition, or timestamp) to the record payload and does not affect routing. TombstoneHandler (Option C) is used to manage null-value records, especially with compacted topics. HeaderFrom (Option D) copies fields into headers but does not change the target topic.
Therefore, RegexRouter is the only SMT that directly supports changing the output topic, as documented in the Kafka Connect SMT reference.
NEW QUESTION # 94
(A stream processing application tracks user activity in online shopping carts, including items added, removed, and ordered throughout the day for each user.
You need to capture data to identify possible periods of user inactivity.
Which type of Kafka Streams window should you use?)
Answer: B
Explanation:
According to the official Apache Kafka Streams documentation, session windows are specifically designed to model periods of activity separated by gaps of inactivity. A session window groups events that occur close together in time and closes when no new events arrive within a defined inactivity gap.
In this use case, tracking user shopping cart behavior requires detecting when a user becomes inactive (for example, stops adding or removing items). Session windows naturally represent this pattern by creating a new window when activity resumes after a period of silence. This makes them ideal for modeling user sessions, cart abandonment detection, and inactivity analysis.
Hopping and tumbling windows (Options B and C) are fixed-time windows and are not well-suited for detecting inactivity, as they segment data based on clock time rather than behavior. Sliding windows are primarily used for continuous joins and do not explicitly model inactivity gaps.
Therefore, session windows are the correct and officially recommended choice for identifying periods of user inactivity in Kafka Streams applications.
NEW QUESTION # 95
Match each configuration parameter with the correct option.
To answer choose a match for each option from the drop-down. Partial
credit is given for each correct answer.
Answer:
Explanation:
Explanation:
Correct Matches
* path.format#Only valid for some Source Connectors
* value.converter#Valid for all Source Connectors
* input.path#Only valid for some Source Connectors
* errors.retry.timeout#Valid for all Source Connectors
* tasks.max#Valid for all Source Connectors
* path.formatThis is a connector-specific configuration, commonly used by file-based or storage-based source connectors (for example, HDFS, S3, or FilePulse connectors). It isnot part of the Kafka Connect framework itself, so it is only valid for some source connectors.
* value.converterThis is aworker-level configurationthat defines how record values are converted (JSON, Avro, Protobuf, etc.). It applies toall connectorsrunning on the worker unless overridden at the connector level.
* input.pathThis configuration is specific to file-based source connectors (e.g., FileStreamSourceConnector). It isnot universally supported, so it is only valid for some source connectors.
* errors.retry.timeoutThis is part of Kafka Connect'serror handling framework, introduced to support retries and tolerance of transient errors. It is ageneric Connect configurationand valid for all connectors.
* tasks.maxThis is amandatory, generic connector configurationthat controls the maximum number of tasks a connector may create. It is valid forall source (and sink) connectors.
NEW QUESTION # 96
......
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