Confluent Certified Developer for Apache Kafka Training (CCDAK)
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M1 — The Confluent Platform and the CCDAK Exam Map
What Confluent adds on top of Apache Kafka and where the boundary sits
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CCDAK format: 90 minutes, proctored, with choice, matching and build-list items
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The six exam domains and their weightings, from development down to testing
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Standing up a Confluent Cloud cluster and a local platform sandbox
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Topic, partition and key design as the first decision in a streaming design
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What the exam leaves out: broker administration and access control policy
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M2 — Producers, Consumers and Delivery Semantics
Producer configuration: acks, retries, batch size, linger and compression
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Partitioning by key for ordering and the ordering boundary that draws
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The consumer poll loop, poll settings and the backpressure it creates
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Offset handling: manual commit, autocommit and the replay it allows
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Idempotent producer, transactions and the read_committed consumer
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Client metrics: record error rate, latency percentiles and throughput
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M3 — Schema Registry and Compatibility Rules
Subjects, naming strategies and why the subject name decides compatibility
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Avro schema evolution: adding a field with a default and a reader version
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Compatibility modes BACKWARD, FORWARD, FULL and NONE in practice
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JSON Schema and Protobuf subjects and their own compatibility behaviour
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Serializers and deserializers for Avro, JSON Schema and Protobuf on the client
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Registering a broken schema and reading the validation error before it ships
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M4 — Kafka Connect on the Confluent Platform
Worker architecture in standalone and distributed mode on a cluster
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Writing a source connector configuration and a sink connector configuration
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Converters and schemas that map a database row onto an Avro record
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Single Message Transforms: rewriting keys, flattening and routing records
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Exactly-once support, dead letter topics and connector error handling
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Managing connectors through the REST API and the command line interface
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M5 — Kafka Streams Topologies
KStream, KTable, GlobalKTable and the stream-table duality in one topology
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Windowing: tumbling, hopping, sliding and session windows with a grace period
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Joins: stream-stream and stream-table, and the co-partitioning requirement
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State stores, changelog topics and what state costs in a local store
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Topology optimisation: caching, stateful operator cost and rebalancing
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Testing a topology with the test driver before it reaches a real cluster
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M6 — Exactly-Once, Confluent Cloud and Security
The transaction coordinator, producer id and sequence number behind EOS
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Where exactly-once holds and where the guarantee stops at a topic boundary
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Confluent Cloud: dedicated clusters, serverless and shared infrastructure
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Networking: private links, VPC peering and the transit gateway path
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Access control with principals, role bindings and service accounts
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Connecting to Confluent Cloud with the command line tools and an API key
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M7 — Observability, Testing and Development Depth
Instrumenting a producer and consumer with JMX and the client metrics
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Consumer lag as the first question: where it comes from and when it matters
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Unit testing with a mock producer and a mock consumer
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Integration testing against a broker with an embedded or containerised cluster
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Turning a business requirement into a topic, a key and a schema design
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Reviewing code for delivery semantics, error handling and rebalance risk
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M8 — Capstone: A Confluent Streaming Service and the Exam Paper
Publish an order stream with Avro schemas registered under two subjects
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Build a Connect pipeline that moves database change events into Kafka
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Write a Streams topology that aggregates revenue per account per hour
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Enable exactly-once processing and show the duplicate is gone on replay
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Deploy to a Confluent Cloud cluster with access rules and lag alerts
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Sit a full 90-minute CCDAK practice paper and score it by domain
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