Confluent CCDAK Certification Exam Sample Questions

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Confluent CCDAK Sample Questions:

01. An application renders web content based on how the current user matches various demographic categories. When the user accesses a page, a page-view event is written to a Kafka topic; the user-profile database is also ingested into a Kafka topic using a Kafka connector.
How should the page-view events and the user-profile data be produced into their respective topics so the application can easily associate each page-view event with the corresponding user profile?
a) Stand up a producer and a Kafka connector on each client machine and assign these machines a subset of page view and corresponding user profile data
b) Configure the two topics so that they are written to the same Kafka cluster
c) Configure the two topics so they are co-partitioned
d) Write a consumer application that processes all records in both the page view and user profile topics and allow it to associate these records as needed
 
02. What does a sustained increase in end-to-end processing latency most likely indicate?
a) Higher replication factor
b) Reduced partition count
c) Increased topic retention
d) Slower downstream processing
 
03. An application uses a Kafka source connector to produce records into a topic, and the load varies with daily customer purchases. Which approach best accommodates this variable load?
a) Increase and decrease the number of Kafka Connect workers based on current load
b) Increase and decrease the number of brokers in the Kafka cluster based on current load
c) Increase and decrease the number of connector tasks based on current load
d) Increase and decrease the number of topic partitions based on current load
 
04. Which component is responsible for exposing Kafka broker metrics via JMX?
a) Kafka Admin client
b) Kafka Connect worker
c) Kafka Streams runtime
d) Kafka broker process
 
05. Your cluster has 3 brokers. Four producer clients send to the driver topic, which has 12 partitions, and their produce requests are returning timeout exceptions. What change would most reduce these exceptions?
a) Increase the replication factor of the “driver” topic to scale out the produce requests
b) Increase the number of brokers from 3 to 4 and distribute the 12 partitions equally across the 4 brokers
c) Increase the number of producer clients from 4 to 6
d) Increase the number of “driver” topic partitions from 12 to 15
 
06. What is the primary purpose of testing Kafka producers and consumers independently from a running cluster?
a) To isolate application logic from infrastructure
b) To improve message throughput
c) To reduce broker resource usage
d) To simplify schema evolution
 
07. Your organization has legacy data stored in a relational database management system (RDBMS). You must ingest this legacy data into Kafka and transform it into multiple formats so it can be processed by the various microservices the organization now relies on.
The development team assigned this task has experience using SQL procedures from managing data in the legacy RDBMS. Considering these factors, which solution best satisfies the requirement?
a) Use the JDBC source connector to produce the legacy data to multiple Kafka topics. For each destination Kafka topic, include single message transforms as needed to transform the data to meet the requirement for that topic.
b) Create a new database schema in the RDBMS that meets the requirements of the microservices and transform the legacy data from the existing schema to the new schema using SQL procedures. Use the Java database connectivity (JDBC) source connector to produce the legacy data to a Kafka topic.
c) Use the Java database connectivity (JDBC) source connector to produce the legacy data to a Kafka topic. Use ksqlDB to transform the data as needed and direct the resulting data to new Kafka topics.
d) Use the Java database connectivity (JDBC) source connector to produce the legacy data to a Kafka topic. Use a Kafka Streams application to transform the data as needed.
 
08. Which components are required for Kafka Connect to serialize and deserialize records using a schema-based format such as Avro?
(Choose two.)
a) Schema Registry
b) Tasks
c) Converters
d) Connectors
 
09. A Kafka Streams application needs customer profile data held in a relational database, but that data contains sensitive PII. Which solution gives the Streams application access to the customer profile data with the PII removed?
a) Use the JDBC source connector to produce the customer profile data to a Kafka topic. Include a single message transform masking operation in the connector configuration to mask the PII data before it is written to the Kafka topic. The Kafka streams application can then consume that topic.
b) Use the Java database connectivity (JDBC) source connector to produce the customer profile data to a Kafka topic. Use a Kafka streams application to process and remove the PII from each customer profile data record as it is written to the initial Kafka topic. The Kafka streams application can then consume that topic.
c) Write a custom Kafka producer to access the customer profile data, remove the customer PII from each record, and produce the filtered record to a Kafka topic. The Kafka streams application can then consume that topic.
d) Use a ksqlDB application to read the customer profile data in the RDBMS, filter the PII data from each record, and write the filtered profile data to a Kafka topic. The Kafka streams application can then consume that topic.
 
10. Your organization needs to enrich incoming sensor data (environmental readings) with sensor device profile data such as location and device model, which is held in a database.
Which approach best meets this requirement?
a) Produce the data coming from the sensor devices into a Kafka topic using the MQTT connector and as an intermediate step, enrich each sensor data record using the JDBC sink connector to access the sensor device profile data combined with multiple SMTs.
b) Write a Kafka producer client that captures the sensor device data using the MQTT proxy and enriches each record using sensor device profile data that it directly accesses from the source database. The enriched records will then be produced into a Kafka topic.
c) Produce the data coming from the sensor devices into a Kafka topic using the MQTT proxy. Produce the sensor device profile data into a second Kafka topic using the JDBC source connector. Write a Kafka streams application to enrich the sensor data records with the sensor device profile data and write this out to a third Kafka topic.
d) Produce the data coming from the sensor devices into a Kafka topic using the Message Queuing Telemetry Transport (MQTT) proxy and as an intermediate step, enrich each sensor data record using the Java database connectivity (JDBC) source connector to access the sensor device profile data combined with multiple single message transforms (SMT).

Answers:

Question: 01
Answer: c		 Question: 02
Answer: d		 Question: 03
Answer: a		 Question: 04
Answer: d		 Question: 05
Answer: b
Question: 06
Answer: a		 Question: 07
Answer: c		 Question: 08
Answer: a, c		 Question: 09
Answer: a		 Question: 10
Answer: c

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