Why tuning handles production load in Kafka - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to set the correct number of partitions for a Kafka topic.

Kafka

kafka-topics --create --topic my-topic --partitions [1] --replication-factor 3 --bootstrap-server localhost:9092

Drag options to blanks, or click blank then click option'

A10

B1

C0

D-1

Attempts:

3 left

💡 Hint

Common Mistakes

Setting partitions to 0 or negative values causes errors.

Using only 1 partition limits parallelism.

Setting partitions to 10 allows Kafka to handle more load by distributing messages across multiple partitions.

2fill in blank

medium

Complete the code to increase the Kafka producer batch size for better throughput.

Kafka

producerConfig.put("batch.size", [1]);

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A"abc"

B"16384"

C"0"

D"-100"

Attempts:

3 left

💡 Hint

Common Mistakes

Using negative or zero batch size disables batching.

Using non-numeric values causes errors.

Setting batch.size to 16384 bytes allows the producer to send larger batches, improving throughput under load.

3fill in blank

hard

Fix the error in the consumer configuration to properly set the fetch max bytes.

Kafka

consumerConfig.put("fetch.max.bytes", [1]);

Drag options to blanks, or click blank then click option'

A"0"

B"-1"

C"one_mb"

D"1048576"

Attempts:

3 left

💡 Hint

Common Mistakes

Using negative or zero disables fetching.

Using non-numeric strings causes errors.

Setting fetch.max.bytes to 1048576 (1 MB) allows the consumer to fetch larger batches, improving performance under load.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps topic names to their partition counts if partitions are greater than 3.

Kafka

partitions_map = {topic: [1] for topic, partitions in topics.items() if partitions [2] 3}

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Apartitions

Btopic

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using topic as value instead of partitions.

Using '<' instead of '>' in condition.

This comprehension creates a dictionary with topics having more than 3 partitions, mapping topic names to their partition counts.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase topic names to their partition counts if partitions are less than or equal to 5.

Kafka

filtered = {{ [1]: [2] for [3], partitions in topics.items() if partitions <= 5 }}

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Atopic.upper()

Bpartitions

Ctopic

Dpartitions.upper()

Attempts:

3 left

💡 Hint

Common Mistakes

Using partitions.upper() which is invalid.

Swapping key and value positions.

This comprehension maps uppercase topic names to their partition counts for topics with 5 or fewer partitions.