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Question 1:

For a system designed to read data from an external database, perform some transformations, and then store the results in a Kafka topic, which approach is most suitable?

  1. Consumer + Producer
  2. Kafka Connect Source
  3. Kafka Connect Sink
  4. Kafka Streams

Response:

The correct answer is 2. Kafka Connect Source.

Explanation: Kafka Connect Source is designed for importing data from external systems into Kafka topics. It can easily read from an external database, and with the appropriate connectors and transformations configured, it can modify the data as required before making it available in a Kafka topic.

Question 2:

When needing to aggregate real-time data from a Kafka topic, compute running totals, and then publish those totals back to another Kafka topic for further analysis, which tool should you use?

  1. Consumer + Producer
  2. Kafka Connect Source
  3. Kafka Connect Sink
  4. Kafka Streams

Response:

The correct answer is 4. Kafka Streams.

Explanation: Kafka Streams is specifically designed for building real-time streaming data pipelines and applications that transform or react to the streams of data. For tasks like aggregating data and computing running totals, Kafka Streams provides the necessary stateful operations and can directly publish the results back to another Kafka topic.

Question 3:

If the objective is to periodically export data from a Kafka topic to a relational database for long-term storage and analysis, which Kafka component would best fulfill this requirement?

  1. Consumer + Producer
  2. Kafka Connect Source
  3. Kafka Connect Sink
  4. Kafka Streams

Response:

The correct answer is 3. Kafka Connect Sink.

Explanation: Kafka Connect Sink is designed to export data from Kafka topics into external systems such as databases, key-value stores, search indexes, and file systems. For scenarios where the goal is to move data from Kafka to a relational database, a Kafka Connect Sink connector can be configured to handle this task efficiently and with minimal coding effort.

Question 4:

A financial institution wants to analyze transaction data in real-time to detect fraudulent activities. The transaction data, which includes sensitive information, is initially stored in a mainframe system. Which approach ensures secure, real-time analysis of this data by a Kafka Streams application while complying with data privacy regulations?

  1. Utilize a mainframe connector with Kafka Connect to ingest the transaction data into a Kafka topic. Apply a Kafka Streams application to anonymize sensitive information in the stream before conducting fraud analysis.
  2. Directly connect the mainframe system to the Kafka Streams application using a custom API, ensuring sensitive data is filtered out during the streaming process.
  3. Employ ksqlDB to directly query the mainframe system, apply data anonymization functions to filter out sensitive information, and then write the sanitized data to a Kafka topic for stream processing.
  4. Implement a batch process to extract transaction data periodically from the mainframe, cleanse the data of sensitive information, and then load the sanitized data into Kafka topics for streaming analysis.

Response:

The correct answer is 1. Utilize a mainframe connector with Kafka Connect to ingest the transaction data into a Kafka topic. Apply a Kafka Streams application to anonymize sensitive information in the stream before conducting fraud analysis.

Question 5:

Your organization aims to implement a real-time recommendation engine for e-commerce users based on their browsing behavior. User session data is considered sensitive and must be anonymized before processing. How can the Kafka ecosystem be leveraged to meet these requirements?

  1. Deploy a Kafka Connect Source Connector to capture session data directly into Kafka, using Stream Processing to anonymize user identifiers before aggregating sessions for recommendation analysis.
  2. Use a custom Kafka Producer application to publish session data to a topic, applying a Stream Processor to anonymize and then process the data for generating recommendations.
  3. Implement a Kafka Connect Sink Connector to store session data into a NoSQL database, with a pre-processor to remove sensitive information before ingestion. Use Kafka Streams to read from the database for analysis.
  4. Create a ksqlDB process to pull session data from source systems, apply anonymization functions within ksqlDB, and output the clean data to a Kafka topic for further processing by the Kafka Streams application.

Response:

The correct answer is 1. Deploy a Kafka Connect Source Connector to capture session data directly into Kafka, using Stream Processing to anonymize user identifiers before aggregating sessions for recommendation analysis.

Question 6:

An organization is implementing a system to monitor and alert on infrastructure health status in real-time. The system collects metrics from various sources, including some that contain proprietary information. Which approach ensures that only non-proprietary, critical health metrics are analyzed and alerted on?

  1. Use Kafka Connect with appropriate Source Connectors for each metric source, configuring the connectors to filter out proprietary information. Process the filtered metrics stream with Kafka Streams for alerting.
  2. Directly stream all metrics into Kafka using custom Producers, then employ a Kafka Streams application to separate proprietary data from non-proprietary data, and analyze the latter for alerting.
  3. Implement a series of ksqlDB statements to ingest metrics into Kafka, applying filtering logic within ksqlDB to remove proprietary information before streaming processing and alerting.
  4. Configure a Kafka Connect Sink Connector to aggregate all metrics into a centralized database, followed by batch processing to remove proprietary information before streaming the data into Kafka for real-time analysis.

Response:

The correct answer is 1. Use Kafka Connect with appropriate Source Connectors for each metric source, configuring the connectors to filter out proprietary information. Process the filtered metrics stream with Kafka Streams for alerting.

Question 7:

A multinational corporation is looking to aggregate sales data from multiple regional systems into a centralized Kafka topic for real-time analysis and reporting. The regional systems vary in technology, including SQL databases and cloud-based storage solutions. Which solution enables the efficient and unified ingestion of these diverse data sources into Kafka?

  1. Deploy Kafka Streams applications near each regional system to collect and forward data to the centralized Kafka topic.
  2. Use Kafka Connect with a mix of Source Connectors suitable for each regional system's technology to ingest data directly into Kafka.
  3. Implement custom Kafka Producers embedded within each regional system to push data to the centralized Kafka topic.
  4. Configure a Kafka Connect Sink Connector for each regional system to replicate data into the centralized Kafka topic.

Response:

The correct answer is 2. Use Kafka Connect with a mix of Source Connectors suitable for each regional system's technology to ingest data directly into Kafka.

Question 8:

An online media platform wishes to analyze user interactions (clicks, views, and comments) in real-time to dynamically adjust content recommendations. The platform generates a high volume of interaction data, necessitating scalable and real-time processing. What architecture best suits this requirement?

  1. Utilize a Kafka Connect Source Connector to ingest interaction data into Kafka, then process this data with Kafka Streams to update content recommendations in real-time.
  2. Directly stream interaction data into Kafka using a custom API, then use ksqlDB to perform real-time analysis and generate content recommendations.
  3. Implement batch processing jobs to periodically analyze interaction data stored in an external database, and then use Kafka to distribute batch analysis results for content recommendation updates.
  4. Configure Kafka Connect Sink Connectors to collect interaction data into a big data platform first, then process the data using external stream processing tools before updating content recommendations.

Response:

The correct answer is 1. Utilize a Kafka Connect Source Connector to ingest interaction data into Kafka, then process this data with Kafka Streams to update content recommendations in real-time.

Question 9:

A utility company monitors a network of IoT devices deployed across an energy grid. The devices send telemetry data (e.g., power usage, system health) every minute. The company wants to aggregate this data for near-real-time monitoring and anomaly detection. Which Kafka-based solution efficiently achieves this goal?

  1. Configure Kafka Connect Sink Connectors to collect telemetry data from the IoT devices into Kafka, followed by a Kafka Streams application to aggregate and analyze the data.
  2. Use Kafka Connect Source Connectors appropriate for the IoT devices' communication protocols to ingest telemetry data into Kafka, then employ Kafka Streams for data aggregation and anomaly detection.
  3. Develop custom Kafka Producers within the IoT devices to send data directly to Kafka topics, then use external tools to pull and analyze the data from Kafka.
  4. Implement a centralized database to collect IoT telemetry data first, then use Kafka Connect Source Connectors to ingest the data from the database into Kafka for further processing.

Response:

The correct answer is 2. Use Kafka Connect Source Connectors appropriate for the IoT devices' communication protocols to ingest telemetry data into Kafka, then employ Kafka Streams for data aggregation and anomaly detection.

Question 10:

A digital marketing platform analyzes user activities to send personalized marketing emails. The platform uses Kafka to stream activity data, with fluctuations in data volume throughout the day. To ensure optimal performance during peak data inflow, what strategy should be employed?

  1. Dynamically adjust the number of partitions in the user activities topic based on incoming data volume.
  2. Scale the Kafka Streams application instances up or down in response to the processing load.
  3. Increase or decrease the number of Kafka Connect Source Connector tasks to match the rate of incoming user activity data.
  4. Modify the replication factor of the user activities topic during high load periods to improve data durability and availability.

Response:

The correct answer is 2. Scale the Kafka Streams application instances up or down in response to the processing load.