Which of the following components is NOT part of an event in Kafka? In the IoT context of smart home energy management, who are the producers of the Kafka system? What does it mean... Which of the following components is NOT part of an event in Kafka? In the IoT context of smart home energy management, who are the producers of the Kafka system? What does it mean for a Kafka topic to be partitioned in the context of the 'energy consumption' topic? Which of the following statements best describes the role of consumers in the Kafka system for the smart home energy management example? In a dataset containing customer information, several attributes such as age, income, and purchase history have missing values. When analyzing this data for a marketing campaign, which method is most suitable for addressing the missing values in the numerical attributes to ensure a more accurate analysis? You developed a machine learning model to predict house prices based on specific neighborhood attributes. The model perfectly predicts the prices of houses in your training dataset but performs poorly on a separate test dataset of houses in a different neighborhood. What does this imply about your model?
Understand the Problem
The question is asking about various aspects of Kafka and machine learning, specifically regarding data management and analysis techniques. It involves identifying components of Kafka, understanding the role of consumers in data systems, handling missing data in datasets, and evaluating a machine learning model's performance based on training data.
Answer
1) Event key, 2) The smart meters, 3) Split across brokers, 4) Consumers read and process, 5) Use a constant value, 6) Overfitting.
- Event key, 2) The smart meters that send energy usage data, 3) The topic can split across multiple Kafka brokers for load balancing, 4) Consumers read and process events, 5) Use a constant value for all missing entries, 6) The model is overfitting because it captures the noise in training data.
Answer for screen readers
- Event key, 2) The smart meters that send energy usage data, 3) The topic can split across multiple Kafka brokers for load balancing, 4) Consumers read and process events, 5) Use a constant value for all missing entries, 6) The model is overfitting because it captures the noise in training data.
More Information
Kafka's versatility stems from its ability to manage data streams efficiently through partitioning and its producer-consumer architectures. Missing data in analytics often require careful handling to maintain data integrity.
Tips
Common mistakes include misunderstanding Kafka architecture and mishandling missing data, which can lead to inaccurate analysis or system inefficiencies.
Sources
- Introduction - Apache Kafka - kafka.apache.org
- Main Components in Apache Kafka - medium.com
- What is Kafka? - Apache Kafka Explained - aws.amazon.com
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