Answer-first summary for fast verification
Answer: Use Cloud Pub/Sub for ingesting real-time sensor data, Dataflow for stream processing, BigQuery for storing processed data, and Vertex AI for training and deploying the anomaly detection model., Use Cloud Pub/Sub for data ingestion, Dataflow for real-time processing, BigQuery for analytics storage, and Vertex AI for the machine learning model.
The optimal configuration for a real-time anomaly detection system with the given requirements involves using Cloud Pub/Sub for data ingestion to handle high-velocity data streams, Dataflow for efficient stream processing with low latency, BigQuery for scalable and cost-effective storage enabling analytics and visualization, and Vertex AI for developing and deploying the machine learning model. This setup ensures scalability, cost-effectiveness, and compliance with data security standards. Options A and C both correctly identify the use of Dataflow and Vertex AI, but option A more accurately specifies the use of BigQuery for storage, which is better suited for analytics and visualization purposes compared to Cloud Storage or Cloud Bigtable mentioned in other options.
Author: LeetQuiz Editorial Team
Ultimate access to all questions.
You are a Machine Learning Engineer at a manufacturing company tasked with developing a real-time anomaly detection system for sensor data across multiple production lines. The system must process high-velocity data streams, identify anomalies with low latency, and store the results for further analytics and visualization. The solution must be cost-effective, scalable, and comply with industry standards for data security. Given these requirements, how would you best architect the pipeline using Google Cloud services? Choose the two most appropriate options. (Choose two)
A
Use Cloud Pub/Sub for ingesting real-time sensor data, Dataflow for stream processing, BigQuery for storing processed data, and Vertex AI for training and deploying the anomaly detection model.
B
Use Cloud Pub/Sub for data ingestion, Cloud Functions for processing each message individually, Cloud Storage for storing results, and AutoML for model training and deployment.
C
Use Cloud Pub/Sub for data ingestion, Dataflow for real-time processing, BigQuery for analytics storage, and Vertex AI for the machine learning model.
D
Use Cloud Pub/Sub for data ingestion, Dataproc for processing, Cloud Bigtable for storage, and AutoML for the machine learning model.
E
All of the above options are suitable for different scenarios depending on the specific requirements and constraints.
No comments yet.