You are building an ML model to detect anomalies in real-time sensor data. You will use Pub/Sub to handle incoming requests. You want to store the results for analytics and visualization. How should you configure the pipeline?
You are building an ML model to detect anomalies in real-time sensor data. You will use Pub/Sub to handle incoming requests. You want to store the results for analytics and visualization. How should you configure the pipeline?
To handle real-time streaming data and apply machine learning models for anomaly detection, the ideal configuration involves using Dataflow for data processing. Dataflow is a fully managed service for executing Apache Beam pipelines that handle stream and batch data processing. Using AI Platform allows for deploying and managing machine learning models. BigQuery is a powerful analytics data warehouse that can store the results for further analysis and visualization. Therefore, the correct configuration is 1 = Dataflow, 2 = AI Platform, 3 = BigQuery.
Definitely A. Dataflow is must.
Even if I follow the link, it should be dataflow, AI-Platform and Bigquery. Real answer should be A
Went with A
right answer i A. Dataflow is a must.
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The correct answer is D. 1 = BigQuery, 2 = AI Platform, 3 = Cloud Storage.
The answers discussed here where considered wrong when I took the mock test of GCP PMLE. Can anyone assist on this whether to go with with the one given my examtopics ?
This use case similar to anomaly detection also points to A only. https://cloud.google.com/blog/products/data-analytics/anomaly-detection-using-streaming-analytics-and-ai
A. Definitely it's the correct answer
AutoML is useful for labeled data. So either A or D. Dataflow is must for pipeline so A is correct
A - Dataflow is the only correct option for this case.
Option A
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Big Query is ideal for analytics
BigQuery for analytics 100%
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