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?

A . 1 Dataflow, 2 – Al Platform, 3 BigQuery
B . 1 DataProc, 2 AutoML, 3 Cloud Bigtable
C . 1 BigQuery, 2 AutoML, 3 Cloud Functions
D . 1 BigQuery, 2 Al Platform, 3 Cloud Storage

View Answer

Answer: A

Explanation:

Dataflow is a fully managed service for executing Apache Beam pipelines that can process streaming or batch data1.

Al Platform is a unified platform that enables you to build and run machine learning applications across Google Cloud2.

BigQuery is a serverless, highly scalable, and cost-effective cloud data warehouse designed for business agility3.

These services are suitable for building an ML model to detect anomalies in real-time sensor data, as they can handle large-scale data ingestion, preprocessing, training, serving, storage, and visualization.

The other options are not as suitable because:

DataProc is a service for running Apache Spark and Apache Hadoop clusters, which are not optimized for streaming data processing4.

AutoML is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs5. However, it does not support custom models or real-time predictions.

Cloud Bigtable is a scalable, fully managed NoSQL database service for large analytical and operational workloads. However, it is not designed for ad hoc queries or interactive analysis. Cloud Functions is a serverless execution environment for building and connecting cloud services. However, it is not suitable for storing or visualizing data.

Cloud Storage is a service for storing and accessing data on Google Cloud. However, it is not a data warehouse and does not support SQL queries or visualization tools.