Google Professional Machine Learning Engineer exam dumps

Google Professional Machine Learning Engineer practice question 404 of 522

Professional Machine Learning Engineer. Professional level, Google Cloud. Free question with the correct answer and a full explanation.

Google Professional Machine Learning Engineer Question 404

Single answerGoogle Cloud Platform

You are designing a machine learning pipeline using TFX components on Google Cloud. Your task is to process a large dataset for training while ensuring scalability and efficient resource utilization. Which TFX component and GCP service combination is the most appropriate for this task?

  1. A

    TFX ExampleGen with Apache Beam executed on Cloud Dataflow

  2. B

    TFX Transform with Apache Spark executed on Compute Engine

  3. C

    TFX Trainer with TensorFlow executed on a local machine

  4. D

    TFX Evaluator with AI Platform (Vertex AI) Pipelines

Show answer and explanation

Correct answer: A

Explanation

The TFX ExampleGen component is designed to ingest and preprocess large datasets, making it the first step in most TFX pipelines. When paired with Apache Beam running on Cloud Dataflow, it ensures scalability, fault tolerance, and efficient resource utilization, making it the most appropriate choice for handling large-scale data processing tasks in a production environment.

  • A. Correct.

    TFX ExampleGen with Apache Beam executed on Cloud Dataflow is the correct choice because ExampleGen is used for ingesting and processing large datasets, and Cloud Dataflow provides a fully managed, scalable processing environment for Apache Beam pipelines.

  • B. Incorrect.

    TFX Transform with Apache Spark executed on Compute Engine is incorrect because Transform is used for feature engineering, and Apache Spark is not natively supported by TFX. Compute Engine also lacks the scalability and efficiency of Cloud Dataflow for this use case.

  • C. Incorrect.

    TFX Trainer with TensorFlow executed on a local machine is incorrect because training large datasets on a local machine is not scalable or efficient for production-grade pipelines.

  • D. Incorrect.

    TFX Evaluator with AI Platform (Vertex AI) Pipelines is incorrect because the Evaluator component is used for model evaluation, not for processing datasets. Additionally, Vertex AI Pipelines is used for orchestrating pipelines, not specifically for data processing.

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