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Professional-Machine-Learning-Engineer Exam Bootcamp, Professio

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    Google Professional Machine Learning Engineer Sample Questions (Q29-Q34):

    NEW QUESTION # 29
    Your company manages an application that aggregates news articles from many different online sources and sends them to users. You need to build a recommendation model that will suggest articles to readers that are similar to the articles they are currently reading. Which approach should you use?

    • A. Create a collaborative filtering system that recommends articles to a user based on the user's past behavior.
    • B. Encode all articles into vectors using word2vec, and build a model that returns articles based on vector similarity.
    • C. Build a logistic regression model for each user that predicts whether an article should be recommended to a user.
    • D. Manually label a few hundred articles, and then train an SVM classifier based on the manually classified articles that categorizes additional articles into their respective categories.

    Answer: A


    NEW QUESTION # 30
    You are building an ML model to detect anomalies in real-time sensor dat a. 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 = DataProc, 2 = AutoML, 3 = Cloud Bigtable
    • B. 1 = Dataflow, 2 - Al Platform, 3 = BigQuery
    • C. 1 = BigQuery, 2 = Al Platform, 3 = Cloud Storage
    • D. 1 = BigQuery, 2 = AutoML, 3 = Cloud Functions

    Answer: B


    NEW QUESTION # 31
    You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure. Which additional readiness check should you recommend to the team?

    • A. Ensure that training is reproducible
    • B. Ensure that model performance is monitored
    • C. Ensure that feature expectations are captured in the schema
    • D. Ensure that all hyperparameters are tuned

    Answer: A


    NEW QUESTION # 32
    A data scientist needs to identify fraudulent user accounts for a company's ecommerce platform. The company wants the ability to determine if a newly created account is associated with a previously known fraudulent user.
    The data scientist is using AWS Glue to cleanse the company's application logs during ingestion.
    Which strategy will allow the data scientist to identify fraudulent accounts?

    • A. Create a FindMatches machine learning transform in AWS Glue.
    • B. Search for duplicate accounts in the AWS Glue Data Catalog.
    • C. Create an AWS Glue crawler to infer duplicate accounts in the source data.
    • D. Execute the built-in FindDuplicates Amazon Athena query.

    Answer: A

    Explanation:
    Explanation/Reference: https://docs.aws.amazon.com/glue/latest/dg/machine-learning.html


    NEW QUESTION # 33
    A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only.
    What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?

    • A. Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.
    • B. Serialize the trained model so the format is compressed for deployment. Build the image and upload it to Docker Hub.
    • C. Serialize the trained model so the format is compressed for deployment. Tag the Docker image with the registry hostname and upload it to Amazon S3.
    • D. Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.

    Answer: A


    NEW QUESTION # 34
    ......

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