GCP ML: Implementation and Operations — Free Practice Questions

22 free GCP ML: Implementation and Operations practice questions with detailed answer explanations. Covers all exam domains, no signup needed.

22 questions · answers explained · free to practise · Google Cloud ML hub

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Sample questions with answers

8 of the 22 questions in this set, with the correct answer marked and explained.

1. Which frameworks are supported for Training and Online Prediction?

  • scikit-learn
    scikit-learn can be used for the breadth and simplicity of classical machine learning. XGBoost can be used for the ease and accuracy of extreme gradient boosting. TensorFlow is supported, for the cutting edge power of deep learning. Keras is able to be used, for easy and fast prototyping of deep learning
  • XGBoost
  • Keras
  • Tensorflow
  • PyTorch

2. When developing your machine learning model, you need to tune your hyperparameters. Which of these answers are examples of hyperparameters? (Choose all that apply)

  • Weights
  • Hidden layers
    Hidden layers are a hyperparameter, which adjusts the training model itself. Learning rate is a hyperparameter, which adjusts the training model itself.
  • Biases
  • Learning rate

3. Your department head is wondering if Cloud Machine Learning Engine’s real-time, online predictive capabilities could help automate some of their procedures. She’s asked you to write up a report outlining the key features. Which of the following could you include? (Choose all that apply.)

  • A Docker container is not required.
    Cloud Machine Learning Engine supports TensorFlow, scikit-learn, and XGBoost frameworks. Cloud Machine Learning Engine offers real-time, serverless online predictions. Cloud Machine Learning Engine does not require a Docker container.
  • Serverless processing
  • It scales to terabytes of data.
  • It supports multiple Cloud Machine Learning Engine frameworks.

4. You overheard your CEO talk about artificial intelligence as a business trend, and how he’d like to know more. Being the go-getter that you are, you fully investigate Google Cloud’s Cloud AI. What talking points can you bring to the next company picnic? (Choose all that apply.)

  • Cloud AI can power chatbots capable of natural conversations.
    Cloud AI’s Dialogflow Enterprise Edition is a development suite for conversational interfaces for websites, mobile apps and more. Google Cloud uses hardware accelerators called TPUs (Tensor Processing Units), specifically for machine learning. Cloud AI works with Cloud Vision, Cloud Video Intelligence, Google Natural Language APIs, as well as others.
  • Cloud AI incorporates a series of APIs for image, video, and text analysis.
  • Cloud AI can read the minds of potential customers with the All-In Thought Processor.
  • Cloud AI uses hardware optimized for machine learning.

5. Your organization is developing an IoT app, and you’re in charge of ensuring that security is handled appropriately. What are some of the security features of Cloud IoT Core? (Choose all that apply.)

  • Key rotation is supported.
    To use Cloud IoT Core functions, the user account or the service account must have the proper roles and permissions, as defined in Cloud IAM for the project. Cloud IoT Core supports rotating keys per device, including registration of concurrent keys and setting an expiration schedule. Cloud IoT Core uses different JSON Web Tokens for public/private keys on each device.
  • Access to Cloud IoT Core is controlled by Cloud IAM.
  • Signatures are verified by two-factor authentication
  • Public/private keys are used for each device.

6. Your boss has asked you to investigate the Cloud Storage Transfer service and detail what source options is presents. What possibilities do you find? (Check all that apply.)

  • Amazon S3 buckets.
    You can transfers objects from Google Cloud Storage buckets, Amazon S3 buckets, Azure Storage containers, and a list of object URLs.
  • Azure Storage containers.
  • List of Object URLs.
  • Google Cloud Storage buckets.

7. Which languages does Datalab support? (Choose all that apply)

  • Python
    Datalab supports SQL queries, specifically with BigQuery. Used with BigQuery UDF queries.
  • Java
  • JavaScript
  • SQL

8. Choose the components that are created when you type 'datalab create (instance_name)'. (Choose all that apply)

  • The compute engine instance to host your Datalab notebook.
    Datalab runs on a Compute Engine instance..
  • A VPC called 'datalab-network'.
  • An App Engine application for your notebook.
  • A Cloud Source repository

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