Introduction to AI and Machine Learning on Google Cloud - Quiz

Introduction to AI and Machine Learning on Google Cloud - Quiz


This course introduces the artificial intelligence (AI) and machine learning (ML) offerings on Google Cloud that support the data-to-AI lifecycle through AI foundations, AI development, and AI solutions. It explores the technologies, products, and tools available to build an ML model, an ML pipeline, and a generative AI project based on the different goals of users, including data scientists, AI developers, and ML engineers.

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AI Foundations

Quest 1: You want to use machine learning to discover the underlying pattern and group a collection of unlabeled photos into different sets. Which should you use?

  • Unsupervised learning, cluster analysis

  • Unsupervised learning, dimensionality reduction

  • Supervised learning, logistic regression

  • Supervised learning, linear regression

Quest 2: Which Google hardware innovation tailors architecture to meet the computation needs on a domain, such as the matrix multiplication in machine learning?

  • GPUs (graphic processing units)

  • CPUs (central processing units)

  • DPUs (data processing units)

  • TPUs (tensor processing units)

Quest 3: If you have unstructured data, like images, text, and/or audio, which storage option on Google Cloud would you choose?

  • Cloud Spanner

  • Cloud SQL

  • Cloud Bigtable

  • Cloud Storage

Quest 4: Which SQL command would you use to create an ML model in BigQuery ML?





Quest 5: Which of the following is one of Google’s seven principles for responsible AI?

  • AI should be used to solve any problem regardless of the ethical principles.

  • Financial benefit should be the only consideration of AI.

  • AI should avoid creating or reinforcing unfair bias.

  • Privacy design should not be a concern of AI.

Quest 6: On Cloud Storage, which data storage class is best for storing data that needs to be accessed less than once a year?

  • Archive storage

  • Nearline storage

  • Standard storage

  • Coldline storage

Quest 7: What are the three layers of the AI/ML framework on Google Cloud?

  • ML development, ML applications, and ML use cases

  • Foundation models, large language models, and application models

  • AI, ML, and deep learning

  • AI foundations, AI development, and AI solutions

Quest 8: Vertex AI, AutoML, and Generative AI Studio align to which stage of the data-to-AI workflow?

  • Storage

  • Ingestion and process

  • Machine learning

  • Analytics

AI Development Options

Quest 1: tf.keras is a high-level TensorFlow library that has been commonly used to build ML models. Which of the following lets you create a neural network with multiple layers?

  • tf.keras.Sequential

  • model.compile


  • tf.keras.Run

Quest 2: A video production company wants to use machine learning to categorize event footage but does not want to train its own ML model. Which option can help you get started?

  • BigQuery ML

  • AutoML

  • Pre-trained APIs

  • Custom training

Quest 3: Which of the following can you do with the Natural Language API?

  • Complete new areas of an existing image.

  • Analyze sentiment and identify subjects of text.

  • Classify pictures.

  • Generate a caption for a YouTube video.

Quest 4: Your company has a massive amount of data, and you want to train your own machine learning model to see what insights ML can provide. Due to resource constraints, you require a codeless solution. Which option is best?

  • Pre-trained APIs

  • AutoML

  • Custom training

  • BigQuery ML

Quest 5: Which code-based solution offered with Vertex AI gives data scientists full control over the development environment and process?

  • AutoML

  • AI Solutions

  • Custom training

  • AI Platform

Quest 6: You work for a global hotel chain that has recently loaded some guest data into BigQuery. You have experience writing SQL and want to leverage machine learning to help predict guest trends for the next few months. Which option is best?

  • Pre-trained APIs

  • AutoML

  • Custom training

  • BigQuery ML

AI Development Workflow

Quest 1: Which stage of the machine learning workflow includes model training and evaluation?

  • Model serving

  • Model development

  • Data preparation

Quest 2: A hospital uses the machine learning technology of Google to help pre-diagnose cancer by feeding historical patient medical data to the model. The goal is to identify as many potential cases as possible. Which metric should the model focus on?

  • Feature importance

  • Recall

  • Precision

  • Confusion matrix

Quest 3: A farm uses the machine learning technology of Google to detect defective apples in their crop, like those with irregular sizes or scratches. The goal is to identify only the apples that are actually bad so that no good apples are wasted. Which metric should the model focus on?

  • Recall

  • Precision

  • Confusion matrix

  • Feature importance

Quest 4: Which stage of the machine learning workflow includes data upload and feature engineering?

  • Model training

  • Data preparation

  • Model serving

Quest 5: Select the correct machine learning workflow.

  • Data preparation, model evaluation, model training

  • Data preparation, model development, model serving

  • Model training, data preparation, model serving

  • Model serving, data preparation, model development

Quest 6: Which of the following provides a toolkit to automate, monitor, and govern machine learning systems by orchestrating the workflow in a serverless manner?

  • Vertex AI Feature Store

  • Vertex AI Pipelines

  • Responsible AI

  • Explainable AI

Quest 7: When you build an ML pipeline on Vertex AI to automate the ML workflow, what are the components you can use?

  • You can include both prebuilt components (by Google) and custom components into the pipeline.

  • You can only rely on custom components.

  • You can only use the prebuilt pipeline template without the flexibility to customize it.

  • You can only use prebuilt components.

Generative AI

Quest 1: Which of the following is the best way to generate more creative or unexpected content by adjusting the model parameters in Generative AI Studio?

  • Set the temperature to a low value.

  • Set the top P to 25%.

  • Set the temperature to a high value.

  • Set the top K to 1.

Quest 2: What are the two categories of AI solutions provided by Google Cloud?

  • Vertex AI and generative AI

  • Vertical solutions, which focus on specific industries, and horizontal solutions, which solve problems across industries

  • Prebuilt solutions and custom solutions

  • Contact Center AI and Document AI

Quest 3: How does generative AI generate new content?

  • It’s programmed based on predetermined algorithms that cannot be altered.

  • It’s a random process.

  • It learns from a massive amount of existing content and can then be used to solve general problems or be further tuned to solve specific problems.

  • The training leads to a foundation model that cannot be further tuned with a new dataset.

Quest 4: Which of the following is a type of prompt that allows a large language model to perform a task with only a small number of examples?

  • Few-shot prompt

  • Unsupervised prompt

  • Zero-shot prompt

  • One-shot prompt

Quest 5: What is Generative AI Studio?

  • A machine learning model that is trained on text only.

  • A technology that lets you code programming languages without learning them.

  • A tool that lets you quickly test and customize generative AI models so you can leverage their capabilities in your applications.

  • A type of artificial intelligence that writes emails for you.

Quest 6: You run a call center that handles customer questions from multiple channels, such as email, phone calls, and chat. You want to improve customer satisfaction and agent efficiency by using AI to automate routine requests, help agents with complex tasks and discover insights. Which AI solution on Google Cloud should you choose?

  • Document AI

  • Discovery AI

  • Contact Center AI

  • Healthcare AI