AWS Certified AI Practitioner · Free Practice Question Medium
Question 33
A retail company wants to leverage machine learning to analyze customer behavior and predict future purchasing trends but lacks in-house coding expertise. The company’s goal is to build a model that can identify patterns in customer data and forecast sales, helping to tailor marketing strategies and inventory management. Since the team does not have any programming skills, they are considering different tools or services that would enable them to develop a machine learning model without writing any code.
Given this limitation, which of the following tools or services would be most suitable for the company to use?
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A
The company should use SageMaker Canvas, as it enables users to create machine learning models using a visual interface
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B
The company should use SageMaker Clarify, as it enables users to create machine learning models using a visual interface
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C
The company should use SageMaker Data Wrangler to simplify data preparation and feature engineering, which are mandatory steps towards building a machine learning model
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D
The company should use SageMaker Built-in Algorithms, which provide a collection of pre-built algorithms for building machine learning models
Reveal correct answer
Correct answer: A
Explanation
Correct option:
The company should use SageMaker Canvas, as it enables users to create machine learning models using a visual interface
SageMaker Canvas is a perfect choice for a company without coding expertise because it provides a fully no-code environment where users can build machine learning models through a user-friendly visual interface. It simplifies the entire machine learning process, from data import and preparation to model building and deployment, without the need to write any code. This makes it highly suitable for business analysts and non-technical users.
Amazon SageMaker Canvas enables you to build your own AI/ML models without having to write a single line of code. You can build ML models for common use cases such as regression and forecasting and can access and evaluate foundation models (FMs) from Amazon Bedrock. You can also access public FMs from Amazon SageMaker JumpStart for content generation, text extraction, and text summarization to support generative AI solutions.
Incorrect options:
The company should use SageMaker Built-in Algorithms, which provide a collection of pre-built algorithms for building machine learning models - SageMaker Built-in Algorithms offer a range of pre-built machine learning algorithms that can be used for various tasks such as classification, regression, and clustering. However, using these algorithms typically requires knowledge of coding to manage data preparation, model training, and tuning. As such, this option is less suitable for a company with no coding expertise.
The company should use SageMaker Data Wrangler to simplify data preparation and feature engineering, which are mandatory steps towards building a machine learning model - SageMaker Data Wrangler can be a valuable tool for companies looking to clean and prepare data with minimal coding. It provides a visual interface for data wrangling tasks such as data cleaning, transformation, and feature engineering. However, it cannot be used to build machine learning models.
The company should use SageMaker Clarify, as it enables users to create machine learning models using a visual interface - SageMaker Clarify automatically evaluates foundation models for your generative AI use case with metrics such as accuracy, robustness, and toxicity to support your responsible AI initiative. SageMaker Clarify explains how input features contribute to your model predictions during model development and inference. Evaluate your FM during customization using automatic and human-based evaluations. SageMaker Clarify cannot be used to build machine learning models.
References:
https://aws.amazon.com/sagemaker/canvas/
https://docs.aws.amazon.com/documentdb/latest/developerguide/no-code-machine-learning.html
Discussion
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