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AIF-C01 Practice Questions

AWS Certified AI Practitioner Exam

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Total Questions : 371

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Question # 31

A company deploys a custom ML model on Amazon SageMaker AI. The company uses the model to build a generative AI application for a healthcare recommendation system.

The company tests the application and finds a potential bias issue. The application consistently recommends different treatment approaches for patients who have identical medical conditions based on patient demographic information.

The company needs a solution to ensure that the application does not generate biased recommendations.

Which solution will meet this requirement?

Options:

Use SageMaker Clarify to detect bias patterns. Collect and use additional balanced training data. Use the data to retrain the model.

Implement prompt engineering techniques to explicitly instruct the model to provide fair recommendations regardless of demographics.

Apply content filtering by using Amazon Comprehend to remove potentially biased recommendations before they reach users.

Create separate foundation model (FM) endpoints for each demographic group to provide specialized care recommendations.

Discussion 0

Question # 32

A company wants to improve the accuracy of the responses from a generative AI application. The application uses a foundation model (FM) on Amazon Bedrock.

Which solution meets these requirements MOST cost-effectively?

Options:

Fine-tune the FM.

Retrain the FM.

Train a new FM.

Use prompt engineering.

Discussion 0

Question # 33

A company wants to develop ML applications to improve business operations and efficiency.

Select the correct ML paradigm from the following list for each use case. Each ML paradigm should be selected one or more times. (Select FOUR.)

• Supervised learning

• Unsupervised learning

Question # 33

Options:

Discussion 0

Answer:

Explanation:

The company is developing ML applications for various use cases, and the task is to select the correct ML paradigm (supervised or unsupervised learning) for each. Supervised learning involves training a model on labeled data to make predictions, while unsupervised learning identifies patterns or structures in unlabeled data. Each use case aligns with one of these paradigms based on its requirements.

Exact Extract from AWS AI Documents:

From the AWS AI Practitioner Learning Path:

" Supervised learning uses labeled data to train models for tasks like classification (e.g., binary or multi-class classification), where the model predicts a category. Unsupervised learning works with unlabeled data for tasks like clustering (e.g., K-means clustering) or dimensionality reduction, identifying patternsor reducing data complexity without predefined labels. "

(Source: AWS AI Practitioner Learning Path, Module on Machine Learning Strategies)

Detailed Explanation:

Binary classification: Supervised learningBinary classification involves predicting one of two classes (e.g., yes/no, spam/not spam) using labeled data, making it a supervised learning task. The model learns from examples where the correct class is provided.

Multi-class classification: Supervised learningMulti-class classification extends binary classification to predict one of multiple classes (e.g., categorizing items into several groups). Like binary classification, it requires labeled data, so it falls under supervised learning.

K-means clustering: Unsupervised learningK-means clustering groups data into clusters based on similarity, without requiring labeled data. This is a classic unsupervised learning task, as the algorithm identifies patterns in the data on its own.

Dimensionality reduction: Unsupervised learningDimensionality reduction (e.g., using techniques like PCA) reduces the number of features in a dataset while preserving important information. It does not require labeled data, making it an unsupervised learning task.

Hotspot Selection Analysis:

The hotspot lists four use cases, each with a dropdown containing " Select..., " " Supervised learning, " and " Unsupervised learning. " The correct selections are:

Binary classification: Supervised learning

Multi-class classification: Supervised learning

K-means clustering: Unsupervised learning

Dimensionality reduction: Unsupervised learning

Each paradigm (supervised and unsupervised learning) is used twice, as the question allows for paradigms to be selected one or more times.

[References:, AWS AI Practitioner Learning Path: Module on Machine Learning Strategies, Amazon SageMaker Developer Guide: Supervised and Unsupervised Learning (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html), AWS Documentation: Introduction to Machine Learning Paradigms (https://aws.amazon.com/machine-learning/), , , , , ]

Question # 34

A company wants to create an application by using Amazon Bedrock. The company has a limited budget and prefers flexibility without long-term commitment.

Which Amazon Bedrock pricing model meets these requirements?

Options:

On-Demand

Model customization

Provisioned Throughput

Spot Instance

Discussion 0

Question # 35

A company is building an ML model. The company collected new data and analyzed the data by creating a correlation matrix, calculating statistics, and visualizing the data.

Which stage of the ML pipeline is the company currently in?

Options:

Data pre-processing

Feature engineering

Exploratory data analysis

Hyperparameter tuning

Discussion 0

Question # 36

A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company needs the LLM to produce more consistent responses to the same input prompt.

Which adjustment to an inference parameter should the company make to meet these requirements?

Options:

Decrease the temperature value

Increase the temperature value

Decrease the length of output tokens

Increase the maximum generation length

Discussion 0

Question # 37

A company is monitoring a predictive model by using Amazon SageMaker Model Monitor. The company notices data drift beyond a defined threshold. The company wants to mitigate a potentially adverse impact on the predictive model.

Options:

Restart the SageMaker AI endpoint.

Adjust the monitoring sensitivity.

Re-train the model with fresh data.

Set up experiments tracking.

Discussion 0

Question # 38

A company has trained a custom foundation model (FM). The company wants to evaluate the toxicity of the FM ' s outputs by using human reviewers. The company has a team of internal reviewers. The company also wants to include external teams of reviewers to scale operations.

Which AWS service or feature will meet these requirements?

Options:

Amazon Bedrock Agents

Amazon Comprehend Custom

Amazon SageMaker JumpStart

Amazon SageMaker Ground Truth

Discussion 0

Question # 39

A company wants to use generative AI to increase developer productivity and software development. The company wants to use Amazon Q Developer.

What can Amazon Q Developer do to help the company meet these requirements?

Options:

Create software snippets, reference tracking, and open-source license tracking.

Run an application without provisioning or managing servers.

Enable voice commands for coding and providing natural language search.

Convert audio files to text documents by using ML models.

Discussion 0

Question # 40

A company has documents that are missing some words because of a database error. The company wants to build an ML model that can suggest potential words to fill in the missing text.

Which type of model meets this requirement?

Options:

Topic modeling

Clustering models

Prescriptive ML models

BERT-based models

Discussion 0

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