Free Amazon Web Services AIF-C01 Practice Exam with Questions & Answers | Set: 5

Amazon SageMaker JumpStart is the correct service for quickly deploying and consuming a foundation model (FM) within a team's VPC.

Amazon SageMaker JumpStart:

Provides access to a wide range of pre-trained models and solutions that can be easily deployed and consumed within a VPC.

Designed to simplify and accelerate the deployment of machine learning models, including foundation models.

Why Option B is Correct:

Rapid Deployment: JumpStart allows for quick deployment of models with minimal configuration, directly within a secure VPC environment.

Ease of Use: Provides a user-friendly interface to select and deploy models, reducing the time to value.

Why Other Options are Incorrect:

A. Amazon Personalize: Focuses on creating personalized recommendations, not deploying foundation models.

C. PartyRock: Not a recognized AWS service.

D. Amazon SageMaker endpoints: Endpoints are for deploying specific models, not a feature for quickly starting with pre-trained foundation models.

Amazon Textract (E) automatically extracts text and structured data from scanned documents, such as PDFs.

Amazon Bedrock (B) offers access to LLMs (such as Amazon Titan or Anthropic Claude) for tasks like summarization and generating embeddings for search.

Workflow:

Amazon Textract extracts text from PDFs in S3.

Amazon Bedrock LLMs summarize the extracted text.

(Optional: Summaries can be indexed using Amazon OpenSearch or another search solution.)

A (Translate) is for language translation, not extraction or summarization.

C (Transcribe) is for audio to text, not PDFs.

D (Polly) is for text-to-speech.

“Amazon Textract extracts text, forms, and tables from scanned documents... Bedrock provides generative AI models to perform summarization and other text generation tasks.”

(Reference: Amazon Textract, Amazon Bedrock, AWS GenAI RAG Reference)

When a model performs well on training data but poorly on evaluation/test data, it indicates overfitting.

Overfitting: The model memorizes the training data patterns instead of generalizing.

Underfitting (A) means the model performs poorly on both training and test data.

Bias (C) refers to systemic errors in predictions, not this training/test mismatch.

Prompt engineering (B) applies to generative AI, not general ML training models.

???? Reference:

AWS ML Glossary – Overfitting and Underfitting

When you have a labeled dataset and need to scale a generative AI solution for more complex or diverse product categories, fine-tuning the foundation model with your dataset is the best approach for consistent, accurate tagging.

D is correct:

“Fine-tuning a foundation model with your labeled data allows the model to generalize to new categories and improve tagging accuracy for your inventory.”

(Reference: Amazon Bedrock Fine-Tuning, AWS Generative AI)

A (zero-shot) and B (prompt templates) do not leverage the labeled data or scale as accurately.

C (continued pre-training) uses unlabeled data, not labeled.

Creating a prompt template that teaches the LLM to detect attack patterns is the most effective way to reduce the risk of the model being manipulated through prompt engineering.

Prompt Templates for Security:

A well-designed prompt template can guide the LLM to recognize and respond appropriately to potential manipulation attempts.

This strategy helps prevent the model from performing undesirable actions or exposing sensitive information by embedding security awareness directly into the prompts.

Why Option A is Correct:

Teaches Model Security Awareness: Equips the LLM to handle potentially harmful inputs by recognizing suspicious patterns.

Reduces Manipulation Risk: Helps mitigate risks associated with prompt engineering attacks by proactively preparing the LLM.

Why Other Options are Incorrect:

B. Increase the temperature parameter: This increases randomness in responses, potentially making the LLM more unpredictable and less secure.

C. Avoid LLMs not listed in SageMaker: Does not directly address the risk of prompt manipulation.

D. Decrease the number of input tokens: Does not mitigate risks related to prompt manipulation.

Diversity in datasets ensures representation of all demographics, reducing bias and improving fairness.

Accuracy is model performance.

Recency bias skews towards recent data.

Reliability measures consistency of results, not representation.

???? Reference:

AWS Responsible AI Guidelines – Data Diversity

Amazon Bedrock Guardrails allow developers to create safeguards that filter harmful content and prevent sensitive topics from being discussed. This functionality helps mitigate prompt injection attacks with minimal implementation effort. According to the official Amazon Bedrock documentation:

“You can configure Guardrails for Amazon Bedrock to define denied topics, use content filters, and apply sensitive information filters, offering protection against prompt injection attacks with minimal development effort.”

Accuracy is the most appropriate metric to measure the performance of an image classification model. It indicates the percentage of correctly classified images out of the total number of images. In the context of classifying plant diseases from images, accuracy will help the company determine how well the model is performing by showing how many images were correctly classified.

Option B (Correct): "Accuracy": This is the correct answer because accuracy measures the proportion of correct predictions made by the model, which is suitable for evaluating the performance of a classification model.

Option A: "R-squared score" is incorrect as it is used for regression analysis, not classification tasks.

Option C: "Root mean squared error (RMSE)" is incorrect because it is also used for regression tasks to measure prediction errors, not for classification accuracy.

Option D: "Learning rate" is incorrect as it is a hyperparameter for training, not a performance metric.

AWS AI Practitioner References:

Evaluating Machine Learning Models on AWS: AWS documentation emphasizes the use of appropriate metrics, like accuracy, for classification tasks.

The correct answer is C because Amazon SageMaker JumpStart provides administrative controls that allow organizations to manage and restrict access to foundation models within the AWS environment.

According to the official AWS documentation:

"Amazon SageMaker JumpStart provides model access management capabilities that enable administrators to control which foundation models are visible and usable by end users. Using AWS Identity and Access Management (IAM) policies, you can restrict access to specific models or completely disable model discovery in JumpStart."

This allows companies to enforce governance over which FMs their users can see and interact with, satisfying the requirement to control employee access to publicly available foundation models.

Explanation of other options:

A. AWS Cost Explorer is used to analyze billing and usage data but does not control access to services or models. It is helpful for budgeting and visibility, not access control.

B. AWS Artifact provides access to compliance reports and certifications, not tools for controlling user access to ML models.

D. Amazon OpenSearch Service is used for search and analytics on structured and unstructured data. It does not provide access control mechanisms for foundation models.

Referenced AWS AI/ML Documents and Study Guides:

Amazon SageMaker JumpStart Documentation – Model Access Management

AWS IAM Documentation – Restricting Access to SageMaker Resources

AWS Machine Learning Specialty Certification Guide – Security and Governance Section