Free AIF-C01 Sample Questions — AWS Certified AI Practitioner AIF-C01

A company is building a contact center application and wants to gain insights from customer conversations. The company wants to analyze and extract key information from the audio of the customer calls. Which solution meets these requirements?

• A. Build a conversational chatbot by using Amazon Lex
• B. Transcribe call recordings by using Amazon Transcribe
• C. Extract information from call recordings by using Amazon SageMaker Model Monitor
• D. Create classification labels by using Amazon Comprehend

A manufacturing company has an application that ingests consumer complaints from publicly available sources. The application uses complex hard-coded logic to process the complaints. The company wants to scale this logic across markets and product lines. Which advantage do generative AI models offer for this scenario?

• A. Predictability of outputs
• B. Adaptability
• C. Less sensitivity to changes in inputs
• D. Explainability

A company wants to develop an interpretable ML model to assess the risk of loan applications. Which type of ML model or algorithm will meet these requirements?

• A. Deep learning model
• B. Logistic regression model
• C. K-means algorithm
• D. Random cut forest algorithm

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. Which solution will meet these requirements?

• A. Restart the SageMaker AI endpoint
• B. Adjust the monitoring sensitivity
• C. Re-train the model with fresh data
• D. Set up experiments tracking

What does an F1 score measure in the context of foundation model (FM) performance?

• A. Model precision and recall
• B. Model speed in generating responses
• C. Financial cost of operating the model
• D. Energy efficiency of the model’s computations

A company is using Amazon Bedrock to build an AI assistant. The AI assistant helps customers find relevant products by making suggestions. However, the AI assistant's responses are often generic and irrelevant. The company wants to use prompt engineering to improve the AI assistant's responses. Which solution will meet these requirements?

• A. Use few-shot prompting to add domain-specific context and explicit instructions
• B. Use chain-of-thought prompting with hidden reasoning steps to ignore explicit domain instructions
• C. Modify the AI assistant's conversational style to use more formal language and include technical product specifications
• D. Use zero-shot prompting to augment retrieval from a product database

An airline company wants to build a conversational AI assistant to answer customer questions about flight schedules, booking, and payments. The company wants to use large language models (LLMs) and a knowledge base to create a text-based chatbot interface. Which solution will meet these requirements with the LEAST development effort?

• A. Train models on Amazon SageMaker Autopilot
• B. Develop a Retrieval Augmented Generation (RAG) agent by using Amazon Bedrock
• C. Create a Python application by using Amazon Q Developer
• D. Fine-tune models on Amazon SageMaker Jumpstart

A company that uses multiple ML models wants to identify changes in original model quality so that the company can resolve any issues. Which AWS service or feature meets these requirements?

• A. Amazon SageMaker JumpStart
• B. Amazon SageMaker HyperPod
• C. Amazon SageMaker Data Wrangler
• D. Amazon SageMaker Model Monitor

A company is developing an ML model to make loan approvals. The company must implement a solution to detect bias in the model. The company must also be able to explain the model's predictions. Which solution will meet these requirements?

• A. Amazon SageMaker Clarify
• B. Amazon SageMaker Data Wrangler
• C. Amazon SageMaker Model Cards
• D. AWS AI Service Cards

A company has built an image classification model to predict plant diseases from photos of plant leaves. The company wants to evaluate how many images the model classified correctly. Which evaluation metric should the company use to measure the model's performance?

• A. R-squared score
• B. Accuracy
• C. Root mean squared error (RMSE)
• D. Learning rate

A company has a foundation model (FM) that was customized by using Amazon Bedrock to answer customer queries about products. The company wants to validate the model's responses to new types of queries. The company needs to upload a new dataset that Amazon Bedrock can use for validation. Which AWS service meets these requirements?

• A. Amazon S3
• B. Amazon Elastic Block Store (Amazon EBS)
• C. Amazon Elastic File System (Amazon EFS)
• D. AWS Snowcone

A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to classify the sentiment of text passages as positive or negative. Which prompt engineering strategy meets these requirements?

• A. Provide examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified
• B. Provide a detailed explanation of sentiment analysis and how LLMs work in the prompt
• C. Provide the new text passage to be classified without any additional context or examples
• D. Provide the new text passage with a few examples of unrelated tasks, such as text summarization or question answering

A company is building a new generative AI chatbot. The chatbot uses an Amazon Bedrock foundation model (FM) to generate responses. During testing, the company notices that the chatbot is prone to prompt injection attacks. What can the company do to secure the chatbot with the LEAST implementation effort?

• A. Fine-tune the FM to avoid harmful responses
• B. Use Amazon Bedrock Guardrails content filters and denied topics
• C. Change the FM to a more secure FM
• D. Use chain-of-thought prompting to produce secure responses

A company built an AI-powered resume screening system. The company used a large dataset to train the model. The dataset contained resumes that were not representative of all demographics. Which core dimension of responsible AI does this scenario present?

• A. Fairness
• B. Explainability
• C. Privacy and security
• D. Transparency

A company needs to monitor the performance of its ML systems by using a highly scalable AWS service. Which AWS service meets these requirements?

• A. Amazon CloudWatch
• B. AWS CloudTrail
• C. AWS Trusted Advisor
• D. AWS Config

A company has petabytes of unlabeled customer data to use for an advertisement campaign. The company wants to classify its customers into tiers to advertise and promote the company's products. Which methodology should the company use to meet these requirements?

• A. Supervised learning
• B. Unsupervised learning
• C. Reinforcement learning
• D. Reinforcement learning from human feedback (RLHF)

A company wants to control employee access to publicly available foundation models (FMs). Which solution meets these requirements?

• A. Analyze cost and usage reports in AWS Cost Explorer
• B. Download AWS security and compliance documents from AWS Artifact
• C. Configure Amazon SageMaker JumpStart to restrict discoverable FMs
• D. Build a hybrid search solution by using Amazon OpenSearch Service

Which option describes embeddings in the context of AI?

• A. method for compressing large datasets
• B. An encryption method for securing sensitive data
• C. method for visualizing high-dimensional data
• D. numerical method for data representation in a reduced dimensionality space

A company is building a solution to generate images for protective eyewear. The solution must have high accuracy and must minimize the risk of incorrect annotations. Which solution will meet these requirements?

• A. Human-in-the-loop validation by using Amazon SageMaker Ground Truth Plus
• B. Data augmentation by using an Amazon Bedrock knowledge base
• C. Image recognition by using Amazon Rekognition
• D. Data summarization by using Amazon QuickSight Q

A design company is using a foundation model (FM) on Amazon Bedrock to generate images for various projects. The company wants to have control over how detailed or abstract each generated image appears Which model parameter should the company modify?

• A. Model checkpoint
• B. Batch size
• C. Generation step
• D. Token length