AWS Certified AI Practitioner AIF-C01
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Question 1
A company makes forecasts each quarter to decide how to optimize operations to meet expected demand. The company uses ML models to make these forecasts.
An AI practitioner is writing a report about the trained ML models to provide transparency and explainability to company stakeholders.
What should the AI practitioner include in the report to meet the transparency and explainability requirements?
- A: Code for model training
- B: Partial dependence plots (PDPs)
- C: Sample data for training
- D: Model convergence tables
Question 2
A company wants to use language models to create an application for inference on edge devices. The inference must have the lowest latency possible.
Which solution will meet these requirements?
- A: Deploy optimized small language models (SLMs) on edge devices.
- B: Deploy optimized large language models (LLMs) on edge devices.
- C: Incorporate a centralized small language model (SLM) API for asynchronous communication with edge devices.
- D: Incorporate a centralized large language model (LLM) API for asynchronous communication with edge devices.
Question 3
A company is developing a mobile ML app that uses a phone's camera to diagnose and treat insect bites. The company wants to train an image classification model by using a diverse dataset of insect bite photos from different genders, ethnicities, and geographic locations around the world.
Which principle of responsible AI does the company demonstrate in this scenario?
- A: Fairness
- B: Explainability
- C: Governance
- D: Transparency
Question 4
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
Question 5
A company has developed a generative text summarization model by using Amazon Bedrock. The company will use Amazon Bedrock automatic model evaluation capabilities.
Which metric should the company use to evaluate the accuracy of the model?
- A: Area Under the ROC Curve (AUC) score
- B: F1 score
- C: BERTScore
- D: Real world knowledge (RWK) score
Question 6
An AI practitioner wants to predict the classification of flowers based on petal length, petal width, sepal length, and sepal width.
Which algorithm meets these requirements?
- A: K-nearest neighbors (k-NN)
- B: K-mean
- C: Autoregressive Integrated Moving Average (ARIMA)
- D: Linear regression
Question 7
A company is using custom models in Amazon Bedrock for a generative AI application. The company wants to use a company managed encryption key to encrypt the model artifacts that the model customization jobs create.
Which AWS service meets these requirements?
- A: AWS Key Management Service (AWS KMS)
- B: Amazon Inspector
- C: Amazon Macie
- D: AWS Secrets Manager
Question 8
A company wants to use large language models (LLMs) to produce code from natural language code comments.
Which LLM feature meets these requirements?
- A: Text summarization
- B: Text generation
- C: Text completion
- D: Text classification
Question 9
A company is introducing a mobile app that helps users learn foreign languages. The app makes text more coherent by calling a large language model (LLM). The company collected a diverse dataset of text and supplemented the dataset with examples of more readable versions. The company wants the LLM output to resemble the provided examples.
Which metric should the company use to assess whether the LLM meets these requirements?
- A: Value of the loss function
- B: Semantic robustness
- C: Recall-Oriented Understudy for Gisting Evaluation (ROUGE) score
- D: Latency of the text generation
Question 10
A company notices that its foundation model (FM) generates images that are unrelated to the prompts. The company wants to modify the prompt techniques to decrease unrelated images.
Which solution meets these requirements?
- A: Use zero-shot prompts.
- B: Use negative prompts.
- C: Use positive prompts.
- D: Use ambiguous prompts.
Question 11
A company wants to use a large language model (LLM) to generate concise, feature-specific descriptions for the company’s products.
Which prompt engineering technique meets these requirements?
- A: Create one prompt that covers all products. Edit the responses to make the responses more specific, concise, and tailored to each product.
- B: Create prompts for each product category that highlight the key features. Include the desired output format and length for each prompt response.
- C: Include a diverse range of product features in each prompt to generate creative and unique descriptions.
- D: Provide detailed, product-specific prompts to ensure precise and customized descriptions.
Question 12
A company is developing an ML model to predict customer churn. The model performs well on the training dataset but does not accurately predict churn for new data.
Which solution will resolve this issue?
- A: Decrease the regularization parameter to increase model complexity.
- B: Increase the regularization parameter to decrease model complexity.
- C: Add more features to the input data.
- D: Train the model for more epochs.
Question 13
A company wants to build an ML model by using Amazon SageMaker. The company needs to share and manage variables for model development across multiple teams.
Which SageMaker feature meets these requirements?
- A: Amazon SageMaker Feature Store
- B: Amazon SageMaker Data Wrangler
- C: Amazon SageMaker Clarify
- D: Amazon SageMaker Model Cards
Question 14
A company is implementing intelligent agents to provide conversational search experiences for its customers. The company needs a database service that will support storage and queries of embeddings from a generative AI model as vectors in the database.
Which AWS service will meet these requirements?
- A: Amazon Athena
- B: Amazon Aurora PostgreSQL
- C: Amazon Redshift
- D: Amazon EMR
Question 15
A financial institution is building an AI solution to make loan approval decisions by using a foundation model (FM). For security and audit purposes, the company needs the AI solution's decisions to be explainable.
Which factor relates to the explainability of the AI solution's decisions?
- A: Model complexity
- B: Training time
- C: Number of hyperparameters
- D: Deployment time
Question 16
A pharmaceutical company wants to analyze user reviews of new medications and provide a concise overview for each medication.
Which solution meets these requirements?
- A: Create a time-series forecasting model to analyze the medication reviews by using Amazon Personalize.
- B: Create medication review summaries by using Amazon Bedrock large language models (LLMs).
- C: Create a classification model that categorizes medications into different groups by using Amazon SageMaker.
- D: Create medication review summaries by using Amazon Rekognition.
Question 17
A company wants to build a lead prioritization application for its employees to contact potential customers. The application must give employees the ability to view and adjust the weights assigned to different variables in the model based on domain knowledge and expertise.
Which ML model type meets these requirements?
- A: Logistic regression model
- B: Deep learning model built on principal components
- C: K-nearest neighbors (k-NN) model
- D: Neural network
Question 18
HOTSPOT
A company wants to build an ML application.
Select and order the correct steps from the following list to develop a well-architected ML workload. Each step should be selected one time.
Question 19
Which strategy will determine if a foundation model (FM) effectively meets business objectives?
- A: Evaluate the model's performance on benchmark datasets.
- B: Analyze the model's architecture and hyperparameters.
- C: Assess the model's alignment with specific use cases.
- D: Measure the computational resources required for model deployment.
Question 20
A company needs to train an ML model to classify images of different types of animals. The company has a large dataset of labeled images and will not label more data.
Which type of learning should the company use to train the model?
- A: Supervised learning
- B: Unsupervised learning
- C: Reinforcement learning
- D: Active learning
Question 21
Which phase of the ML lifecycle determines compliance and regulatory requirements?
- A: Feature engineering
- B: Model training
- C: Data collection
- D: Business goal identification
Question 22
A food service company wants to develop an ML model to help decrease daily food waste and increase sales revenue. The company needs to continuously improve the model's accuracy.
Which solution meets these requirements?
- A: Use Amazon SageMaker and iterate with newer data.
- B: Use Amazon Personalize and iterate with historical data.
- C: Use Amazon CloudWatch to analyze customer orders.
- D: Use Amazon Rekognition to optimize the model.
Question 23
A company has developed an ML model to predict real estate sale prices. The company wants to deploy the model to make predictions without managing servers or infrastructure.
Which solution meets these requirements?
- A: Deploy the model on an Amazon EC2 instance.
- B: Deploy the model on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster.
- C: Deploy the model by using Amazon CloudFront with an Amazon S3 integration.
- D: Deploy the model by using an Amazon SageMaker endpoint.
Question 24
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?
- A: Create software snippets, reference tracking, and open source license tracking.
- B: Run an application without provisioning or managing servers.
- C: Enable voice commands for coding and providing natural language search.
- D: Convert audio files to text documents by using ML models.
Question 25
A company wants to develop an AI application to help its employees check open customer claims, identify details for a specific claim, and access documents for a claim.
Which solution meets these requirements?
- A: Use Agents for Amazon Bedrock with Amazon Fraud Detector to build the application.
- B: Use Agents for Amazon Bedrock with Amazon Bedrock knowledge bases to build the application.
- C: Use Amazon Personalize with Amazon Bedrock knowledge bases to build the application.
- D: Use Amazon SageMaker to build the application by training a new ML model.
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