Free AWS-CERTIFIED-AI-PRACTITIONER-AIF-C01 – Amazon AWS-CERTIFIED-AI-PRACTITIONER-AIF-C01 Practice Test Question

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• ○ R-squared score
• ○ Accuracy
• ○ Root mean squared error (RMSE)
• ○ Learning rate

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

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• ○ Integration with Amazon S3 for object storage
• ○ Support for geospatial indexing and queries
• ○ Scalable index management and nearest neighbor search capability
• ○ Ability to perform real-time analysis on streaming data

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• ○ Improving network security by using intrusion detection systems
• ○ Creating photorealistic images from text descriptions for digital marketing
• ○ Enhancing database performance by using optimized indexing
• ○ Analyzing financial data to forecast stock market trends

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

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• ○ Supervised learning with a manually curated dataset of good responses and bad responses
• ○ Reinforcement learning with rewards for positive customer feedback
• ○ Unsupervised learning to find clusters of similar customer inquiries
• ○ Supervised learning with a continuously updated FAQ database

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• ○ Decrease the temperature value.
• ○ Increase the temperature value.
• ○ Decrease the length of output tokens.
• ○ Increase the maximum generation length.

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• ○ Calculate the total cost of resources used by the model.
• ○ Measure the model's accuracy against a predefined benchmark dataset.
• ○ Count the number of layers in the neural network.
• ○ Assess the color accuracy of images processed by the model.

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• ○ Tokens are the basic units of input and output that a generative AI model operates on, representing words, subwords, or other linguistic units.
• ○ Tokens are the mathematical representations of words or concepts used in generative AI models.
• ○ Tokens are the pre-trained weights of a generative AI model that are fine-tuned for specific tasks.
• ○ Tokens are the specific prompts or instructions given to a generative AI model to generate output.

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• ○ Fuzzing training data to find vulnerabilities
• ○ Denial of service (DoS)
• ○ Penetration testing with authorization
• ○ Jailbreak

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• ○ Amazon SageMaker Clarify
• ○ Amazon SageMaker Data Wrangler
• ○ Amazon SageMaker Model Cards
• ○ AWS AI Service Cards

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• ○ AWS Key Management Service (AWS KMS)
• ○ Amazon Inspector
• ○ Amazon Macie
• ○ AWS Secrets Manager

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• ○ Model complexity
• ○ Training time
• ○ Number of hyperparameters
• ○ Deployment time

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• ○ Logistic regression model
• ○ Deep learning model built on principal components
• ○ K-nearest neighbors (k-NN) model
• ○ Neural network

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• ○ Supervised learning
• ○ Unsupervised learning
• ○ Reinforcement learning
• ○ Active learning

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• ○ Feature engineering
• ○ Model training
• ○ Data collection
• ○ Business goal identification

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• ○ AWS CloudTrail
• ○ Amazon CloudWatch
• ○ AWS Audit Manager
• ○ Amazon S3 Intelligent-Tiering
• ○ Amazon S3 Standard

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• ○ Chain-of-thought prompting
• ○ Prompt injection
• ○ Few-shot prompting
• ○ Prompt templating

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• ○ Summarizing customer complaints
• ○ Classifying customers based on product usage
• ○ Segmenting customers based on type of investments
• ○ Forecasting revenue for certain products

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

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• ○ To encrypt text data
• ○ To compress text files
• ○ To break text into smaller units for processing
• ○ To translate text between languages

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• ○ Generation of content embeddings
• ○ Generation of embeddings for user queries
• ○ Creation of the search index
• ○ Retrieval of relevant content
• ○ Response generation for the user

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• ○ Recall
• ○ Accuracy
• ○ Precision
• ○ Lift chart

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• ○ RAG can use external knowledge sources to generate more accurate and informative responses.
• ○ RAG is designed to improve the speed of language model training.
• ○ RAG is primarily used for speech recognition tasks.
• ○ RAG is a technique for data augmentation in computer vision tasks.

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• ○ Deploy the model by using an Amazon EC2 compute optimized instance.
• ○ Use the model with on-demand throughput on Amazon Bedrock.
• ○ Store the model in Amazon S3 and host the model by using AWS Lambda.
• ○ Purchase Provisioned Throughput for the model on Amazon Bedrock.

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• ○ Supervised learning
• ○ Unsupervised learning
• ○ Reinforcement learning
• ○ Transfer learning

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• ○ User inputs and model outputs are anonymized and shared with third-party model providers.
• ○ User inputs and model outputs are not shared with any third-party model providers.
• ○ User inputs are kept confidential, but model outputs are shared with third-party model providers.
• ○ User inputs and model outputs are redacted before the inputs and outputs are shared with third-party model providers.

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• ○ Use the Bilingual Evaluation Understudy (BLEU) score to estimate the absolute translation quality of the two methods.
• ○ Use the Bilingual Evaluation Understudy (BLEU) score to estimate the relative translation quality of the two methods.
• ○ Use the BERTScore to estimate the absolute translation quality of the two methods.
• ○ Use the BERTScore to estimate the relative translation quality of the two methods.

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• ○ Unsupervised learning
• ○ Supervised learning
• ○ Reinforcement learning
• ○ Semi-supervised learning

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• ○ Amazon Rekognition
• ○ Amazon Textract
• ○ Amazon Lex
• ○ Amazon Q Business

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• ○ SageMaker Canvas
• ○ SageMaker Clarify
• ○ SageMaker Model Monitor
• ○ SageMaker Data Wrangler

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• ○ Amazon Comprehend
• ○ Amazon Personalize
• ○ Amazon Polly
• ○ Amazon OpenSearch Service

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• ○ Use a rule-based system instead of an ML model.
• ○ Apply explainable AI techniques to show customers which factors influenced the model’s decision.
• ○ Develop an interactive UI for customers and provide clear technical explanations about the system.
• ○ Increase the accuracy of the model to reduce the need for transparency.

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• ○ Use a large language model (LLM) to perform natural language processing (NLP) for sentiment analysis.
• ○ Use a regression algorithm to classify the feedback based on predefined categories. Then, analyze user sentiment.
• ○ Use a recommendation engine algorithm to detect user sentiment.
• ○ Use a time series algorithm to predict user sentiment based on past feedback.

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• ○ A variational autoencoder (VAE) model
• ○ A transformer-based model
• ○ A diffusion model
• ○ A generative adversarial network (GAN) model

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• ○ Immediately start training a custom FM by using the company's existing data.
• ○ Conduct stakeholder interviews to refine use cases and set measurable goals.
• ○ Implement a prebuilt AI assistant solution and measure its impact on customer satisfaction.
• ○ Analyze industry AI implementations and replicate the most successful features.

Answer is in the explanation below.

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• ○ Optimize the deployed model architecture to prioritize computational efficiency during model inference.
• ○ Adopt a distributed inference approach by using multiple smaller models across multiple Availability Zones.
• ○ Adopt a hybrid strategy by deploying the model on premises and storing the data on AWS.
• ○ Deploy multiple models and use a dynamic model selection mechanism that queries different models randomly.

Answer is in the explanation below.

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• ○ Model size
• ○ Inference latency
• ○ Context window
• ○ Fine-tuning

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• ○ Knowledge Bases
• ○ Guardrails
• ○ Playgrounds
• ○ Prompt management

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• ○ Include demographically diverse data sources to improve model fairness.
• ○ Apply consistent labeling standards across the training dataset.
• ○ Focus exclusively on high-volume data regardless of quality.
• ○ Use only synthetic data to train the model.
• ○ Use fewer labels to reduce ambiguity in data during model training.

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• ○ AWS Audit Manager
• ○ Amazon SageMaker Model Cards
• ○ Amazon SageMaker Model Monitor
• ○ AWS Artifact

Answer is in the explanation below.

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• ○ Implement strict input validation and output formatting rules.
• ○ Implement Retrieval Augmented Generation (RAG) to ground responses.
• ○ Use smaller, more transparent models for medical applications.
• ○ Increase computational resources and model size.