AWS Certified AI Practitioner (AIF-C01) Practice Exam Questions and Answers – Part 2/4

Answer: A,B. Amazon Polly is used to deploy high-quality, natural-sounding human voices in dozens of languages || Amazon Comprehend service uses machine learning to find insights and relationships in the text

Amazon Polly converts text into lifelike speech, and Amazon Comprehend uses natural language processing to extract insights from text. The distractors mismatch the services, such as pairing Transcribe with conversational interfaces instead of Lex.

Answer: B. FMs use self-supervised learning to create labels from input data, however, fine-tuning an FM is a supervised learning process

Foundation models rely on self-supervised learning to generate implicit labels from vast amounts of raw data. Fine-tuning customizes the model for specific tasks using labeled examples, which makes it a supervised learning process.

Answer: C. OpenSearch Serverless vector store

Knowledge Bases for Amazon Bedrock natively defaults to OpenSearch Serverless for vector storage. Although it supports Pinecone and Aurora, the default store is automatically created if you do not provide one.

Answer: A. Amazon Transcribe and Amazon Comprehend

Amazon Transcribe converts audio calls into text. Amazon Comprehend then runs sentiment analysis on that text. Avoid Rekognition for audio, as it handles computer vision tasks.

Answer: C. The company should use hyperparameters for model tuning, which involves adjusting parameters such as regularization, learning rates, and dropout rates to enhance the model's ability to generalize well to new data

Hyperparameter tuning adjusts regularization and dropout rates to prevent overfitting, improving model generalization. While adding data helps, hyperparameter tuning specifically targets the gap between training and production.

Answer: D. Augment the data by generating new instances of data for underrepresented groups

Data augmentation directly balances underrepresented groups by creating new synthetic training examples, which mitigates bias. Manual correction is not scalable, regularization only prevents overfitting, and switching models ignores the root cause of bad data.

Answer: A. Amazon Rekognition

Amazon Rekognition offers pre-trained computer vision capabilities that you can customize for specific business needs. Textract is strictly for text extraction, while SageMaker is a broader service requiring you to build models from scratch.

Answer: C. Negative prompting

Negative prompting tells a generative AI model exactly what concepts or terms to exclude from its output. This is ideal for blocking competitor names or sensitive topics, whereas few-shot or chain-of-thought prompt techniques are used to guide desired content.

Answer: A. Foundation Models serve as a broad base for various AI applications by providing generalized capabilities, whereas Large Language Models are specialized for understanding and generating human language

Foundation Models are broad, general-purpose systems trained on massive datasets for various modalities like text or images. Large Language Models are a specialized subset of this category focused exclusively on understanding and generating human language.

Answer: D. Instructions, Context, Input data, Output Indicator

A well-crafted prompt consists of instructions, context, input data, and an output indicator. Remember that hyperparameters and parameters are configured at the model level, not manually embedded directly within the prompt text itself.

Answer: C. The data should include user-input along with the correct user intent, providing examples of user queries and the corresponding intent

Few-shot prompting teaches the model by pairing user inputs with their corresponding correct intent. Providing matching input and response pairs might show the final answer, but it fails to explicitly demonstrate the intermediate intent classification step.

Answer: A. The company should enable model invocation logging, which allows for detailed logging of all requests and responses during model invocations in Amazon Bedrock

Enabling model invocation logging in Amazon Bedrock captures the actual prompt inputs and generated outputs for auditing. CloudTrail only tracks control plane API calls for account security and does not record the payload data of the model responses.

Answer: B. Confusion matrix

A confusion matrix evaluates classification models by showing true positives, true negatives, false positives, and false negatives. Mean Absolute Error and Root Mean Squared Error are regression metrics designed for continuous numerical predictions instead of categorical ones.

Answer: A. Reinforcement learning involves an agent interacting with an environment by taking actions and receiving rewards or penalties, learning a policy to maximize cumulative rewards over time

Reinforcement learning uses an agent that interacts with an environment, receiving rewards or penalties to maximize cumulative success. It does not rely on labeled datasets like supervised learning or group unlabeled data like unsupervised learning.

Answer: C,D. The company should use Domain Adaptation Fine-Tuning, which involves fine-tuning the model on domain-specific data to adapt its knowledge to that particular domain || The company should use Continued Pre-Training, which involves further training the model on a large corpus of domain-specific data, enhancing its ability to understand domain-specific terms, jargon, and context

Domain Adaptation Fine-Tuning and Continued Pre-Training are the correct approaches because they use large amounts of unlabeled or labeled domain-specific data to teach the model specialized terminology and patterns. On the exam, remember that reinforcement learning relies on human feedback rather than raw domain knowledge.