AWS Certified AI Practitioner (AIF-C01) Practice Exam Questions and Answers – Part 5/8

Answer: C. Fine-tuning changes the weights of the FM whereas Retrieval-augmented generation (RAG) does not change the weights of the FM

Fine-tuning alters the underlying weights of a foundation model through additional training. Retrieval-augmented generation, or RAG, leaves model weights unchanged and instead grounds responses using external knowledge sources.

Answer: B. Amazon SageMaker Feature Store

Amazon SageMaker Feature Store is a purpose-built repository designed to store, share, and manage features for machine learning models. Data Wrangler is used for data preparation, Clarify detects bias, and Ground Truth handles data labeling.

Answer: B. Feature Engineering involves selecting, modifying, or creating features from raw data to improve the performance of machine learning models, and it is important because it can significantly enhance model accuracy and efficiency

Feature engineering involves selecting, modifying, or creating features from raw data to significantly improve machine learning model accuracy. Distractors incorrectly confuse feature engineering with data collection, visualization, or hyperparameter tuning.

Answer: A,C. Amazon SageMaker Model Monitor || Amazon SageMaker Clarify

SageMaker Clarify detects bias and explains predictions to ensure fairness, while SageMaker Model Monitor tracks production models for drift. The other services are general security or compliance tools, not specifically responsible AI practices.

Answer: C. Chain-of-thought prompting

Chain-of-thought prompting breaks complex problems into smaller, logical intermediate steps to improve model reasoning capabilities. Zero-shot and few-shot prompting provide examples rather than explicitly decomposing the reasoning process.

Answer: A,E. Amazon SageMaker Model Monitor || Amazon Augmented AI (Amazon A2I)

SageMaker Model Monitor tracks deployed models for data drift, while Amazon A2I provides human review workflows for low-confidence predictions. Ground Truth handles training data labeling, not production model oversight.

Answer: B. A token is a sequence of characters that a model can interpret or predict as a single unit of meaning, whereas, an embedding is a vector of numerical values that represents condensed information obtained by transforming input into that vector

A token is a sequence of characters processed as a single unit of meaning, whereas an embedding represents that input as a numerical vector. Embeddings condense information into mathematical space for similarity comparisons.

Answer: C. Foundation models can perform a wide range of tasks across different domains by leveraging their extensive pre-training on large datasets

Foundation models leverage extensive pre-training on massive datasets to generalize across multiple domains and perform diverse tasks. They are not restricted to single tasks or solely structured data like legacy machine learning systems.

Answer: B. Top P

Top P defines the percentage of most likely candidates considered for the next token, controlling the output pool dynamically. Top K sets a strict number of candidates, while Temperature controls overall randomness.

Answer: C. The model can recreate new animal images that were not in the training dataset

Generative models learn the underlying distribution of training data to synthesize entirely new, unseen examples. Discriminative models, like classifiers, only map inputs to specific labels. Use the term recreate to spot the generative choice.

Answer: A. Image processing focuses on enhancing and manipulating images for visual quality, whereas computer vision involves interpreting and understanding the content of images to make decisions

Image processing handles pixel manipulations like filtering and compression, whereas computer vision interprets image content to make automated decisions. Remember that vision systems aim to mimic human understanding, not just alter pixels.

Answer: D. Model training in deep learning involves using large datasets to adjust the weights and biases of a neural network through multiple iterations, using techniques such as gradient descent to minimize the error

Deep learning training iteratively adjusts neural network weights and biases using large datasets and optimization techniques like gradient descent to minimize error. Network weights are never set manually using predefined rules.

Answer: C,D. They facilitate easier debugging and optimization || They foster trust and confidence in model predictions

Transparent models facilitate easier debugging by revealing how inputs become outputs and foster trust among stakeholders in high-stakes industries. Computational power and integration complexity depend on architecture, not model transparency.

Answer: A. Neural networks consist of layers of nodes (neurons) that process input data, adjusting the weights of connections between nodes through training to recognize patterns and make predictions

Neural networks rely on layers of interconnected nodes that process inputs and adjust connection weights during training to recognize patterns. They learn directly from data rather than relying on explicit programming or storing outcomes.

Answer: A. Applying multiple layers of security measures including input validation, access controls, and continuous monitoring to address vulnerabilities

Defense-in-depth relies on applying multiple overlapping security measures like input validation, strict access controls, and continuous monitoring. Relying on a single security layer, such as just encryption, leaves the application vulnerable.

Answer: B. Amazon SageMaker Model Dashboard

Amazon SageMaker Model Dashboard provides a centralized visual interface to track, manage, and monitor all machine learning models across your AWS account. Model Monitor only checks production data quality without providing a holistic model inventory.