AWS Certified AI Practitioner (AIF-C01) Practice Exam Questions and Answers – Part 11/12

Answer: D. Influences the number of most-likely candidates that the model considers for the next token

The Top K parameter limits the token selection pool to the most likely candidates, directly controlling text diversity. Do not confuse this with Top P, which uses a probability percentage cutoff, or Temperature, which scales the overall creativity.

Answer: B,D. In traditional machine learning, a data scientist manually determines the set of relevant features that the software must analyze, whereas in deep learning, the data scientist gives only raw data to the software and the deep learning network derives the features by itself || Deep learning is a subset of machine learning that uses neural networks with many layers to learn from large amounts of data, while traditional machine learning algorithms often require feature extraction and can use various methods such as decision trees or support vector machines

Deep learning automatically extracts features from raw data using neural networks, whereas traditional machine learning requires manual feature engineering. Remember that deep learning typically demands large datasets and computational power, unlike simpler traditional models.

Answer: D. LLMs are used for generating human-like text, translating languages, summarizing text, and answering questions based on large datasets

Large language models excel at text-based tasks like summarization, translation, and answering questions. They are not designed to generate multimedia assets like 3D models, video, or audio directly, which require specialized generative architectures.

Answer: B. K-Means is an unsupervised learning algorithm used for clustering data points into groups, while KNN is a supervised learning algorithm used for classifying data points based on their proximity to labeled examples

K-Means is an unsupervised clustering algorithm that groups unlabelled data, whereas K-Nearest Neighbors is a supervised classification technique relying on labeled examples. Avoid confusing these two solely because they both start with the letter K.

Answer: B. RAG refers to querying and retrieving information from a data source to augment a generated response to a prompt, whereas, Agent refers to an application that carries out orchestrations through cyclically interpreting inputs and producing outputs by using a foundation model

Retrieval Augmented Generation strictly retrieves external data to enhance a prompt, while an agent actively orchestrates multistep tasks by interpreting inputs and executing actions. Agents often utilize data retrieval as just one tool within a broader automated workflow.

Answer: A. By learning patterns from existing data and using algorithms to generate new content that mimics those patterns

Generative AI creates new content by learning underlying patterns from training data and using those learned distributions to generate novel, similar outputs. For the exam, remember that generative AI goes beyond rigid templates or traditional programming; it actively learns from data rather than relying on hard-coded rules.

Answer: C. Data residency refers to where data is physically stored, while data logging tracks data access and changes over time

Data residency dictates the physical or geographical location where data is stored, ensuring compliance with local regulations. Data logging records access and modifications to that data, creating a necessary audit trail for security and troubleshooting.

Answer: C. The company should use benchmark datasets, which are pre-compiled, standardized datasets specifically designed to test for biases and discrimination in model outputs

Standardized benchmark datasets provide a reliable, pre-compiled resource to test large language models for bias without requiring extensive manual setup. Generating synthetic data or relying on manual human review demands significantly more administrative effort and resources to maintain.

Answer: C. Feature extraction reduces the number of features by transforming data into a new space, while feature selection reduces the number of features by selecting the most relevant ones from the existing features

Feature extraction transforms original data into a completely new set of features, such as using principal component analysis to reduce dimensionality. Feature selection simply chooses the most relevant variables from the existing dataset without altering their underlying structure.

Answer: D. AWS Config

AWS Config continuously monitors and records resource configurations, allowing automated compliance checks against your desired baselines. While AWS Audit Manager and Artifact handle compliance reporting and audits, they lack the active, continuous resource configuration tracking required here.

Answer: C,E. AWS Lambda || Amazon Rekognition

AWS Lambda and Amazon Rekognition are regional services, meaning you provision and manage them within a specific AWS region. Distractors like IAM, CloudFront, and Web Application Firewall operate globally to provide consistent security and edge delivery across all regions.

Answer: B. Prompt engineering, Retrieval Augmented Generation (RAG), Fine-tuning

Prompt engineering is the simplest customization method, followed by Retrieval Augmented Generation which requires vector stores, while fine-tuning demands deep data science expertise. Fine-tuning alters model weights, making it the most complex and resource-intensive technique of the three.

Answer: A. Guardrails for Amazon Bedrock

Guardrails for Amazon Bedrock is the correct choice because it lets you block harmful content and mask PII directly within your generative AI workflows. Knowledge Bases is for Retrieval Augmented Generation, not content filtering or privacy protection.

Answer: C. To offer transparency and information about the intended use, limitations, and potential impacts of AWS AI services, helping users implement Responsible AI practices

AWS AI service cards provide transparency by detailing intended uses and limitations to support Responsible AI practices. Distractors about technical documentation or a prepaid marketplace fail because the cards strictly focus on ethical implementation guidance.

Answer: B. Yes, it is possible to increase both bias and variance, but this typically leads to a model that performs poorly due to both underfitting and overfitting

Bias and variance can both increase simultaneously, which usually yields a poorly performing model that underfits and overfits. While they often exhibit a tradeoff, removing training data or using a flawed algorithm can incorrectly increase both errors.