AWS Certified AI Practitioner (AIF-C01) Practice Exam Questions and Answers – Part 3/4

Answer: D. Exploratory Data Analysis (EDA)

Exploratory Data Analysis is correct because calculating statistical summaries and generating visualizations to uncover trends is the core focus of EDA. Data preparation is the strongest distractor, but it involves cleaning and transforming data rather than exploring it for initial insights.

Answer: A. The company should use Amazon OpenSearch Service, which is designed to provide fast search capabilities and supports full-text search, indexing, and similarity scoring

Amazon OpenSearch Service is the correct choice because it provides native support for vector searches, fast index lookups, and similarity scoring required for retrieval-augmented generation workloads. Relational databases like Aurora lack these specialized semantic search capabilities.

Answer: A. Top K

Top K is the correct parameter because it directly limits the token pool to a specific number of the most likely next candidates. Temperature controls randomness globally, while Top P uses a probability threshold instead of a fixed count.

Answer: D. Amazon SageMaker Model Dashboard

The SageMaker Model Dashboard provides a centralized portal to track deployed models, monitor endpoints, and troubleshoot real-time inference issues. JumpStart is for deploying pre-trained models, while Clarify focuses solely on detecting bias in your data.

Answer: A. Sampling bias

Sampling bias is correct because the skewed outcomes stem directly from certain demographic groups being overrepresented or underrepresented in the training data. Measurement bias involves faulty sensors, while confirmation bias applies to human expectations.

Answer: C. Enable multilingual user experiences in your applications

Enabling multilingual experiences is the correct answer because translation tasks belong to Amazon Translate, not Rekognition. Rekognition is strictly for computer vision tasks like facial recognition, celebrity detection, and searching image libraries.

Answer: B. The company should use Provisioned Throughput mode, which allows the company to reserve a specific amount of capacity in advance

Provisioned Throughput is correct because Amazon Bedrock requires you to purchase this reserved capacity to test and deploy any models customized via fine-tuning. On-Demand mode only applies to base foundation models and cannot access your customizations.

Answer: B. Use higher Temperature to get more creative responses for the same prompt on Amazon Bedrock

A higher Temperature value is the correct setting because it increases the randomness of token selection, yielding more creative and diverse responses. Lower temperature values force the model to produce deterministic and predictable outputs.

Answer: D. Generative AI encompasses models and algorithms capable of creating new content such as text, images, and audio based on patterns learned from existing data

Generative AI creates new content like text, images, and audio by learning patterns from existing data. Options focusing on data retrieval, predefined tasks, or just analyzing data without creating content are incorrect because they fail to produce original output.

Answer: C. The company should reduce the number of tokens in the input

Generative AI model costs on Amazon Bedrock are calculated based on the number of input and output tokens processed. Adjusting temperature or top-P affects the randomness and diversity of the output but does not lower the usage price.

Answer: D,E. You can evaluate, compare, and select Foundation Models quickly based on pre-defined quality and responsibility metrics || Pre-trained models are fully customizable for your use case with your data

SageMaker JumpStart allows users to quickly evaluate foundation models and deploy customizable pre-trained models. SageMaker Canvas is the AWS no-code solution, while training data is never used to update the base models surfaced in JumpStart.

Answer: B. Model evaluation is the process of evaluating and comparing model outputs to determine the model that is best suited for a use case, whereas, model inference is the process of a model generating an output (response) from a given input (prompt)

Model inference is the act of a model generating an output from a given prompt, whereas model evaluation is the process of comparing these outputs to find the best model. The other options incorrectly swap or merge these two distinct concepts.

Answer: A. Manage machine learning experiments

MLflow integrates with SageMaker to help data scientists track, organize, and compare iterative machine learning experiments. Automatic tuning is handled by SageMaker AMT, while Canvas provides no-code tools and Ground Truth handles data labeling.

Answer: D. Transformer models use a self-attention mechanism and implement contextual embeddings

Transformer models use self-attention mechanisms to weigh the importance of words in a sequence alongside contextual embeddings. The other options describe generative adversarial networks, variational autoencoders, and diffusion models instead of transformers.

Answer: C. Bidirectional Encoder Representations from Transformers (BERT)

BERT uses bidirectional context to generate dynamic word embeddings, capturing nuanced meanings based on surrounding text. Older models like Word2Vec rely on static embeddings, while SVD and PCA are dimensionality reduction techniques that lack language context.