AWS Certified AI Practitioner (AIF-C01) Practice Exam Questions and Answers – Part 7/8

Answer: B,E. Sentiment analysis || Fraud identification

Semi-supervised learning leverages a small amount of labeled data alongside a large volume of unlabeled data, which fits applications like sentiment analysis and fraud identification. Clustering and dimensionality reduction are strictly unsupervised.

Answer: B. Infrastructure as a Service (IaaS)

Amazon EC2 provides virtualized compute resources, categorizing it as Infrastructure as a Service because you manage the operating system and application. PaaS offerings, like Elastic Beanstalk, handle the underlying infrastructure automatically.

Answer: B. When temperature is set to 1

Setting the temperature to its maximum value randomizes word selection, increasing the probability of hallucinations in generative text models. A temperature of zero forces deterministic responses, effectively reducing hallucination risks.

Answer: D. Cloud computing refers to the on-demand delivery of IT resources and applications via the internet with pay-as-you-go pricing

Cloud computing is defined as the on-demand delivery of IT resources over the internet with pay-as-you-go pricing. For the exam, associate this model with flexibility and scalability instead of owning physical hardware.

Answer: A. Reinforcement learning focuses on an agent learning optimal actions through interactions with the environment and feedback, while supervised learning involves training models on labeled data to make predictions

Reinforcement learning uses an agent interacting with an environment to maximize cumulative rewards. Supervised learning, however, maps inputs to outputs using labeled datasets to make accurate predictions.

Answer: D. IAM Identity Center

IAM Identity Center is the recommended service to manage workforce access to Amazon Q Business applications. Standard IAM users are better suited for administrative AWS resource access rather than end-user application access.

Answer: A. Feature engineering for structured data often involves tasks such as normalization and handling missing values, while for unstructured data, it involves tasks such as tokenization and vectorization

Structured data feature engineering usually involves normalization and handling missing values. Unstructured data requires techniques like tokenization and vectorization to convert text or images into usable numerical formats.

Answer: B. AWS Audit Manager

AWS Audit Manager continuously audits your AWS usage by automating evidence collection to simplify risk assessments. AWS Artifact simply provides access to compliance reports and does not actively collect evidence.

Answer: C,E. Get answers to your AWS account-specific cost-related questions using natural language || Understand and manage your cloud infrastructure on AWS

Amazon Q Developer answers natural language questions about AWS costs and helps manage infrastructure. It cannot visualize cost data or autonomously deploy and modify resources, making those distractors incorrect.

Answer: D. The company should use a multi-modal embedding model, which is designed to represent and align different types of data (such as text and images) in a shared embedding space, allowing the chatbot to understand and interpret both forms of input simultaneously

A multimodal embedding model aligns text and images within a shared vector space for cost-effective search and querying. A generative model is overkill, more expensive, and better suited for creating new content.

Answer: A. A multimodal model can accept a mix of input types such as audio/text and create a mix of output types such as video/image

Multimodal models accept diverse inputs like text and audio while generating mixed outputs like images and video. The defining characteristic is handling multiple data formats simultaneously rather than being restricted to a single type.

Answer: A. A hiring algorithm consistently prefers candidates from a particular gender, even though the candidates' qualifications are similar across genders

Algorithmic bias occurs when a model systematically favors specific groups despite similar qualifications. Human judgment errors or random weather fluctuations fail to represent systematic machine learning bias.

Answer: B. SageMaker model cards include information about the model such as intended use and risk rating of a model, training details and metrics, evaluation results, and observations. AI service cards provide transparency about AWS AI services' intended use, limitations, and potential impacts

SageMaker model cards document custom model details like training metrics and risk ratings. AI service cards provide transparency for pre-trained AWS services regarding their intended use and limitations.

Answer: D. The company should iteratively test and adjust the chatbot prompts to ensure that its outputs consistently reflect the company's tone and style

Prompt engineering iteratively refines responses to match a specific tone or style. Lowering temperature reduces randomness but does not guarantee the desired tone, while batch inferencing and token limits are unrelated.

Answer: A. The company should use an optimized small language model (SLM) deployed directly on the edge device, allowing for real-time, low-latency inference

Deploying an optimized small language model directly on the edge device ensures real-time performance without network latency. Large models exceed edge hardware limits, while central APIs introduce unacceptable network delays.