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Amazon AIF-C01 Sample Question Answers
Question # 1
A company is building a large language model (LLM) question answering chatbot. The company wants to decrease the number of actions call center employees need to take to respond to customer questions.Which business objective should the company use to evaluate the effect of the LLM chatbot?
A. Website engagement rate B. Average call duration C. Corporate social responsibility D. Regulatory compliance
Answer: B
Explanation
The business objective to evaluate the effect of an LLM chatbot aimed at reducing the actions required by call
center employees should be average call duration.
Average Call Duration:
This metric measures the time taken to handle a customer call or query. A successful LLM chatbot should
reduce the call duration by efficiently providing answers, minimizing the need for human intervention.
By decreasing the average call duration, the company can improve call center efficiency, reduce costs, and
enhance the user experience.
Why Option B is Correct:
Direct Impact: The objective aligns directly with the goal of reducing the number of actions call center
employees must take.
Operational Efficiency: Reducing call duration is a clear indicator of the chatbot's effectiveness in assisting
customers without human help.
Why Other Options are Incorrect:
A. Website engagement rate: Is unrelated to call center operations.
C. Corporate social responsibility: Does not relate to call center efficiency.
D. Regulatory compliance: Is important but does not measure the effectiveness of a chatbot in reducing
employee actions.
Question # 2
A company is deploying AI/ML models by using AWS services. The company wants to offer transparency into the models' decision-making processes and provide explanations for the model outputs.
A. Amazon SageMaker Model Cards B. Amazon Rekognition C. Amazon Comprehend D. Amazon Lex
Answer: A
Explanation
Amazon SageMaker Model Cards document model details, performance, intended use cases, and risk
considerations. They support responsible AI by improving transparency and governance.
Rekognition is computer vision.
Comprehend is NLP for entity/sentiment.
Lex is conversational AI.
# Reference:
AWS Documentation – SageMaker Model Cards
Question # 3
What does an F1 score measure in the context of foundation model (FM) performance?
A. Model precision and recall B. Model speed in generating responses C. Financial cost of operating the model D. Energy efficiency of the model's computations
Answer: A
Explanation
The F1 score is a metric used to evaluate the performance of a classification model by considering both
precision and recall. Precision measures the accuracy of positive predictions (i.e., the proportion of true
positive predictions among all positive predictions made by the model), while recall measures the model's
ability to identify all relevant positive instances (i.e., the proportion of true positive predictions among all actual positive instances). The F1 score is the harmonic mean of precision and recall, providing a single
metric that balances both concerns. This is particularly useful when dealing with imbalanced datasets or when
the cost of false positives and false negatives is significant. Options B, C, and D pertain to other aspects of
model performance but are not related to the F1 score.
Reference: AWS Certified AI Practitioner Exam Guide
Question # 4
A software company wants to use a large language model (LLM) for workflow automation. The application will transform user messages into JSON files. The company will use the JSON files as inputs for data pipelines.The company has a labeled dataset that contains user messages and output JSON files.Which solution will train the LLM for workflow automation?
A. Unsupervised learning B. Continued pre-training C. Fine-tuning D. Reinforcement learning from human feedback (RLHF)
Answer: C
Explanation
Fine-tuning is the process of training a pre-trained LLM with a labeled dataset specific to a desired task—in
this case, mapping user messages to JSON outputs. Fine-tuning leverages supervised learning to specialize the
model’s outputs.
C is correct:
“Fine-tuning is a supervised learning approach in which a model is further trained on a custom, labeled
dataset to adapt to a specific use case.”
(Reference: Amazon Bedrock Fine-Tuning, AWS Certified AI Practitioner Study Guide)
A is incorrect—unsupervised learning does not use labeled data.
B (continued pre-training) uses unlabeled data.
D (RLHF) uses reward signals and human feedback, not direct labeled input/output pairs
Question # 5
A company is using large language models (LLMs) to develop online tutoring applications. The company needs to apply configurable safeguards to the LLMs. These safeguards must ensure that the LLMs follow standard safety rules when creating applications.Which solution will meet these requirements with the LEAST effort?
A. Amazon Bedrock playgrounds B. Amazon SageMaker Clarify C. Amazon Bedrock Guardrails D. Amazon SageMaker JumpStart
Answer: C
Explanation
The correct answer is C because Amazon Bedrock Guardrails provides out-of-the-box configurable safety
mechanisms to control the behavior of LLMs in generative AI applications. Guardrails can be configured with
denylists, content filters, sensitive topics, and tone enforcement, all without retraining the model.
From AWS documentation:
"Amazon Bedrock Guardrails allows developers to define safety and responsible AI policies directly in the
model inference layer, making it easy to prevent harmful, biased, or unsafe outputs with minimal
configuration."
Explanation of other options:
A. Bedrock playgrounds are interactive environments for testing prompts and models but do not provide
production-grade safety enforcement.
B. SageMaker Clarify focuses on bias detection and explainability for supervised ML models — it does not
directly apply guardrails to LLM outputs.
D. SageMaker JumpStart is for model fine-tuning and deployment, not for enforcing safety policies on LLM
AWS Certified ML Specialty Study Guide – Safety in Generative AI
Question # 6
A company wants to learn about generative AI applications in an experimental environment.Which solution will meet this requirement MOST cost-effectively?
A. Amazon Q Developer B. Amazon SageMaker JumpStart C. Amazon Bedrock PartyRock D. Amazon Q Business
Answer: C
Explanation
The correct answer is Amazon Bedrock PartyRock, a playground for building and experimenting with
generative AI apps in a low-cost, no-code environment. PartyRock is designed for innovation and learning. It
enables users to try out prompts, LLM apps, and templates using Amazon Bedrock under a free-tier friendly
setup. According to AWS, PartyRock abstracts infrastructure and allows rapid prototyping using models from
Bedrock providers. This makes it ideal for early experimentation, especially for non-developers or those not
ready to invest in full production pipelines. In contrast, Amazon Q Developer is for software engineering
tasks, SageMaker JumpStart focuses on deploying ML models, and Q Business targets enterprise knowledge
workers. None of those are as cost-effective and experimental-focused as PartyRock.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Documentation – PartyRock Overview
AWS Generative AI Learning Path – Getting Started Tools
Question # 7
A company wants to use Amazon Q Business for its data. The company needs to ensure the security and privacy of the data. Which combination of steps will meet these requirements? (Select TWO.)
A. Enable AWS Key Management Service (AWS KMS) keys for the Amazon Q Business Enterprise index. B. Set up cross-account access to the Amazon Q index. C. Configure Amazon Inspector for authentication. D. Allow public access to the Amazon Q index. E. Configure AWS Identity and Access Management (IAM) for authentication.
Answer: A E
Explanation
The correct answers are A and E because both directly align with AWS best practices for securing generative
AI services and data privacy in enterprise applications.
From the AWS Amazon Q Business documentation:
"AWS Key Management Service (KMS) integrates with Amazon Q Business to encrypt sensitive data at rest.
You can use customer-managed KMS keys to meet compliance requirements."
And:
"You must configure IAM access controls to manage which users and applications can access Amazon Q
Business indexes, ensuring that only authorized users can retrieve information."
Explanation of other options:
B. Cross-account access is not a common requirement for internal enterprise use of Amazon Q Business
unless explicitly sharing data across organizations. It’s not a requirement for securing access.
C. Amazon Inspector is a vulnerability management tool for EC2 and containers. It is unrelated to Amazon Q
authentication or security.
D. Allowing public access would violate security and privacy principles and directly contradict the stated
requirement.
Question # 8
A company wants to upload customer service email messages to Amazon S3 to develop a business analysis application. The messages sometimes contain sensitive data. The company wants to receive an alert every time sensitive information is found.Which solution fully automates the sensitive information detection process with the LEAST development effort?
A. Configure Amazon Macie to detect sensitive information in the documents that are uploaded to Amazon
S3. B. Use Amazon SageMaker endpoints to deploy a large language model (LLM) to redact sensitive data. C. Develop multiple regex patterns to detect sensitive data. Expose the regex patterns on an Amazon
SageMaker notebook. D. Ask the customers to avoid sharing sensitive information in their email messages.
Answer: A
Explanation
The correct answer is A because Amazon Macie is a fully managed data security and privacy service that uses
machine learning to automatically detect sensitive data such as PII (personally identifiable information) in
Amazon S3. It requires no custom development, and it can be configured to generate alerts when sensitive
data is detected in newly uploaded objects.
From AWS documentation:
"Amazon Macie automatically discovers and classifies sensitive data in S3 buckets and generates alerts when
it detects sensitive content, such as names, addresses, and credit card numbers."
Explanation of other options:
B. Deploying an LLM on SageMaker to perform redaction is custom and operationally intensive.
C. Regex-based detection is brittle and requires extensive manual work, with high maintenance overhead.
D. Asking customers to avoid sharing sensitive data is not enforceable and does not meet compliance or
security standards.
Referenced AWS AI/ML Documents and Study Guides:
Question # 9
A company wants to build a lead prioritization application for its employees to contact potential customers. The application must give employees the ability to view and adjust the weights assigned to different variables in the model based on domain knowledge and expertise.Which ML model type meets these requirements?
A. Logistic regression model B. Deep learning model built on principal components C. K-nearest neighbors (k-NN) model D. Neural network
Answer: B
Question # 10
An ecommerce company is deploying a chatbot. The chatbot will give users the ability to ask questions about the company's products and receive details on users' orders. The company must implement safeguards for the chatbot to filter harmful content from the input prompts and chatbot responses.Which AWS feature or resource meets these requirements?
A. Amazon Bedrock Guardrails B. Amazon Bedrock Agents C. Amazon Bedrock inference APIs D. Amazon Bedrock custom models
Answer: A
Explanation
The ecommerce company is deploying a chatbot that needs safeguards to filter harmful content from input
prompts and responses. Amazon Bedrock Guardrails provide mechanisms to ensure responsible AI usage by filtering harmful content, such as hate speech, violence, or misinformation, making it the appropriate feature
for this requirement.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Amazon Bedrock Guardrails enable developers to implement safeguards for generative AI applications, such
as chatbots, by filtering harmful content in input prompts and model responses. Guardrails include content
filters, word filters, and denied topics to ensure safe and responsible outputs."
(Source: AWS Bedrock User Guide, Guardrails for Responsible AI)
Detailed Explanation:
Option A: Amazon Bedrock GuardrailsThis is the correct answer. Amazon Bedrock Guardrails are
specifically designed to filter harmful content from chatbot inputs and responses, ensuring safe interactions
for users.
Option B: Amazon Bedrock AgentsAmazon Bedrock Agents are used to automate tasks and integrate with
external tools, not to filter harmful content. This option does not meet the requirement.
Option C: Amazon Bedrock inference APIsAmazon Bedrock inference APIs allow users to invoke foundation
models for generating responses, but they do not provide built-in content filtering mechanisms.
Option D: Amazon Bedrock custom modelsCustom models on Amazon Bedrock allow users to fine-tune
models, but they do not inherently include safeguards for filtering harmful content unless explicitly
A hospital wants to use a generative AI solution with speech-to-text functionality to help improve employee skills in dictating clinical notes.
A. Amazon Q Developer B. Amazon Polly C. Amazon Rekognition D. AWS HealthScribe
Answer: D
Explanation
AWS HealthScribe provides speech-to-text and medical documentation generation, specifically designed for
healthcare applications.
Amazon Polly is text-to-speech, not speech-to-text.
Amazon Rekognition is computer vision.
Amazon Q Developer is a generative AI assistant for developers, not healthcare.
# Reference:
AWS Documentation – AWS HealthScribe
Question # 12
A company wants to develop an Al application to help its employees check open customer claims, identify details for a specific claim, and access documents for a claim. Which solution meets these requirements?
A. Use Agents for Amazon Bedrock with Amazon Fraud Detector to build the application. B. Use Agents for Amazon Bedrock with Amazon Bedrock knowledge bases to build the application. C. Use Amazon Personalize with Amazon Bedrock knowledge bases to build the application. D. Use Amazon SageMaker AI to build the application by training a new ML model.
Answer: B
Explanation
The company wants an AI application to help employees check open customer claims, identify claim details,
and access related documents. Agents for Amazon Bedrock can automate tasks by interacting with external
systems, while Amazon Bedrock knowledge bases provide a repository of information (e.g., claim details and
documents) that the agent can query to respond to employee requests, making this the best solution.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Agents for Amazon Bedrock enable developers to build applications that can perform tasks by interacting
with external systems and data sources. When paired with Amazon Bedrock knowledge bases, agents can
access structured and unstructured data, such as documents or databases, to provide detailed responses for use
cases like customer service or claims management."
(Source: AWS Bedrock User Guide, Agents and Knowledge Bases)
Detailed Explanation:
Option A: Use Agents for Amazon Bedrock with Amazon Fraud Detector to build the application.Amazon
Fraud Detector is for detecting fraudulent activities, not for managing customer claims or accessing
documents. This option is irrelevant.
Option B: Use Agents for Amazon Bedrock with Amazon Bedrock knowledge bases to build the application.
This is the correct answer. Agents for Amazon Bedrock can interact with knowledge bases to retrieve claim
details and documents, enabling employees to check open claims and access relevant information.
Option C: Use Amazon Personalize with Amazon Bedrock knowledge bases to build the application.Amazon
Personalize is for building recommendation systems, not for retrieving claim details or documents. This
option does not meet the requirements.
Option D: Use Amazon SageMaker AI to build the application by training a new ML model.Training a new
ML model on SageMaker is unnecessary and complex for this use case, as the task can be efficiently handled
A company wants to use AWS services to build an AI assistant for internal company use. The AI assistant's responses must reference internal documentation. The company stores internal documentation as PDF, CSV, and image files.Which solution will meet these requirements with the LEAST operational overhead?
A. Use Amazon SageMaker AI to fine-tune a model. B. Use Amazon Bedrock Knowledge Bases to create a knowledge base. C. Configure a guardrail in Amazon Bedrock Guardrails. D. Select a pre-trained model from Amazon SageMaker JumpStart.
Answer: B
Explanation
The best solution is Amazon Bedrock Knowledge Bases, which allows for the seamless integration of
structured and unstructured internal documents—such as PDFs, CSVs, and extracted image text—into a
retrieval-augmented generation (RAG) pipeline. According to AWS documentation, Bedrock Knowledge
Bases offer a no-code or low-code setup to link your enterprise data with foundation models for context-aware
responses, without needing to fine-tune or retrain models. The system indexes documents in an Amazon S3
bucket, creates embeddings, and stores them in a vector store. At inference time, the model retrieves relevant
context and incorporates it into its response. This approach provides dynamic and up-to-date responses while
maintaining data privacy, with minimal operational overhead. Unlike fine-tuning or building a model from
scratch in SageMaker, which requires considerable compute resources and model management, Bedrock
Knowledge Bases are serverless and easy to configure. It is designed exactly for internal knowledge AI
assistants.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Developer Guide – Knowledge Bases
AWS Generative AI Best Practices – RAG Patterns for Enterprise Search
Question # 14
A company needs to use Amazon SageMaker AI for model training and inference. The company must comply with regulatory requirements to run SageMaker jobs in an isolated environment without internet access.Which solution will meet these requirements?
A. Run SageMaker training and inference by using SageMaker Experiments. B. Run SageMaker training and inference by using network isolation. C. Encrypt the data at rest by using encryption for SageMaker geospatial capabilities. D. Associate appropriate AWS Identity and Access Management (IAM) roles with the SageMaker jobs.
Answer: B
Explanation
Network isolation is a key security feature for SageMaker. It ensures that training and inference jobs run in a
VPC and are not accessible from the internet. Per the official SageMaker documentation:
“When you enable network isolation, your model can’t make any outbound network calls. This is useful for
security and regulatory compliance when working with sensitive data.”
Question # 15
An AI practitioner is building a model to generate images of humans in various professions. The AI practitioner discovered that the input data is biased and that specific attributes affect the image generation and create bias in the model.Which technique will solve the problem?
A. Data augmentation for imbalanced classes B. Model monitoring for class distribution C. Retrieval Augmented Generation (RAG) D. Watermark detection for images
Answer: A
Explanation
Data augmentation for imbalanced classes is the correct technique to address bias in input data affecting
image generation.
Data Augmentation for Imbalanced Classes:
Involves generating new data samples by modifying existing ones, such as flipping, rotating, or cropping
images, to balance the representation of different classes.
Helps mitigate bias by ensuring that the training data is more representative of diverse characteristics and
scenarios.
Why Option A is Correct:
Balances Data Distribution: Addresses class imbalance by augmenting underrepresented classes, which
reduces bias in the model.
Improves Model Fairness: Ensures that the model is exposed to a more diverse set of training examples,
promoting fairness in image generation.
Why Other Options are Incorrect:
B. Model monitoring for class distribution: Helps identify bias but does not actively correct it.
C. Retrieval Augmented Generation (RAG): Involves combining retrieval and generation but is unrelated to
mitigating bias in image generation.
D. Watermark detection for images: Detects watermarks in images, not a technique for addressing bias.
Question # 16
A company wants to use an ML model to analyze customer reviews on social media. The model must determine if each review has a neutral, positive, or negative sentiment.
A. Open-ended generation B. Text summarization C. Machine translation D. Classification
Answer: D
Explanation
The correct answer is D – Classification. In this scenario, the goal is to assign each social media review to one
of three predefined categories: positive, negative, or neutral. According to AWS documentation, classification
models are used when “inputs must be mapped to one label from a fixed set of possible labels.” Sentiment
analysis is one of the most common NLP classification tasks and is supported in Amazon Comprehend,
Amazon SageMaker, and Amazon Bedrock. Open-ended generation produces free-form text and is not
appropriate for categorical outputs. Summarization condenses long-form content, and machine translation
converts text across languages. Only classification aligns with sentiment detection, enabling the model to
learn sentiment cues such as emotional wording or tone markers. AWS highlights sentiment classification as a
key use case for supervised learning with text data.
AWS ML Specialty Guide – Classification Algorithms
Question # 17
A company is developing an AI solution to help make hiring decisions.Which strategy complies with AWS guidance for responsible AI?
A. Use the AI solution to make final hiring decisions without human review. B. Train the AI solution exclusively on data from previous successful hires. C. Test the AI solution to ensure that it does not discriminate against any protected groups. D. Keep the AI decision-making process confidential to maintain a competitive advantage.
Answer: C
Explanation
The correct answer is C – Test the AI solution to ensure that it does not discriminate against any protected
groups. According to AWS Responsible AI principles, fairness and bias mitigation are essential when AI is
used for high-impact decisions such as hiring. AWS documentation emphasizes evaluating datasets, model
outputs, and demographic performance to ensure that AI systems do not reinforce or reproduce discriminatory
patterns. Services such as Amazon SageMaker Clarify support automated bias detection and explainability,
helping teams identify and mitigate unwanted correlations in training data or model predictions. Option A
violates AWS guidance, as human-in-the-loop review is required for sensitive decisions. Option B risks
amplifying historical bias because training on only “successful” hires can create feedback loops. Option D
contradicts transparency principles, which AWS states are crucial for accountability in regulated or ethical
decision-making domains. Therefore, rigorous fairness testing aligns with AWS’s recommended practices for
responsible AI in hiring workflows.
Referenced AWS Documentation:
AWS Responsible AI Whitepaper – Fairness and Bias Mitigation
Amazon SageMaker Clarify Documentation
Question # 18
A company wants to collaborate with several research institutes to develop an AI model. The company needs standardized documentation of model version tracking and a record of model development.Which solution meets these requirements?
A. Track the model changes by using Git. B. Track the model changes by using Amazon Fraud Detector. C. Track the model changes by using Amazon SageMaker Model Cards. D. Track the model changes by using Amazon Comprehend.
Answer: C
Explanation
Amazon SageMaker Model Cards provide a standardized way to document and track model information,
including versions and performance. According to AWS documentation:
“SageMaker Model Cards provide a single source of truth for model information including intended use,
training details, evaluation metrics, and ethical considerations to support governance and collaboration.”
Question # 19
What is tokenization used for in natural language processing (NLP)?
A. To encrypt text data B. To compress text files C. To break text into smaller units for processing D. To translate text between languages
Answer: C
Explanation
The correct answer is C because tokenization is the NLP process of breaking down text into smaller units,
such as words, subwords, or characters, that can be processed by ML models.
From AWS documentation:
"Tokenization is the process of splitting text into meaningful units, such as words or subwords, that are used
as input tokens in NLP tasks. Tokenization is an essential step in preparing text data for models."
This is a foundational concept used in language models, including those on Amazon Bedrock and SageMaker.
Explanation of other options:
A. Encryption is not related to NLP and is used for data security.
B. Compression reduces file size but is unrelated to language processing.
D. Translation is a separate NLP task that uses tokenization as a preprocessing step but is not the definition of
Amazon Bedrock Foundation Model Documentation – Tokenization and Input Formatting
Question # 20
A company is using an Amazon Nova Canvas model to generate images. The model generates images successfully. The company needs to prevent the model from including specific items in the generated images.Which solution will meet this requirement?
A. Use a higher temperature value. B. Use a more detailed prompt. C. Use a negative prompt. D. Use another foundation model (FM).
Answer: C
Explanation
The correct answer is C – Use a negative prompt. Negative prompts instruct a generative image model to
avoid certain features, objects, or styles in the output. This technique is fully supported by models like
Amazon Nova Canvas on Bedrock, which are based on diffusion or image generation architectures.
According to AWS documentation, negative prompts refine output control by telling the model what not to
include, thereby improving brand alignment, compliance, or creative direction. A higher temperature
increases randomness, not control. A detailed prompt helps, but without exclusion instructions, the model may
still include unwanted elements. Changing the model may yield better output but doesn’t directly solve this
control requirement. Negative prompts are purpose-built for this scenario.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Documentation – Prompt Engineering for Image Models
AWS Generative AI Guide – Controlled Generation with Negative Prompts
Question # 21
A company wants to use AI to protect its application from threats. The AI solution needs to check if an IP address is from a suspicious source.
A. Build a speech recognition system B. Create a natural language processing (NLP) named entity recognition system C. Develop an anomaly detection system D. Create a fraud forecasting system
Answer: C
Explanation
Anomaly detection identifies unusual behavior (such as suspicious IP traffic) compared to normal baselines.
Speech recognition (A) is irrelevant.
NER in NLP (B) extracts entities from text, not detect malicious IPs.
Fraud forecasting (D) predicts fraudulent transactions but not directly suspicious IP activity.
# Reference:
AWS Documentation – Anomaly Detection
Question # 22
A company's large language model (LLM) is experiencing hallucinations.How can the company decrease hallucinations?
A. Set up Agents for Amazon Bedrock to supervise the model training. B. Use data pre-processing and remove any data that causes hallucinations. C. Decrease the temperature inference parameter for the model. D. Use a foundation model (FM) that is trained to not hallucinate.
Answer: C
Explanation
Hallucinations in large language models (LLMs) occur when the model generates outputs that are factually
incorrect, irrelevant, or not grounded in the input data. To mitigate hallucinations, adjusting the model's
inference parameters, particularly the temperature, is a well-documented approach in AWS AI Practitioner
resources. The temperature parameter controls the randomness of the model's output. A lower temperature
makes the model more deterministic, reducing the likelihood of generating creative but incorrect responses,
which are often the cause of hallucinations.
Exact Extract from AWS AI Documents:
From the AWS documentation on Amazon Bedrock and LLMs:
"The temperature parameter controls the randomness of the generated text. Higher values (e.g., 0.8 or above)
increase creativity but may lead to less coherent or factually incorrect outputs, while lower values (e.g., 0.2 or
0.3) make the output more focused and deterministic, reducing the likelihood of hallucinations."
(Source: AWS Bedrock User Guide, Inference Parameters for Text Generation)
Detailed Explanation:
Option A: Set up Agents for Amazon Bedrock to supervise the model training.Agents for Amazon Bedrock
are used to automate tasks and integrate LLMs with external tools, not to supervise model training or directly
address hallucinations. This option is incorrect as it does not align with the purpose of Agents in Bedrock.
Option B: Use data pre-processing and remove any data that causes hallucinations.While data pre-processing
can improve model performance, identifying and removing specific data that causes hallucinations is
impractical because hallucinations are often a result of the model's generative process rather than specific
problematic data points. This approach is not directly supported by AWS documentation for addressing
hallucinations.
Option C: Decrease the temperature inference parameter for the model.This is the correct approach. Lowering
the temperature reduces the randomness in the model's output, making it more likely to stick to factual and
A media streaming platform wants to provide movie recommendations to users based on the users' account history.
A. Amazon Polly B. Amazon Comprehend C. Amazon Transcribe D. Amazon Personalize
Answer: D
Explanation
Amazon Personalize is a fully managed ML service for personalized recommendations (movies, products,
music, etc.) based on user behavior and history.
Polly converts text to lifelike speech.
Comprehend performs NLP tasks like sentiment analysis.
Transcribe is speech-to-text.
# Reference:
AWS Documentation – Amazon Personalize
Question # 24
A financial company uses a generative AI model to assign credit limits to new customers. The company wants to make the decision-making process of the model more transparent to its customers.
A. Use a rule-based system instead of an ML model. B. Apply explainable AI techniques to show customers which factors influenced the model's decision. C. Develop an interactive UI for customers and provide clear technical explanations about the system. D. Increase the accuracy of the model to reduce the need for transparency.
Answer: B
Explanation
The correct answer is B because explainable AI (XAI) provides transparency into how models reach specific
decisions. According to AWS documentation, techniques such as SHAP values (SHapley Additive
exPlanations) or LIME can identify which input features (e.g., income, debt ratio, or credit history) most
influenced a model’s prediction. This helps financial institutions comply with fairness and transparency
requirements under regulatory frameworks like the Equal Credit Opportunity Act. AWS SageMaker Clarify is
a built-in service that offers explainability reports and bias detection to enhance trust. Rule-based systems and
UIs alone do not satisfy transparency standards, and accuracy improvements do not replace explainability. By
implementing explainable AI, customers can understand and trust credit limit decisions, reducing bias
concerns and ensuring compliance.
Referenced AWS AI/ML Documents and Study Guides:
Amazon SageMaker Clarify Documentation – Explainability and Feature Attribution
AWS Responsible AI Practices – Transparency and Accountability
Question # 25
A company has developed an ML model to predict real estate sale prices. The company wants to deploy the model to make predictions without managing servers or infrastructure.Which solution meets these requirements?
A. Deploy the model on an Amazon EC2 instance. B. Deploy the model on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. C. Deploy the model by using Amazon CloudFront with an Amazon S3 integration. D. Deploy the model by using an Amazon SageMaker AI endpoint.
Answer: D
Explanation
Amazon SageMaker endpoints provide fully managed, serverless model deployment for real-time and batch
predictions, allowing companies to deploy ML models without handling any servers or infrastructure
management.
D is correct: SageMaker endpoints let you deploy, scale, and monitor ML models with no infrastructure
overhead.
A and B require infrastructure management.
C (CloudFront/S3) is not for model deployment, but for static content delivery.
“Amazon SageMaker endpoints allow you to deploy machine learning models for inference without the need
to manage underlying infrastructure.”
(Reference: AWS SageMaker Model Deployment, AWS Certified AI Practitioner Study Guide)
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