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Amazon AIF-C01 Sample Question Answers
Question # 1
A bank is fine-tuning a large language model (LLM) on Amazon Bedrock to assist customers with questions about their loans. The bank wants to ensure that the model does not reveal any private customer data.Which solution meets these requirements?
A. Use Amazon Bedrock Guardrails. B. Remove personally identifiable information (PII) from the customer data before fine-tuning the LLM. C. Increase the Top-K parameter of the LLM. D. Store customer data in Amazon S3. Encrypt the data before fine-tuning the LLM.
Answer: B
Explanation
The goal is to prevent a fine-tuned large language model (LLM) on Amazon Bedrock from revealing private
customer data. Let’s analyze the options:
A. Amazon Bedrock Guardrails: Guardrails in Amazon Bedrock allow users to define policies to filter
harmful or sensitive content in model inputs and outputs. While useful for real-time content moderation, they
do not address the risk of private data being embedded in the model during fine-tuning, as the model could
still memorize sensitive information.
B. Remove personally identifiable information (PII) from the customer data before fine-tuning the LLM:
Removing PII (e.g., names, addresses, account numbers) from the training dataset ensures that the model does not learn or memorize sensitive customer data, reducing the risk of data leakage. This is a proactive and
effective approach to data privacy during model training.
C. Increase the Top-K parameter of the LLM: The Top-K parameter controls the randomness of the model’s
output by limiting the number of tokens considered during generation. Adjusting this parameter affects output
diversity but does not address the privacy of customer data embedded in the model.
D. Store customer data in Amazon S3. Encrypt the data before fine-tuning the LLM: Encrypting data in
Amazon S3 protects data at rest and in transit, but during fine-tuning, the data is decrypted and used to train
the model. If PII is present, the model could still learn and potentially expose it, so encryption alone does not
solve the problem.
Exact Extract Reference: AWS emphasizes data privacy in AI/ML workflows, stating, “To protect sensitive
data, you can preprocess datasets to remove personally identifiable information (PII) before using them for
model training. This reduces the risk of models inadvertently learning or exposing sensitive information.”
AWS AI Practitioner Study Guide (emphasis on data privacy in LLM fine-tuning
Question # 2
Sentiment analysis is a subset of which broader field of AI?
A. Computer vision B. Robotics C. Natural language processing (NLP) D. Time series forecasting
Answer: C
Explanation
Sentiment analysis is the task of determining the emotional tone or intent behind a body of text (positive,
negative, neutral).
This falls under Natural Language Processing (NLP) because it deals with understanding and processing
human language.
Computer vision relates to images, robotics to autonomous machines, and time series forecasting to predicting
values from sequential data.
# Reference:
AWS ML Glossary – NLP
Question # 3
Which prompting technique can protect against prompt injection attacks?
A. Adversarial prompting B. Zero-shot prompting C. Least-to-most prompting D. Chain-of-thought prompting
Answer: A
Explanation
The correct answer is A because adversarial prompting is a defensive technique used to identify and protect
against prompt injection attacks in large language models (LLMs). In adversarial prompting, developers
intentionally test the model with manipulated or malicious prompts to evaluate how it behaves under attack
and to harden the system by refining prompts, filters, and validation logic.
From AWS documentation:
"Adversarial prompting is used to evaluate and defend generative AI models against harmful or manipulative
inputs (prompt injections). By testing with adversarial examples, developers can identify vulnerabilities and
apply safeguards such as Guardrails or context filtering to prevent model misuse."
Prompt injection occurs when an attacker tries to override system or developer instructions within a prompt,
leading the model to disclose restricted information or behave undesirably. Adversarial prompting helps
uncover and mitigate these risks before deployment.
Explanation of other options:
B. Zero-shot prompting provides no examples and does not protect against injection attacks.
C. Least-to-most prompting is a reasoning technique used to break down complex problems step-by-step, not
a security measure.
D. Chain-of-thought prompting encourages detailed reasoning by the model but can actually increase
exposure to prompt injection if not properly constrained.
Referenced AWS AI/ML Documents and Study Guides:
AWS Responsible AI Practices – Prompt Injection and Safety Testing
Amazon Bedrock Developer Guide – Secure Prompt Design and Evaluation
AWS Generative AI Security Whitepaper – Adversarial Testing and Guardrails
Question # 4
A digital devices company wants to predict customer demand for memory hardware. The company does not have coding experience or knowledge of ML algorithms and needs to develop a data-driven predictive model. The company needs to perform analysis on internal data and external data.Which solution will meet these requirements?
A. Store the data in Amazon S3. Create ML models and demand forecast predictions by using Amazon
SageMaker built-in algorithms that use the data from Amazon S3. B. Import the data into Amazon SageMaker Data Wrangler. Create ML models and demand forecast
predictions by using SageMaker built-in algorithms. C. Import the data into Amazon SageMaker Data Wrangler. Build ML models and demand forecast
predictions by using an Amazon Personalize Trending-Now recipe.
Answer: D
Explanation
Amazon SageMaker Canvas is a visual, no-code machine learning interface that allows users to build machine
learning models without having any coding experience or knowledge of machine learning algorithms. It
enables users to analyze internal and external data, and make predictions using a guided interface.
Option D (Correct): "Import the data into Amazon SageMaker Canvas. Build ML models and demand
forecast predictions by selecting the values in the data from SageMaker Canvas": This is the correct answer
because SageMaker Canvas is designed for users without coding experience, providing a visual interface to
build predictive models with ease.
Option A: "Store the data in Amazon S3 and use SageMaker built-in algorithms" is incorrect because it
requires coding knowledge to interact with SageMaker's built-in algorithms.
Option B: "Import the data into Amazon SageMaker Data Wrangler" is incorrect. Data Wrangler is primarily
for data preparation and not directly focused on creating ML models without coding.
Option C: "Use Amazon Personalize Trending-Now recipe" is incorrect as Amazon Personalize is for building
recommendation systems, not for general demand forecasting.
AWS AI Practitioner References:
Amazon SageMaker Canvas Overview: AWS documentation emphasizes Canvas as a no-code solution for
building machine learning models, suitable for business analysts and users with no coding experience.
Question # 5
A company that streams media is selecting an Amazon Nova foundation model (FM) to process documents and images. The company is comparing Nova Micro and Nova Lite. The company wants to minimize costs.
A. Nova Micro uses transformer-based architectures. Nova Lite does not use transformer-based
architectures. B. Nova Micro supports only text data. Nova Lite is optimized for numerical data. C. Nova Micro supports only text. Nova Lite supports images, videos, and text. D. Nova Micro runs only on CPUs. Nova Lite runs only on GPUs.
Answer: C
Explanation
The correct answer is C, because Amazon Nova Micro is a smaller, lower-cost foundation model that is text
only, while Nova Lite is a more capable multimodal model that supports images, videos, and text. According
to AWS Bedrock documentation, the Nova model family includes variants that differ in capability and cost.
Nova Micro is optimized for lightweight text-based tasks, including summarization, question answering, and
basic reasoning. This makes it cheaper to operate and well-suited for cost-sensitive workloads. Nova Lite, on
the other hand, is a multimodal FM that can analyze documents, screenshots, photographs, charts, and videos,
making it ideal for media companies requiring cross-format understanding. AWS clarifies that both Micro and
Lite use transformer-based architectures, and run on managed infrastructure that abstracts hardware
considerations. Therefore, the main differentiator is capability—and Nova Micro being text-only is the more
cost-effective option. Nova Lite is appropriate only when image or video analysis is required.
Referenced AWS Documentation:
Amazon Bedrock – Nova Model Family Overview
AWS Generative AI Model Selection Guide
Question # 6
A company is building an AI application to summarize books of varying lengths. During testing, the application fails to summarize some books. Why does the application fail to summarize some books?
A. The temperature is set too high. B. The selected model does not support fine-tuning. C. The Top P value is too high. D. The input tokens exceed the model's context size.
Answer: D
Explanation
Foundation models have a context window (max tokens), which limits the size of the input text (prompt +
instructions).
If the input (e.g., a very long book) exceeds this limit, the model cannot process it, causing failure.
Temperature (A) and Top P (C) control randomness, not input size.
Fine-tuning (B) is irrelevant to input truncation failures.
# Reference:
AWS Documentation – Amazon Bedrock Model Parameters (context size limits
Question # 7
A company wants to identify harmful language in the comments section of social media posts by using an ML model. The company will not use labeled data to train the model. Which strategy should the company use to identify harmful language?
A. Use Amazon Rekognition moderation. B. Use Amazon Comprehend toxicity detection. C. Use Amazon SageMaker AI built-in algorithms to train the model. D. Use Amazon Polly to monitor comments.
Answer: B
Explanation
Amazon Comprehend toxicity detection is a managed NLP service that can analyze text for harmful or toxic
language using pre-trained models—no need for labeled data or custom training.
B is correct: Comprehend’s toxicity detection API is designed for this use case, works out-of-the-box, and
requires no data labeling or model training.
A (Rekognition) is for image and video content moderation.
C would require labeled data for training.
D (Polly) is for text-to-speech, not content moderation.
“Amazon Comprehend can detect toxicity in text with pre-trained models, requiring no labeled training data.”
(Reference: Amazon Comprehend Toxicity Detection, AWS AI Practitioner Official Guide)
Question # 8
A social media company wants to use a large language model (LLM) for content moderation. The company wants to evaluate the LLM outputs for bias and potential discrimination against specific groups or individuals.Which data source should the company use to evaluate the LLM outputs with the LEAST administrative effort?
A. User-generated content B. Moderation logs C. Content moderation guidelines D. Benchmark datasets
Answer: D
Explanation
Benchmark datasets are pre-validated datasets specifically designed to evaluate machine learning models for
bias, fairness, and potential discrimination. These datasets are the most efficient tool for assessing an LLM’s
performance against known standards with minimal administrative effort.
Option D (Correct): "Benchmark datasets": This is the correct answer because using standardized benchmark
datasets allows the company to evaluate model outputs for bias with minimal administrative overhead.
Option A: "User-generated content" is incorrect because it is unstructured and would require significant effort
to analyze for bias.
Option B: "Moderation logs" is incorrect because they represent historical data and do not provide a
standardized basis for evaluating bias.
Option C: "Content moderation guidelines" is incorrect because they provide qualitative criteria rather than a
quantitative basis for evaluation.
AWS AI Practitioner References:
Evaluating AI Models for Bias on AWS: AWS supports using benchmark datasets to assess model fairness
and detect potential bias efficiently.
Question # 9
A company that uses multiple ML models wants to identify changes in original model quality so that the company can resolve any issues.Which AWS service or feature meets these requirements?
A. Amazon SageMaker JumpStart B. Amazon SageMaker HyperPod C. Amazon SageMaker Data Wrangler D. Amazon SageMaker Model Monitor
Answer: D
Explanation
Amazon SageMaker Model Monitor is specifically designed to automatically detect and alert on changes in
model quality, such as data drift, prediction drift, or other anomalies in model performance once deployed.
D is correct:
"Amazon SageMaker Model Monitor continuously monitors the quality of machine learning models in
production. It automatically detects concept drift, data drift, and other quality issues, enabling teams to take
corrective actions."
(Reference: Amazon SageMaker Model Monitor Documentation, AWS Certified AI Practitioner Study Guide)
A (JumpStart) provides prebuilt solutions and models, not monitoring.
B (HyperPod) is for large-scale training, not model monitoring.
C (Data Wrangler) is for data preparation, not ongoing model quality monitoring
Question # 10
A company wants to use a pre-trained generative AI model to generate content for its marketing campaigns. The company needs to ensure that the generated content aligns with the company's brand voice and messaging requirements.Which solution meets these requirements?
A. Optimize the model's architecture and hyperparameters to improve the model's overall performance. B. Increase the model's complexity by adding more layers to the model's architecture. C. Create effective prompts that provide clear instructions and context to guide the model's generation. D. Select a large, diverse dataset to pre-train a new generative model.
Answer: C
Explanation
Creating effective prompts is the best solution to ensure that the content generated by a pre-trained generative
AI model aligns with the company's brand voice and messaging requirements.
Effective Prompt Engineering:
Involves crafting prompts that clearly outline the desired tone, style, and content guidelines for the model.
By providing explicit instructions in the prompts, the company can guide the AI to generate content that
matches the brand’s voice and messaging.
Why Option C is Correct:
Guides Model Output: Ensures the generated content adheres to specific brand guidelines by shaping the
model's response through the prompt.
Flexible and Cost-effective: Does not require retraining or modifying the model, which is more resource
efficient.
Why Other Options are Incorrect:
A. Optimize the model's architecture and hyperparameters: Improves model performance but does not
specifically address alignment with brand voice.
B. Increase model complexity: Adding more layers may not directly help with content alignment.
D. Pre-training a new model: Is a costly and time-consuming process that is unnecessary if the goal is content
alignment.
Question # 11
A company acquires International Organization for Standardization (ISO) accreditation to manage AI risks and to use AI responsibly. What does this accreditation certify?
A. All members of the company are ISO certified. B. All AI systems that the company uses are ISO certified. C. All AI application team members are ISO certified. D. The company’s development framework is ISO certified.
Answer: D
Explanation
ISO certifications apply to processes, frameworks, and systems — not individuals or every piece of software.
When a company is ISO-certified, its development framework and governance processes comply with ISO
standards for security, risk, or AI responsibility.
# Reference:
AWS Compliance Programs – ISO
Question # 12
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to know how much information can fit into one prompt.Which consideration will inform the company's decision?
A. Temperature B. Context window C. Batch size D. Model size
Answer: B
Explanation
The context window determines how much information can fit into a single prompt when using a large
language model (LLM) like those on Amazon Bedrock.
Context Window:
The context window is the maximum amount of text (measured in tokens) that a language model can process
in a single pass.
For LLM applications, the size of the context window limits how much input data, such as text for sentiment
analysis, can be fed into the model at once.
Why Option B is Correct:
Determines Prompt Size: The context window size directly informs how much information (e.g., words or
sentences) can fit in one prompt.
Model Capacity: The larger the context window, the more information the model can consider for generating
outputs.
Why Other Options are Incorrect:
A. Temperature: Controls randomness in model outputs but does not affect the prompt size.
C. Batch size: Refers to the number of training samples processed in one iteration, not the amount of
information in a prompt.
D. Model size: Refers to the number of parameters in the model, not the input size for a single prompt.
Question # 13
A company wants to label training datasets by using human feedback to fine-tune a foundation model (FM). The company does not want to develop labeling applications or manage a labeling workforce. Which AWS service or feature meets these requirements?
A. Amazon SageMaker Data Wrangler B. Amazon SageMaker Ground Truth Plus C. Amazon Transcribe D. Amazon Macie
Answer: B
Explanation
Amazon SageMaker Ground Truth Plus provides a fully managed data labeling service where AWS manages
the workforce, tools, and processes.
Data Wrangler is for data preparation and transformation.
Transcribe is for speech-to-text.
Macie is for sensitive data discovery, not labeling.
# Reference:
AWS Documentation – SageMaker Ground Truth Plus
Question # 14
A bank has fine-tuned a large language model (LLM) to expedite the loan approval process. During an external audit of the model, the company discovered that the model was approving loans at a faster pace for a specific demographic than for other demographics.How should the bank fix this issue MOST cost-effectively?
A. Include more diverse training data. Fine-tune the model again by using the new data. B. Use Retrieval Augmented Generation (RAG) with the fine-tuned model. C. Use AWS Trusted Advisor checks to eliminate bias. D. Pre-train a new LLM with more diverse training data.
Answer: A
Explanation
Comprehensive and Detailed Explanation From Exact Extract:
The best practice for mitigating bias in AI/ML models, according to AWS and responsible AI frameworks, is
to ensure that the training data is representative and diverse. If a model demonstrates bias (such as favoring a
particular demographic), the recommended, cost-effective approach is to collect additional data from
underrepresented groups and retrain (fine-tune) the model with the improved dataset.
A. Include more diverse training data. Fine-tune the model again by using the new data:
“The most effective method to reduce model bias is to curate and include diverse, representative training data,
B (RAG) is unrelated to model fairness or bias mitigation; it’s for grounding LLMs with external knowledge.
C (AWS Trusted Advisor) is for AWS resource optimization/security—not for ML model bias detection or
mitigation.
D (Pre-train a new LLM) would be extremely costly and is unnecessary; fine-tuning with better data is much
more efficient.
References:
Responsible AI on AWS
Amazon SageMaker Clarify: Detecting and Mitigating Bias
AWS Certified AI Practitioner Exam Guide
Question # 15
Which scenario describes a potential risk and limitation of prompt engineering In the context of a generative AI model?
A. Prompt engineering does not ensure that the model always produces consistent and deterministic
outputs, eliminating the need for validation. B. Prompt engineering could expose the model to vulnerabilities such as prompt injection attacks. C. Properly designed prompts reduce but do not eliminate the risk of data poisoning or model hijacking. D. Prompt engineering does not ensure that the model will consistently generate highly reliable outputs
when working with real-world data.
Answer: B
Question # 16
A customer service team is developing an application to analyze customer feedback and automatically classify the feedback into different categories. The categories include product quality, customer service, and delivery experience.Which AI concept does this scenario present?
A. Computer vision B. Natural language processing (NLP) C. Recommendation systems D. Fraud detection
Answer: B
Explanation
The scenario involves analyzing customer feedback and automatically classifying it into categories such as
product quality, customer service, and delivery experience. This task requires processing and understanding
textual data, which is a core application of natural language processing (NLP). NLP encompasses techniques
for analyzing, interpreting, and generating human language, including tasks like text classification, sentiment
analysis, and topic modeling, all of which are relevant to this use case.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Natural Language Processing (NLP) enables machines to understand and process human language. Common
NLP tasks include text classification, sentiment analysis, named entity recognition, and topic modeling.
Services like Amazon Comprehend can be used to classify text into predefined categories based on content."
(Source: AWS AI Practitioner Learning Path, Module on AI and ML Concepts)
Detailed Explanation:
Option A: Computer visionComputer vision involves processing and analyzing visual data, such as images or
videos. Since the scenario deals with textual customer feedback, computer vision is not applicable.
Option B: Natural language processing (NLP)This is the correct answer. The task of classifying customer
feedback into categories requires understanding and processing text, which is an NLP task. AWS services like
Amazon Comprehend are specifically designed for such text classification tasks.
Option C: Recommendation systemsRecommendation systems suggest items or content based on user
preferences or behavior. The scenario does not involve recommending products or services but rather
classifying feedback, so this option is incorrect.
Option D: Fraud detectionFraud detection involves identifying anomalous or fraudulent activities, typically in
financial or transactional data. The scenario focuses on text classification, not anomaly detection, making this
option irrelevant.
References:
AWS AI Practitioner Learning Path: Module on AI and ML Concepts
A company has developed an ML model for image classification. The company wants to deploy the model to production so that a web application can use the model.The company needs to implement a solution to host the model and serve predictions without managing any of the underlying infrastructure.Which solution will meet these requirements?
A. Use Amazon SageMaker Serverless Inference to deploy the model. B. Use Amazon CloudFront to deploy the model. C. Use Amazon API Gateway to host the model and serve predictions. D. Use AWS Batch to host the model and serve predictions.
Answer: A
Explanation
Amazon SageMaker Serverless Inference is the correct solution for deploying an ML model to production in a
way that allows a web application to use the model without the need to manage the underlying infrastructure.
Amazon SageMaker Serverless Inference provides a fully managed environment for deploying machine
learning models. It automatically provisions, scales, and manages the infrastructure required to host the
model, removing the need for the company to manage servers or other underlying infrastructure.
Why Option A is Correct:
No Infrastructure Management: SageMaker Serverless Inference handles the infrastructure management for
deploying and serving ML models. The company can simply provide the model and specify the required
compute capacity, and SageMaker will handle the rest.
Cost-Effectiveness: The serverless inference option is ideal for applications with intermittent or unpredictable
traffic, as the company only pays for the compute time consumed while handling requests.
Integration with Web Applications: This solution allows the model to be easily accessed by web applications
via RESTful APIs, making it an ideal choice for hosting the model and serving predictions.
Why Other Options are Incorrect:
B. Use Amazon CloudFront to deploy the model: CloudFront is a content delivery network (CDN) service for
distributing content, not for deploying ML models or serving predictions.
C. Use Amazon API Gateway to host the model and serve predictions: API Gateway is used for creating,
deploying, and managing APIs, but it does not provide the infrastructure or the required environment to host
and run ML models.
D. Use AWS Batch to host the model and serve predictions: AWS Batch is designed for running batch
computing workloads and is not optimized for real-time inference or hosting machine learning models.
Thus, A is the correct answer, as it aligns with the requirement of deploying an ML model without managing
any underlying infrastructure.
Question # 18
A financial company uses AWS to host its generative AI models. The company must generate reports to show adherence to international regulations for handling sensitive customer data
A. Amazon Macie B. AWS Artifact C. AWS Secrets Manager D. AWS Config
Answer: B
Explanation
AWS Artifact provides compliance reports and certifications (ISO, SOC, GDPR-related documentation) to
prove regulatory adherence.
Question # 19
A company uses Amazon Bedrock to implement a generative AI assistant on a website. The AI assistant helps customers with product recommendations and purchasing decisions. The company wants to measure the direct impact of the AI assistant on sales performance.
A. The conversion rate of customers who purchase products after AI assistant interactions B. The number of customer interactions with the AI assistant C. Sentiment analysis scores from customer feedback after AI assistant interactions D. Natural language understanding accuracy rates
Answer: A
Explanation
The most direct business KPI for sales performance is conversion rate (percentage of users who purchase after
AI assistant interaction).
Number of interactions (B) shows engagement, not sales impact.
Sentiment analysis (C) shows customer satisfaction but not revenue impact.
NLU accuracy (D) is a technical metric, not a business outcome.
# Reference:
AWS Generative AI Use Cases – Measuring Business Value
Question # 20
A company is using few-shot prompting on a base model that is hosted on Amazon Bedrock. The model currently uses 10 examples in the prompt. The model is invoked once daily and is performing well. The company wants to lower the monthly cost.Which solution will meet these requirements?
A. Customize the model by using fine-tuning. B. Decrease the number of tokens in the prompt. C. Increase the number of tokens in the prompt. D. Use Provisioned Throughput.
Answer: B
Explanation
Decreasing the number of tokens in the prompt reduces the cost associated with using an LLM model on
Amazon Bedrock, as costs are often based on the number of tokens processed by the model.
Token Reduction Strategy:
By decreasing the number of tokens (words or characters) in each prompt, the company reduces the
computational load and, therefore, the cost associated with invoking the model.
Since the model is performing well with few-shot prompting, reducing token usage without sacrificing
performance can lower monthly costs.
Why Option B is Correct:
Cost Efficiency: Directly reduces the number of tokens processed, lowering costs without requiring additional
adjustments.
Maintaining Performance: If the model is already performing well, a reduction in tokens should not
significantly impact its performance.
Why Other Options are Incorrect:
A. Fine-tuning: Can be costly and time-consuming and is not needed if the current model is already
performing well.
C. Increase the number of tokens: Would increase costs, not lower them.
D. Use Provisioned Throughput: Is unrelated to token costs and applies more to read/write capacity in
databases.
Question # 21
A company is developing a mobile ML app that uses a phone's camera to diagnose and treat insect bites. The company wants to train an image classification model by using a diverse dataset of insect bite photos from different genders, ethnicities, and geographic locations around the world.Which principle of responsible Al does the company demonstrate in this scenario?
A. Fairness B. Explainability C. Governance D. Transparency
Answer: A
Explanation
The company is training an image classification model for diagnosing insect bites using a diverse dataset that
includes photos from different genders, ethnicities, and geographic locations. This approach demonstrates the
principle of fairness in responsible AI, as it aims to reduce bias and ensure the model performs equitably
across diverse populations.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Fairness in AI involves ensuring that models do not exhibit bias against certain groups and perform equitably
across diverse populations. This can be achieved by training models on diverse datasets that represent various
demographics, such as gender, ethnicity, and geographic location."
(Source: AWS AI Practitioner Learning Path, Module on Responsible AI)
Detailed Explanation:
Option A: FairnessThis is the correct answer. By using a diverse dataset, the company ensures the model is
less likely to be biased against specific groups, promoting fairness in its predictions and treatments for insect
bites.
Option B: ExplainabilityExplainability refers to making the model’s decisions understandable to users, such
as byproviding insights into how predictions are made. The scenario focuses on dataset diversity, not
explainability.
Option C: GovernanceGovernance involves establishing policies and processes to manage AI systems, such as
compliance and oversight. The scenario does not describe governance mechanisms.
Option D: TransparencyTransparency involves disclosing how a model works, its limitations, and its data
sources. While transparency is important, the scenario specifically highlights the diversity of the dataset,
which aligns more directly with fairness.
References:
AWS AI Practitioner Learning Path: Module on Responsible AI
A retail store wants to predict the demand for a specific product for the next few weeks by using the Amazon SageMaker DeepAR forecasting algorithm.Which type of data will meet this requirement?
A. Text data B. Image data C. Time series data D. Binary data
Answer: C
Explanation
Amazon SageMaker's DeepAR is a supervised learning algorithm designed for forecasting scalar (one
dimensional) time series data. Time series data consists of sequences of data points indexed in time order,
typically with consistent intervals between them. In the context of a retail store aiming to predict product
demand, relevant time series data might include historical sales figures, inventory levels, or related metrics
recorded over regular time intervals (e.g., daily or weekly). By training the DeepAR model on this historical
time series data, the store can generate forecasts for future product demand. This capability is particularly
useful for inventory management, staffing, and supply chain optimization. Other data types, such as text,
image, or binary data, are not suitable for time series forecasting tasks and would not be appropriate inputs for
the DeepAR algorithm.
Reference: Amazon SageMaker DeepAR Algorithm
Question # 23
A company wants to control employee access to publicly available foundation models (FMs). Which solution meets these requirements?
A. Analyze cost and usage reports in AWS Cost Explorer. B. Download AWS security and compliance documents from AWS Artifact. C. Configure Amazon SageMaker JumpStart to restrict discoverable FMs. D. Build a hybrid search solution by using Amazon OpenSearch Service.
Answer: C
Explanation
The correct answer is C because Amazon SageMaker JumpStart provides administrative controls that allow
organizations to manage and restrict access to foundation models within the AWS environment.
According to the official AWS documentation:
"Amazon SageMaker JumpStart provides model access management capabilities that enable administrators to
control which foundation models are visible and usable by end users. Using AWS Identity and Access
Management (IAM) policies, you can restrict access to specific models or completely disable model discovery
in JumpStart."
This allows companies to enforce governance over which FMs their users can see and interact with, satisfying
the requirement to control employee access to publicly available foundation models.
Explanation of other options:
A. AWS Cost Explorer is used to analyze billing and usage data but does not control access to services or
models. It is helpful for budgeting and visibility, not access control.
B. AWS Artifact provides access to compliance reports and certifications, not tools for controlling user access
to ML models.
D. Amazon OpenSearch Service is used for search and analytics on structured and unstructured data. It does
not provide access control mechanisms for foundation models.
Referenced AWS AI/ML Documents and Study Guides:
Amazon SageMaker JumpStart Documentation – Model Access Management
AWS IAM Documentation – Restricting Access to SageMaker Resources
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 the harmonic mean of precision and recall, making it a balanced metric for evaluating model
performance when there is an imbalance between false positives and false negatives. Speed, cost, and energy
efficiency are unrelated to the F1 score. References: AWS Foundation Models Guide.
Question # 25
An accounting firm wants to implement a large language model (LLM) to automate document processing. The firm must proceed responsibly to avoid potential harms.What should the firm do when developing and deploying the LLM? (Select TWO.)
A. Include fairness metrics for model evaluation. B. Adjust the temperature parameter of the model. C. Modify the training data to mitigate bias. D. Avoid overfitting on the training data. E. Apply prompt engineering techniques.
Answer: A C
Explanation
To implement a large language model (LLM) responsibly, the firm should focus on fairness and mitigating
bias, which are critical for ethical AI deployment.
A. Include Fairness Metrics for Model Evaluation:
Fairness metrics help ensure that the model's predictions are unbiased and do not unfairly discriminate against
any group.
These metrics can measure disparities in model outcomes across different demographic groups, ensuring
responsible AI practices.
C. Modify the Training Data to Mitigate Bias:
Adjusting training data to be more representative and balanced can help reduce bias in the model's predictions.
Mitigating bias at the data level ensures that the model learns from a diverse and fair dataset, reducing
potential harms in deployment.
Why Other Options are Incorrect:
B. Adjust the temperature parameter of the model: Controls randomness in outputs but does not directly
address fairness or bias.
D. Avoid overfitting on the training data: Important for model generalization but not directly related to
responsible AI practices regarding fairness and bias.
E. Apply prompt engineering techniques: Useful for improving model outputs but not specifically for
mitigating bias or ensuring fairness.
Question # 26
A company wants to generate synthetic data responses for multiple prompts from a large volume of data. The company wants to use an API method to generate the responses. The company does not need to generate the responses immediately.
A. Input the prompts into the model. Generate responses by using real-time inference. B. Use Amazon Bedrock batch inference. Generate responses asynchronously. C. Use Amazon Bedrock agents. Build an agent system to process the prompts recursively. D. Use AWS Lambda functions to automate the task. Submit one prompt after another and store each
response.
Answer: B
Explanation
The correct answer is B – Use Amazon Bedrock batch inference, which allows asynchronous generation of
large-scale model outputs through APIs without requiring low-latency performance. According to AWS
Bedrock documentation, batch inference is ideal for high-volume workloads that can tolerate delay, such as
bulk content generation or summarization jobs. Unlike real-time inference, it processes requests in bulk,
reducing cost and operational load. AWS handles the queuing, processing, and scaling automatically. Bedrock
Agents (option C) are for workflow orchestration, not large-scale generation. AWS Lambda (option D) can
automate tasks but is not optimized for high-volume LLM calls. Batch inference provides cost efficiency,
scalability, and simplicity for delayed, asynchronous generation needs.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Developer Guide – Batch Inference
AWS ML Specialty Study Guide – Scalable Inference Options
Question # 27
A company is using supervised learning to train an AI model on a small labeled dataset that is specific to a target task. Which step of the foundation model (FM) lifecycle does this describe?
A. Fine-tuning B. Data selection C. Pre-training D. Evaluation
Answer: A
Explanation
Fine-tuning involves training an already pre-trained FM on a smaller, labeled dataset for task specialization.
Data selection is about curating training data.
Pre-training is the initial training phase on massive datasets.
Evaluation happens after training, not during.
# Reference:
AWS Documentation – Fine-tuning in Amazon Bedrock
Question # 28
Which technique involves training AI models on labeled datasets to adapt the models to specific industry terminology and requirements?
A. Data augmentation B. Fine-tuning C. Model quantization D. Continuous pre-training
Answer: B
Explanation
Fine-tuning involves training a pre-trained AI model on a labeled dataset specific to a particular task or
domain, adapting it to industry terminology and requirements. This process adjusts the model’s parameters to
better fit the target use case, such as understanding specialized vocabulary or meeting domain-specific needs.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Fine-tuning allows you to adapt a pre-trained foundation model to your specific use case by training it on a
labeled dataset. This technique is commonly used to customize models forindustry-specific terminology,
improving their accuracy for specialized tasks."
(Source: AWS Bedrock User Guide, Model Customization)
Detailed Explanation:
Option A: Data augmentationData augmentation involves generating synthetic data to expand a training
dataset, typically for tasks like image or text generation. It does not specifically adapt models to industry
terminology or requirements.
Option B: Fine-tuningThis is the correct answer. Fine-tuning trains a pre-trained model on a labeled dataset
tailored to the target domain, enabling it to learn industry-specific terminology and requirements, as described
in the question.
Option C: Model quantizationModel quantization reduces the precision of a model’s weights to optimize it for
deployment (e.g., on edge devices). It does not involve training on labeled datasets or adapting to industry
terminology.
Option D: Continuous pre-trainingContinuous pre-training extends the initial training of a model on a large,
general dataset. While it can improve general performance, it is not specifically tailored to industry
requirements using labeled datasets, unlike fine-tuning.
A company needs to choose a model from Amazon Bedrock to use internally. The company must identify a model that generates responses in a style that the company's employees prefer.What should the company do to meet these requirements?
A. Evaluate the models by using built-in prompt datasets. B. Evaluate the models by using a human workforce and custom prompt datasets. C. Use public model leaderboards to identify the model. D. Use the model InvocationLatency runtime metrics in Amazon CloudWatch when trying models.
Answer: B
Question # 30
A company wants to implement a large language model (LLM)-based chatbot to provide customer service agents with real-time contextual responses to customers' inquiries. The company will use the company's policies as the knowledge base.
A. Retrain the LLM on the company policy data. B. Fine-tune the LLM on the company policy data. C. Implement Retrieval Augmented Generation (RAG) for in-context responses. D. Use pre-training and data augmentation on the company policy data.
Answer: C
Explanation
Retraining or pre-training is costly and unnecessary for just using company policies.
Fine-tuning adapts models but is inefficient for frequently changing company documents.
Retrieval-Augmented Generation (RAG) is the best approach — it retrieves relevant policy documents from a
knowledge base and feeds them into the model context in real time, ensuring accurate and up-to-date
responses.
# Reference:
AWS Documentation – RAG with Amazon Bedrock
Question # 31
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 # 32
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 # 33
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 # 34
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 # 35
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 # 36
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 # 37
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 # 38
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
