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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
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