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AWS Certified AI Practitioner AIF-C01 (AIF-C01) — Questions 601–619

619 questions total · 9pages · All types, answers revealed

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601
Multi-Selectmedium

A developer is building an agent using Amazon Bedrock Agents to handle customer support inquiries. The agent needs to look up order status from a database and escalate complex issues to a human. Which THREE components are essential for this agent?

Select 3 answers
A.A Lambda function that connects to the database and returns results
B.A fine-tuned foundation model specific to the support domain
C.A custom model trained on historical support tickets
D.A knowledge base containing troubleshooting documentation
E.An action group with an OpenAPI schema for the database query
AnswersA, D, E

Lambda provides the execution logic for the action group.

Why this answer

An action group to query the database, a Lambda function to execute the query, and a knowledge base are essential. IAM roles are needed but not listed here. The other options are not required: fine-tuning is not needed, and custom models are optional.

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602
MCQhard

A healthcare startup uses Amazon SageMaker to train a model predicting patient readmission. They need to ensure the model's predictions do not discriminate based on protected attributes like age or race. Which SageMaker feature allows them to monitor and mitigate bias during training?

A.SageMaker Model Monitor
B.SageMaker Autopilot
C.SageMaker Debugger
D.SageMaker Clarify
AnswerD

Provides bias detection and explainability.

Why this answer

SageMaker Clarify is the correct choice because it is specifically designed to detect and mitigate bias in machine learning models. It provides built-in capabilities to analyze training data and model predictions for bias against protected attributes such as age or race, and can generate bias reports and suggest mitigation strategies during training.

Exam trap

The trap here is that candidates often confuse SageMaker Clarify with SageMaker Model Monitor, assuming that monitoring for data drift also covers bias detection, but Clarify is the dedicated service for bias detection and mitigation.

How to eliminate wrong answers

Option A is wrong because SageMaker Model Monitor is used to detect data drift and model quality degradation in production, not to analyze or mitigate bias during training. Option B is wrong because SageMaker Autopilot automates the process of building, training, and tuning models, but it does not include built-in bias detection or mitigation features. Option C is wrong because SageMaker Debugger is designed to monitor training jobs for issues like vanishing gradients or overfitting by capturing tensors and metrics, not for bias detection or mitigation.

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603
Multi-Selecteasy

A company is using Amazon Bedrock to generate content for a marketing application. The company wants to ensure that the model does not generate content that violates the company's brand guidelines, which prohibit certain keywords and tones. Which TWO features should the company use to enforce these guidelines? (Choose two.)

Select 2 answers
A.Enable Amazon CloudWatch Logs to capture model output and manually review.
B.Create a prompt template that instructs the model to adhere to brand guidelines and avoid prohibited keywords.
C.Configure Amazon Bedrock Guardrails with custom deny topics and content filters.
D.Use AWS IAM policies to restrict the model's output to only approved words.
E.Encrypt the model responses using AWS KMS to prevent unauthorized viewing.
AnswersB, C

Prompt engineering is an effective way to guide model behavior without additional infrastructure.

Why this answer

Prompt engineering allows the company to embed brand guidelines directly into the instruction given to the model, effectively steering the output away from prohibited keywords and tones. Option C is correct because Amazon Bedrock Guardrails provides a managed, policy-based mechanism to define custom deny topics and content filters that can block or mask unwanted content at inference time, enforcing brand guidelines without manual intervention.

Exam trap

The trap here is that candidates often confuse IAM policies with content moderation, mistakenly believing that IAM can restrict model output vocabulary, when in fact IAM only governs API-level permissions and has no awareness of the semantic content of model responses.

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604
MCQmedium

A healthcare organization uses an ML model to predict patient readmission risk. The model performs well overall but has significantly higher false negative rates for elderly patients. The team needs to mitigate this bias. Which step should they take FIRST?

A.Retrain the model with fairness constraints using SageMaker Clarify
B.Remove the age feature from the model
C.Collect more data for elderly patients to reduce representation bias
D.Use SageMaker Clarify to compute bias metrics and identify the extent of the disparity
AnswerD

Measuring the bias is the prerequisite for any mitigation; Clarify provides metrics like difference in false negative rates.

Why this answer

The first step is to identify and understand the bias through measurement. SageMaker Clarify can compute fairness metrics like false negative rate difference to quantify the disparity. Retraining with fairness constraints or reweighting are mitigation steps that come after measurement.

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605
MCQmedium

A machine learning team uses Amazon SageMaker to train and deploy models. They need to ensure that only approved base models from the AWS Marketplace are used. Which feature should they use to enforce this policy?

A.SageMaker Role Manager
B.IAM policies that allow only specific model package ARNs from the AWS Marketplace
C.AWS Config rules to detect non-compliant models
D.SageMaker Model Registry
AnswerB

By using resource-level permissions in IAM, you can restrict users to only use certain model package ARNs.

Why this answer

SageMaker Role Manager helps administrators create least-privilege IAM roles, but it does not restrict which models can be used. Model access in SageMaker is controlled via IAM policies on the CreateTrainingJob and CreateModel APIs, combined with resource-level permissions.

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606
MCQmedium

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

A.Train a custom model from scratch on the policy documents each month
B.Use a larger foundation model with a longer context window and paste all documents into each prompt
C.Fine-tune a base LLM on the policy documents monthly
D.Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store
AnswerD

RAG retrieves relevant document chunks at query time, ensuring the chatbot always answers from the latest uploaded documents without any model retraining.

Why this answer

RAG (Retrieval-Augmented Generation) allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining. The other options either require expensive retraining for each update or lack document grounding.

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607
Multi-Selectmedium

A company is deploying an AI-based customer service chatbot using Amazon Lex and AWS Lambda. The company must ensure compliance with GDPR by allowing users to request deletion of their personal data collected during conversations. Which TWO actions should the company take? (Choose two.)

Select 2 answers
A.Use the Amazon Lex DeleteTranscript API to delete user conversation transcripts.
B.Apply an IAM policy that denies access to the conversation logs for the user who requested deletion.
C.Delete the corresponding log streams in Amazon CloudWatch Logs that contain the conversation data.
D.Disable encryption of conversation logs to allow manual deletion.
E.Modify the Lex bot's conversation log settings to stop collecting personal data.
AnswersA, C

DeleteTranscript directly removes stored transcripts for a specific user.

Why this answer

Amazon Lex provides the DeleteTranscript API, which allows the deletion of user conversation transcripts stored by Lex. This directly supports GDPR compliance by enabling the removal of personal data from conversation logs. The API targets the specific transcripts associated with a user, ensuring that their data is permanently deleted.

Exam trap

The trap here is that candidates may think that restricting access (IAM policy) or stopping data collection (modifying settings) is sufficient for GDPR deletion, but GDPR requires actual erasure of existing data, not just access control or future prevention.

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608
MCQmedium

Refer to the exhibit. A company has the S3 bucket policy shown above. The company uses Amazon Rekognition to analyze images in the 'my-images' bucket. An IAM role used by a Lambda function calls Rekognition. Why might Rekognition be unable to read images from the bucket?

A.The Lambda function does not have an IAM role that allows Rekognition to call S3
B.The bucket policy does not grant Rekognition access to the bucket
C.The bucket policy does not include PutObject permission
D.The condition 'aws:SourceAccount' does not match the account of the Rekognition service
AnswerD

The condition ensures that the request originates from the specified account; if not, access is denied.

Why this answer

The bucket policy includes a condition 'aws:SourceAccount' that restricts access to requests originating from a specific AWS account. If the Rekognition service is being called from a different account (e.g., the Lambda function's IAM role is in a different account than the one specified in the condition), Rekognition will be denied read access to the S3 bucket. This condition is often misconfigured, causing cross-account access failures even when the principal is allowed.

Exam trap

The AIF-C01 exam often tests the nuance that a bucket policy can explicitly grant access to a service principal (like Rekognition) but still block requests due to a condition key mismatch, leading candidates to incorrectly assume the policy is missing the permission entirely.

How to eliminate wrong answers

Option A is wrong because the Lambda function's IAM role is used to call Rekognition, not to directly access S3; Rekognition itself needs permission to read from the bucket, which is governed by the bucket policy. Option B is wrong because the bucket policy does grant Rekognition access (the exhibit shows an Allow effect for the Rekognition service principal), but the condition is blocking it. Option C is wrong because PutObject permission is irrelevant to reading images; Rekognition only needs GetObject (read) permission to analyze images, not write access.

Full explanation →
609
Multi-Selectmedium

A developer is building a chatbot that must refuse to answer questions about internal financial data. They also need to filter out any offensive language from user inputs. Which TWO Bedrock features should they use? (Choose TWO.)

Select 2 answers
A.Bedrock Agent
B.Bedrock Guardrails – topic denial
C.Bedrock Guardrails – content filtering
D.Prompt engineering with negative instructions
E.Bedrock Knowledge Base
AnswersB, C

Denies responses about internal financial data.

Why this answer

Guardrails provide both topic denial and content filtering. Topic denial blocks specific subjects, and content filtering handles offensive language.

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610
Multi-Selectmedium

A company wants to build a multi-language customer support chatbot using Amazon Bedrock. The chatbot should support English, Spanish, and French. The team needs to translate user queries into English before processing and then translate responses back. Which TWO approaches could achieve this? (Choose TWO)

Select 2 answers
A.Use a separate model fine-tuned for translation for each language pair
B.Use a Bedrock Agent with an action group that calls a translation Lambda function
C.Instruct the foundation model via prompt engineering to translate the query and response
D.Configure Bedrock Guardrails to translate automatically
E.Store translated documents in a Bedrock Knowledge Base
AnswersB, C

An agent can invoke a Lambda that performs translation using Amazon Translate or a model.

Why this answer

Bedrock Agents can orchestrate translation steps; prompt engineering can instruct the model to translate. Knowledge Bases and Guardrails are not translation solutions. Human agents are not automated.

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611
MCQmedium

A developer is using Bedrock Agents to create a travel planning assistant. The agent needs to call a hotel booking API and a flight API. What is the correct way to define these external API calls in Bedrock Agents?

A.Create action groups with the API schemas and Lambda functions
B.Hardcode the API endpoints in the agent's prompt
C.Configure the APIs as guardrails content filters
D.Define the APIs as knowledge base data sources
AnswerA

Action groups allow the agent to invoke external APIs by defining the API schema and a Lambda function to execute the call.

Why this answer

Action groups in Bedrock Agents define the tools (APIs) that the agent can call, including the OpenAPI schema and Lambda function integration. Knowledge Bases are for RAG, not API calls. Guardrails are for content filtering.

The playground is for testing prompts.

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612
MCQmedium

A company is building a chatbot using Amazon Bedrock to answer customer questions about their product catalog. The chatbot should only use information from the company's internal knowledge base and should not generate answers based on the model's pre-training data. Which feature should be enabled?

A.Use prompt engineering to instruct the model to only use the knowledge base
B.Configure a knowledge base with Retrieval Augmented Generation (RAG)
C.Enable model invocation logging to review responses
D.Fine-tune the model on the product catalog data
AnswerB

RAG grounds responses in the provided knowledge base, avoiding use of pre-training data.

Why this answer

Configuring a knowledge base with Retrieval Augmented Generation (RAG) allows the chatbot to retrieve relevant documents from the company's internal knowledge base and use them as context for generating answers. This ensures the model's responses are grounded solely in the provided data, preventing reliance on its pre-training knowledge.

Exam trap

The trap here is that candidates often confuse fine-tuning with RAG, assuming fine-tuning alone can restrict the model to a specific knowledge domain, when in fact fine-tuning does not prevent the model from using its pre-training data and can still produce off-topic responses.

How to eliminate wrong answers

Option A is wrong because prompt engineering alone cannot reliably prevent the model from using its pre-training data; it only provides instructions that the model may still override with its internal knowledge. Option C is wrong because model invocation logging only records responses for auditing and debugging, it does not constrain the model's source of information. Option D is wrong because fine-tuning adapts the model to the product catalog but does not guarantee that the model will ignore its pre-training data; it can still generate answers from its original training corpus.

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613
MCQmedium

An AI practitioner is training a resume screening model and discovers that the model has a significantly lower recall for female candidates compared to male candidates. Which type of bias is MOST likely present?

A.Representation bias
B.Historical bias
C.Aggregation bias
D.Measurement bias
AnswerB

Historical bias stems from past prejudices in the data; the model learns that female candidates are less likely to be hired, resulting in lower recall.

Why this answer

Historical bias occurs when training data reflects past societal biases, such as fewer women in certain job roles. This leads to the model learning those biases and producing unfair outcomes.

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614
Multi-Selectmedium

A company wants to use AWS Lake Formation to govern access to data used for AI training. They need to ensure that only approved columns of sensitive tables are visible to data scientists. Which THREE steps should they implement? (Choose THREE)

Select 3 answers
A.Use AWS Glue crawlers to catalog the data and populate the Data Catalog
B.Create a SageMaker notebook instance and attach an IAM role
C.Define column-level permissions in Lake Formation to grant access to specific columns for the data scientist role
D.Enable S3 versioning on the training data bucket
E.Register the S3 bucket containing the training data with Lake Formation
AnswersA, C, E

A Glue Data Catalog is required for Lake Formation to manage permissions on tables and columns.

Why this answer

Lake Formation allows you to register S3 locations, create resource links, and grant permissions at column level. The other options are not specific to Lake Formation column-level access control.

Full explanation →
615
MCQmedium

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

A.Train a custom model from scratch on the policy documents each month
B.Fine-tune a base LLM on the policy documents monthly
C.Use a larger foundation model with a longer context window and paste all documents into each prompt
D.Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store
AnswerD

RAG retrieves relevant document chunks at query time, ensuring the chatbot always answers from the latest uploaded documents without any model retraining.

Why this answer

RAG (Retrieval-Augmented Generation) allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining. The other options either require expensive retraining for each update or lack document grounding.

Full explanation →
616
MCQhard

An enterprise deploys a foundation model on Amazon Bedrock with a knowledge base. Users report that the model is returning outdated information. What is the most likely cause?

A.The model was fine-tuned
B.The model is not the latest version
C.The knowledge base data source is not refreshed
D.The inference parameters are incorrect
AnswerC

If the underlying data source hasn't been updated, the knowledge base contains stale data.

Why this answer

When a knowledge base is attached to a foundation model on Amazon Bedrock, the model retrieves information from the data source to augment its responses. If the data source is not refreshed, the model will return outdated information even if the model itself is current. Option C directly addresses this by identifying the stale data source as the root cause.

Exam trap

The trap here is that candidates may confuse model versioning (Option B) with data freshness, but the question specifically ties the symptom to the knowledge base, making the refresh cycle the critical factor.

How to eliminate wrong answers

Option A is wrong because fine-tuning adjusts the model's weights on a specific dataset, which does not inherently cause outdated information; in fact, fine-tuning could update the model with newer data. Option B is wrong because using an older model version might affect performance or capabilities, but the question specifically states the model is returning outdated information, which points to the knowledge base content, not the model version. Option D is wrong because inference parameters (e.g., temperature, top_p) control randomness and creativity of responses, not the freshness or accuracy of the information retrieved from the knowledge base.

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617
Multi-Selectmedium

Which THREE statements about Amazon SageMaker Ground Truth are correct? (Choose three.)

Select 3 answers
A.It can only be used for text data.
B.It provides built-in workflows for image classification and object detection.
C.It supports automated data labeling using active learning.
D.It integrates with Amazon SageMaker to use the labeled data for training.
E.It can only use a public workforce from Amazon Mechanical Turk.
AnswersB, C, D

Ground Truth supports these tasks.

Why this answer

Amazon SageMaker Ground Truth provides built-in workflows for common tasks like image classification and object detection, which simplifies the setup of labeling jobs. These pre-built templates handle the UI and data formatting, allowing users to focus on the labeling instructions rather than building the labeling interface from scratch.

Exam trap

AWS often tests the misconception that Ground Truth is limited to text data or only supports public workforces, while in reality it handles multiple data modalities and offers flexible workforce options including private and vendor-managed.

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618
MCQhard

A developer is using the Amazon Bedrock InvokeModel API with a model that has a context window of 8,000 tokens. The developer sends a prompt that is 7,500 tokens long and expects a response of about 1,000 tokens. The API call fails with an error indicating the input exceeds the model's context window. Why did this happen?

A.The API automatically reserves 2,000 tokens for output, reducing available input capacity
B.The model requires a minimum of 512 tokens for internal processing
C.The context window includes both input and output tokens, so the total of 7,500 + 1,000 = 8,500 exceeds the 8,000 limit
D.The model's context window counts only input tokens; output tokens are separate
AnswerC

The developer must reduce input length or request a shorter output to fit within the limit.

Why this answer

The context window of a foundation model includes both the input prompt and the generated output tokens. The developer's prompt of 7,500 tokens plus the expected 1,000-token response totals 8,500 tokens, which exceeds the model's 8,000-token limit. The InvokeModel API enforces this combined limit, so even though the prompt alone is under the limit, the total token count causes the error.

Exam trap

The trap here is that candidates often assume the context window applies only to the input prompt, ignoring that the output response also consumes tokens from the same window, leading them to incorrectly select option D.

How to eliminate wrong answers

Option A is wrong because the API does not automatically reserve a fixed number of tokens for output; the context window is shared dynamically between input and output, and the error occurs only when the total exceeds the limit. Option B is wrong because there is no minimum token requirement for internal processing; the model can handle prompts of any length up to the context window limit. Option D is wrong because the context window counts both input and output tokens together, not input tokens separately; output tokens consume part of the same window.

Full explanation →
619
Multi-Selectmedium

A company is using Amazon Bedrock Knowledge Bases with a RAG pipeline. They want to improve the relevance of retrieved chunks for user queries. Which TWO configuration changes are likely to help?

Select 2 answers
A.Use a model with higher-dimensional embeddings
B.Disable chunking and store entire documents as single vectors
C.Reduce the chunk size
D.Increase the model's max context length
E.Increase the chunk size
AnswersA, C

Higher dimensions can capture finer semantic differences.

Why this answer

Reducing chunk size can improve precision, and using embeddings with higher dimensions may capture more semantic nuance. Increasing chunk size typically reduces precision, and disabling chunking would break retrieval. Increasing max context length does not affect retrieval.

Full explanation →
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