AIF-C01 Multiple Select — 40 Questions

Question 1

A data science team wants to document and share their model's intended use, performance, and limitations with stakeholders. They also need to track the model's version and deployment history. Which TWO AWS services or features should they use?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Amazon SageMaker Model Cards. SageMaker Model Cards provide standardized documentation for transparency. SageMaker Model Registry tracks model versions, deployment stages, and metadata. SageMaker Pipelines is for ML workflows, not documentation or version tracking. SageMaker Studio is an IDE. SageMaker Clarify is for bias and explainability.

Answer

Amazon SageMaker Pipelines

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Amazon SageMaker Clarify

Answer

Amazon SageMaker Studio

Question 2

A company wants to use AWS services to process natural language text. Which TWO AWS services provide natural language processing (NLP) capabilities? (Select TWO.)

Accepted Answer

Amazon Comprehend. Amazon Comprehend (C) is correct because it is AWS's fully managed NLP service that uses machine learning to extract insights from text, including sentiment analysis, entity recognition, key phrase extraction, language detection, and topic modeling. Amazon Lex (E) is correct because it provides NLP capabilities for building conversational interfaces (chatbots and voice bots) using automatic speech recognition and natural language understanding to interpret user intent and slot values. Amazon Translate (A) is not marked correct because it performs language translation rather than general NLP analysis, even though it is a text-processing service. Amazon Rekognition (B) is not marked correct because it is a computer vision service for image and video analysis, not natural language text. Amazon Polly (D) is not marked correct because it is a text-to-speech service that converts written text into lifelike speech, which is speech synthesis rather than NLP understanding.

Answer

Amazon Translate

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Amazon Rekognition

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Amazon Polly

Question 3

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.)

Accepted Answer

Create a prompt template that instructs the model to adhere to brand guidelines and avoid prohibited keywords.. 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.

Answer

Enable Amazon CloudWatch Logs to capture model output and manually review.

Answer

Use AWS IAM policies to restrict the model's output to only approved words.

Answer

Encrypt the model responses using AWS KMS to prevent unauthorized viewing.

Question 4

A company uses Amazon Macie to discover sensitive data in an S3 bucket containing training datasets. The bucket policy currently prohibits access from external accounts. Which TWO steps are necessary to allow a cross-account SageMaker training job to access this bucket while maintaining security?

Accepted Answer

Add a bucket policy that grants the SageMaker execution role from the other account s3:GetObject and s3:ListBucket permissions. Option C is correct because the S3 bucket policy is the resource-based policy that must explicitly grant the cross-account SageMaker execution role access; since the bucket currently prohibits external accounts, you must add a statement allowing that role principal s3:GetObject and s3:ListBucket on the bucket and its objects. Option D is correct because the SageMaker execution role in the other account also needs an identity-based IAM policy permitting s3:GetObject and s3:ListBucket on the source bucket, since cross-account access requires both the identity policy and the resource policy to allow the action. Option A is wrong because Macie is a data-discovery and classification service and cannot grant access to a SageMaker execution role. Option B is wrong because an S3 VPC endpoint only affects private network routing for traffic within a VPC and does not by itself authorize cross-account access. Option E is wrong because removing the bucket policy's external-access prohibition would broadly open the bucket rather than securely scoping access to the SageMaker execution role.

Answer

Configure Macie to automatically grant access to the SageMaker execution role

Answer

Create a VPC endpoint for S3 and associate it with the SageMaker VPC

Answer

Remove the bucket policy that prohibits external access

Question 5

A company is using Amazon Bedrock to generate personalized marketing emails. They notice that the model sometimes produces outputs that are off-brand or contain factual errors about their products. Which TWO prompt engineering techniques would be MOST effective to address these issues? (Choose TWO.)

Accepted Answer

Include few-shot examples in the prompt that demonstrate correct brand tone and factual accuracy. Option A is correct because few-shot examples embed concrete demonstrations of the desired brand tone and accurate product facts directly in the prompt, letting the model imitate those patterns and reducing off-brand or incorrect outputs. Option C is correct because a system prompt sets persistent instructions and context—such as brand guidelines and authoritative product details—that condition every response, which directly constrains tone and factual grounding. Chain-of-thought prompting (B) mainly improves multi-step reasoning and does not supply brand or factual grounding, so it does not address these issues. Zero-shot prompting (D) removes the very examples that would steer tone and accuracy, making it counterproductive. Lowering temperature to 0.0 (E) only reduces sampling randomness; it cannot correct a model that lacks brand or product knowledge, so it does not fix off-brand or factually wrong content.

Answer

Apply chain-of-thought prompting to encourage reasoning

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Use zero-shot prompting without any examples

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Decrease the temperature to 0.0 to eliminate randomness

Question 6

Which TWO practices help ensure responsible AI when deploying generative AI applications? (Select TWO.)

Accepted Answer

Implement guardrails to filter harmful or inappropriate content. Option D is correct because implementing guardrails—such as content filters, prompt shields, and safety classifiers—directly mitigates the risk of generative AI producing harmful, offensive, or inappropriate content, which is a core requirement of responsible AI deployment. Option E is correct because continuously monitoring model outputs for bias and drift ensures that the system remains fair and accurate as data distributions and user behavior change over time, enabling timely remediation. Option A is incorrect because removing content filters increases the risk of harmful outputs and violates responsible AI principles rather than ensuring them. Option B is incorrect because increasing model size at the cost of interpretability reduces transparency and explainability, which are key responsible AI goals. Option C is incorrect because using only synthetic data does not by itself guarantee privacy or fairness and can introduce its own biases and quality issues.

Answer

Deploy the model without any content filters to maximize creativity

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Increase model size to improve accuracy at the expense of interpretability

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Use only synthetic data for training to avoid privacy issues

Question 7

A company is using Amazon SageMaker to manage the lifecycle of their machine learning models. They need to implement a governance framework that includes model versioning, monitoring for drift, and decommissioning of outdated models. Which THREE AWS services or features should they use together to meet these requirements? (Select THREE.)

Accepted Answer

SageMaker Pipelines. SageMaker Model Registry (C) is the correct service for model versioning and governance, as it maintains a catalog of model versions with metadata, approval statuses, and lineage, enabling tracking and controlled promotion of models through their lifecycle. SageMaker Model Monitor (E) is correct because it continuously monitors deployed endpoints for data drift, model quality drift, bias drift, and feature attribution drift, alerting when the model deviates from baseline behavior. SageMaker Pipelines (B) is correct because it provides CI/CD orchestration to automate the ML workflow, including training, evaluation, registration to the Model Registry, and deployment, which supports governance and decommissioning through repeatable, versioned pipeline executions. AWS CloudTrail (A) only records API activity for auditing and does not provide model versioning, drift monitoring, or lifecycle decommissioning, so it does not meet the requirements. SageMaker Role Manager (D) merely helps create and manage IAM roles and permissions for SageMaker personas; it does not provide versioning, drift detection, or model decommissioning capabilities.

Answer

AWS CloudTrail

Answer

SageMaker Role Manager

Question 8

A company wants to build a system that automatically routes support tickets to the appropriate department based on the ticket text. The system must handle new categories that emerge over time without retraining. Which TWO approaches should the company combine to achieve this? (Select TWO.)

Accepted Answer

Use Amazon Comprehend for custom classification. Option B is correct because Amazon Comprehend custom classification can categorize ticket text into departments, providing the routing decision. Option E is correct because Amazon Kendra indexes support documents and uses semantic search to match ticket text to the most relevant department knowledge base, allowing new or emerging categories to be handled through indexed content without retraining the classifier. Together, Comprehend custom classification and Kendra enable routing that adapts to new categories by using Kendra to surface relevant department knowledge for unfamiliar tickets. Option A is incorrect because Amazon Forecast predicts time-series values like ticket volume, not ticket content or routing. Option C is incorrect because Comprehend topic modeling and entity detection discover topics and entities but do not directly route tickets to departments. Option D is incorrect because Amazon Rekognition analyzes images and video, not support ticket text.

Answer

Use Amazon Forecast to predict ticket volume

Answer

Use Amazon Comprehend for topic modeling and entity detection

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Use Amazon Rekognition to analyze ticket images

Question 9

A company is using Amazon Bedrock Agents to build a travel booking assistant that can search for flights, book hotels, and answer questions about travel policies. Which TWO components are required to enable the agent to call external services? (Select TWO.)

Accepted Answer

Action group with API schema. Option A (Action group with API schema) is correct because an action group defines the set of tasks the Bedrock Agent can perform and includes an OpenAPI schema that describes the external APIs the agent can invoke, which is the mechanism for calling external services. Option B (AWS Lambda function) is correct because Bedrock Agents use Lambda functions as the compute layer to execute the business logic for action groups, receiving the agent's request and returning the API response. Option C (Amazon DynamoDB table) is not required; it is only a possible data store for the Lambda function's implementation, not a component that enables external service calls. Option D (Amazon SageMaker endpoint) is not required; it is used for hosting custom ML models, not for enabling agent action groups. Option E (Bedrock Knowledge Base) is not required; it provides retrieval-augmented generation over data sources for answering questions, not for calling external services.

Answer

Amazon DynamoDB table

Answer

Amazon SageMaker endpoint

Answer

Bedrock Knowledge Base

Question 10

A data science team is evaluating the output quality of a text generation model. They want to use both automated metrics and human judgment. Which THREE approaches should they include in their evaluation strategy? (Select THREE.)

Accepted Answer

Conduct human evaluation with a rating rubric. Option A is correct because human evaluation with a rating rubric directly captures subjective quality dimensions such as fluency, coherence, relevance, and factual consistency that automated metrics cannot fully assess, which is essential when the team explicitly wants human judgment. Option D is correct because ROUGE (recall-oriented n-gram/LCS overlap) and BLEU (precision-oriented n-gram overlap with brevity penalty) are standard automated reference-based metrics for comparing generated text against reference texts, matching the team's goal of using automated metrics. Option E is correct because running the model on a task-specific benchmark dataset provides standardized, objective, task-relevant performance measurement (e.g., accuracy, F1, exact match) that complements both human ratings and text-overlap metrics. Option B does not belong because inference latency under load measures operational performance and scalability, not output quality of the generated text. Option C does not belong because perplexity is typically computed on a language model's token likelihoods over a corpus and is not a reliable measure of the quality of generated text, especially for open-ended generation.

Answer

Measure inference latency under load

Answer

Compute perplexity on the generated text

Question 11

Which TWO statements about the bias-variance tradeoff are correct? (Choose TWO.)

Accepted Answer

High bias typically leads to underfitting. Option C is correct because high bias means the model makes overly simplistic assumptions about the data, so it fails to capture the underlying pattern and systematically underfits both training and test data. Option E is correct because high variance means the model is overly sensitive to the particular training samples, fitting noise as well as signal, which produces excellent training performance but poor generalization—the hallmark of overfitting. Option A is wrong because high bias leads to underfitting, not overfitting; overfitting is associated with high variance. Option B is wrong because high variance models are highly sensitive to changes in training data, not insensitive. Option D is wrong because increasing model complexity typically increases variance (and decreases bias), not decreases variance.

Answer

High bias typically leads to overfitting

Answer

High variance models are insensitive to changes in training data

Answer

Increasing model complexity typically decreases variance

Question 12

A company uses Amazon SageMaker to build and deploy models. They want to enforce compliance that all model endpoints are encrypted in transit and use least privilege access. Which THREE steps should they take? (Choose THREE.)

Accepted Answer

Configure the SageMaker endpoint to use a custom SSL certificate via AWS Certificate Manager. Configuring a SageMaker endpoint to use a custom SSL certificate from AWS Certificate Manager (ACM) ensures that all data transmitted between clients and the endpoint is encrypted in transit using TLS. This enforces the compliance requirement for encryption in transit by replacing the default SageMaker certificate with a customer-managed certificate, which can be validated and rotated as needed.

Answer

Enable AWS CloudTrail to log all endpoint invocations

Answer

Disable root access on the SageMaker notebook instances

Question 13

Which THREE considerations are essential for ensuring responsible AI in a model that predicts employee performance? (Choose 3)

Accepted Answer

Incorporate human review before final decisions. Option C is correct because responsible AI in an HR context requires human-in-the-loop oversight: performance predictions should inform, not automate, decisions about employees, so a human reviewer can catch errors and provide context before any action is taken. Option D is correct because employee performance data is personal and often sensitive, so the model must comply with privacy and consent requirements (e.g., GDPR lawful basis, purpose limitation, and data minimization) to be ethically and legally sound. Option E is correct because bias testing across demographic groups (e.g., comparing error rates, selection rates, and disparate impact metrics by gender, age, or ethnicity) is essential to detect and mitigate discriminatory outcomes in performance predictions. Option A does not belong because minimizing features to cut cost is an efficiency concern, not a responsible-AI safeguard, and can even harm fairness if it removes relevant variables. Option B does not belong because publishing individual employees' predicted performance publicly would violate privacy and confidentiality rather than constitute meaningful transparency, which is better served by model documentation and explainability to authorized stakeholders.

Answer

Minimize the number of features to reduce cost

Answer

Publish the model's predictions publicly for transparency

Question 14

A company uses Amazon SageMaker Pipelines for MLOps. The security team requires that all pipeline steps use only approved Docker images from a private Amazon ECR repository, and that all pipeline artifacts are encrypted with a customer managed KMS key. Which THREE steps must the company configure to meet these requirements? (Choose three.)

Accepted Answer

Specify a KMS key in the pipeline definition for encrypting output artifacts.. Amazon SageMaker Pipelines allows you to specify a KMS key in the pipeline definition to encrypt output artifacts at rest. This ensures that all artifacts generated by pipeline steps are encrypted using a customer managed KMS key, meeting the security team's encryption requirement.

Answer

Set an ECR lifecycle policy to delete untagged images older than 30 days.

Answer

Enable AWS Config rules to check for public ECR repositories.

Question 15

Which TWO of the following are benefits of using Amazon Bedrock for building applications with foundation models?

Accepted Answer

No infrastructure management. Option A (No infrastructure management) is correct because Amazon Bedrock is a fully managed, serverless service: AWS handles provisioning, scaling, patching, and hosting of the underlying compute for the foundation models, so developers just call the API without managing any servers or clusters. Option C (Access to multiple foundation models) is correct because Bedrock provides a single unified API to choose from a range of foundation models from providers such as Anthropic, AI21 Labs, Cohere, Meta, Stability AI, and Amazon, letting you switch or compare models without integrating separate SDKs or endpoints. Option B is not a Bedrock benefit as stated, since fine-tuning is an optional capability you must explicitly configure (and not all models support it), not an automatic feature. Option D is incorrect because Bedrock is not free for all models; usage is billed per input/output token or per image, and pricing varies by model. Option E is incorrect because image generation is only available through specific models (e.g., Stability AI or Amazon Titan Image Generator), not as a universal built-in capability of the service.

Answer

Automatic model fine-tuning

Answer

Free tier for all models

Answer

Built-in image generation capability

Question 16

A company is building a generative AI application using Amazon Bedrock and needs to ensure that the model does not generate outputs containing personally identifiable information (PII). Which TWO actions should the company take? (Choose 2)

Accepted Answer

Implement a custom AWS Lambda function to scan and redact PII from inputs and outputs.. Option A is correct because a custom AWS Lambda function can programmatically scan both the prompt inputs and the model responses for PII patterns (for example, using regex or Amazon Comprehend's PII detection APIs) and redact or block them before they reach the user, giving the company full control over what data enters and leaves the generative AI application. Option D is correct because Amazon Bedrock Guardrails provides a native, purpose-built sensitive information filter that can detect and either block or mask PII entities (such as names, emails, and SSNs) in both user inputs and model outputs, directly satisfying the requirement to prevent PII from appearing in generated content. Option B is not correct because IAM policies only control who can invoke which Bedrock models and actions; they do not inspect or filter the content of prompts and responses for PII. Option C is not correct because CloudWatch Logs only captures and stores model outputs for auditing and monitoring after the fact; it does not prevent PII from being generated or returned. Option E is not correct because placing the Bedrock endpoint in a private VPC only affects network isolation and traffic routing, not the content-level detection or redaction of PII in model outputs.

Answer

Use AWS Identity and Access Management (IAM) policies to restrict model access.

Answer

Enable Amazon CloudWatch Logs to capture and audit model outputs.

Answer

Place the Bedrock model endpoint within a private VPC.

Question 17

Which TWO are best practices for model monitoring in production on AWS?

Accepted Answer

Monitor input data drift. Option C (Monitor input data drift) is correct because production models degrade when the statistical distribution of incoming features diverges from training data, so tracking data drift with tools like SageMaker Model Monitor detects this covariate shift early. Option E (Monitor prediction drift) is correct because shifts in the distribution of model outputs (e.g., changing class proportions or score distributions) signal concept drift or upstream data issues that require investigation or retraining. Together, input and prediction drift monitoring are the two core best practices for maintaining model quality in production. Option A is wrong because disabling logging removes the observability needed to detect drift, errors, and bias, and logging overhead is typically negligible relative to inference. Option B is wrong because instance type (CPU vs. GPU) is a performance/cost choice, not a monitoring best practice. Option D is wrong because retraining daily is not a best practice by itself; retraining should be triggered by monitored drift or performance degradation, and daily retraining can be costly and destabilizing.

Answer

Disable logging to reduce latency

Answer

Use only CPU instances

Answer

Retrain model daily

Question 18

A data scientist is using a foundation model to summarize long documents. Which TWO of the following steps are most likely to improve the quality of the summaries?

Accepted Answer

Break the input document into chunks and summarize each chunk separately.. Option A is correct because chunking a long document and summarizing each chunk separately keeps each request within the model's effective context window, avoiding truncation and the degraded recall that occurs when a foundation model must attend to very long inputs, and the chunk summaries can then be combined into a final summary. Option C is correct because few-shot prompting supplies concrete examples of the desired summary style, length, and level of detail, which steers the model's output distribution toward the target format more reliably than a bare instruction. Option B is not appropriate because a high temperature increases randomness and creativity, which harms factual fidelity and consistency in summarization. Option D is not the priority here because frequency penalty only discourages repeated tokens and does not address the core problems of long-input context limits or output style alignment. Option E is not correct because increasing max tokens only raises the output length cap; it does not extend the model's usable context window for the input document and can even encourage overly long, unfocused summaries.

Answer

Use a high temperature parameter to increase creativity.

Answer

Use a low frequency penalty to reduce repetition.

Answer

Use a longer context length by increasing the max tokens parameter.

Question 19

Which THREE steps are typically involved in fine-tuning a foundation model? (Select THREE.)

Accepted Answer

Prepare a labeled dataset specific to the target domain. Fine-tuning a foundation model begins with selecting an appropriate pre-trained foundation model as the starting point (D), since the whole point of fine-tuning is to adapt existing general-purpose weights rather than train from scratch. Next, you must prepare a labeled dataset specific to the target domain (B), because supervised fine-tuning requires task-relevant input-output pairs to steer the model toward the desired behavior. Then you train the model on that domain dataset with a lower learning rate (C), which is standard practice to avoid catastrophic forgetting and to gently nudge the pre-trained weights instead of overwriting them. Option A is incorrect because deploying the model immediately without additional training is the opposite of fine-tuning—it describes using the base model as-is. Option E is incorrect because fine-tuning does not require choosing an architecture with more parameters than the base model; you typically fine-tune the same pre-trained architecture, and increasing parameter count is not a fine-tuning step.

Answer

Deploy the model immediately without additional training

Answer

Choose a model architecture with more parameters than the base model

Question 20

A data scientist is setting up a RAG pipeline using Amazon Bedrock Knowledge Bases. They need to ingest documents, split them into chunks, generate embeddings, and store them for retrieval. Which TWO steps are part of the ingestion process? (Choose TWO)

Accepted Answer

Parse the documents and split them into chunks. Option D is correct because the ingestion (data source sync) process in Amazon Bedrock Knowledge Bases begins by parsing the source documents and splitting them into manageable chunks during the chunking step, which is required before embeddings can be created. Option E is correct because after chunking, the service invokes a selected Amazon Bedrock embedding model (for example, Amazon Titan Embeddings or Cohere Embed) to convert each chunk into a vector representation that is then written to the vector store. Option A is not part of the standard ingestion flow; while Lambda functions can be used with custom transformations or orchestration, defining a Lambda for post-processing is not a required ingestion step. Option B is incorrect because similarity search is a retrieval-time operation performed against the vector store, not part of ingestion. Option C is also incorrect because augmenting the user prompt with retrieved chunks happens during the retrieval/generation (query) phase, after ingestion is complete.

Answer

Define a Lambda function for post-processing

Answer

Perform a similarity search on the vector store

Answer

Augment the user prompt with retrieved chunks