AIF-C01 Practice Test 19 — 15 Questions

Question 1

A company wants to monitor for malicious activity in their machine learning pipelines, such as unauthorized access to training data or model artifacts. Which AWS service can provide automated threat detection and continuous monitoring?

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

Accepted Answer

Amazon GuardDuty. Amazon GuardDuty is a threat detection service that continuously monitors for malicious activity and unauthorized behavior across AWS workloads, including machine learning pipelines. It uses machine learning, anomaly detection, and integrated threat intelligence to identify threats such as unauthorized access to S3 buckets containing training data or model artifacts, without requiring manual intervention.

Answer

AWS Config

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AWS Shield

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Amazon Inspector

Question 2

A financial services company uses Bedrock Agents to automate a multi-step loan approval process. The agent needs to call an external credit scoring API and a compliance database, then combine results. The agent currently fails when the API returns a 503 error. How should the practitioner address this?

Accepted Answer

Implement a custom Lambda function for the action group to handle errors and retries. Lambda functions integrated with action groups can implement retry logic, error handling, and custom processing. Granularity tuning or trace truncation does not handle errors. Guardrails are for content filtering, not API error handling.

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Adjust the Bedrock Agent's granularity setting to 'high'

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Reduce the agent's trace truncation limit to shorten the context

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Use Bedrock Guardrails with a topic denial rule for error messages

Question 3

An organization wants to document their model's intended use, limitations, performance metrics, and ethical considerations. Which tool or practice is designed specifically for this purpose?

Accepted Answer

Model cards. Model cards are short documents that accompany a trained model and describe its intended use, limitations, performance across subgroups, training data, and ethical considerations. They were popularized by Google and are now a standard responsible-AI artifact, making them the tool designed specifically for documenting a model's context and caveats. This matches the requirement to capture intended use, limitations, metrics, and ethics in one place.

Answer

Amazon SageMaker Clarify

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AWS CloudTrail

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Amazon SageMaker Model Monitor

Question 4

A developer is using the Amazon Bedrock Converse API to build a multi-turn conversation application. The developer wants the model to adopt a specific persona and follow strict formatting rules for all responses. Which approach should the developer take?

Accepted Answer

Set the persona and formatting instructions in the system prompt. The Amazon Bedrock Converse API supports a system prompt that allows developers to set global instructions, such as persona and formatting rules, which persist across the entire conversation. This is the correct approach because system prompts are designed to define the model's behavior and constraints without needing to repeat them in every user message, ensuring consistency and efficiency.

Answer

Include the persona and formatting instructions in every user message

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Use few-shot examples in the first user message only

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Adjust the temperature to 0 to force deterministic formatting

Question 5

A financial services company uses Amazon Rekognition to verify customer identities. To ensure responsible AI practices, which measure should the company prioritize?

Accepted Answer

Regularly audit the model for demographic bias. Regularly auditing the model for demographic bias is a core responsible AI practice, especially for identity verification systems where biased outcomes could lead to unfair treatment of certain customer groups. Amazon Rekognition's facial analysis and comparison features must be tested across diverse demographics to ensure equitable performance, as bias can arise from imbalanced training data or algorithmic artifacts.

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Use only black-box models to protect intellectual property

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Increase model complexity to improve accuracy

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Minimize the amount of training data collected

Question 6

What is the primary purpose of a model card?

Accepted Answer

To document the model's intended use, performance, and limitations for transparency. A model card is a document that provides essential information about a machine learning model, including its intended use, performance characteristics, limitations, and ethical considerations. Its primary purpose is to promote transparency and responsible AI by helping users understand when and how to use the model appropriately. It is not a technical artifact for deployment or registration.

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To provide a detailed performance benchmark on a single metric

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To register the model in SageMaker Model Registry

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To store the model's parameters and weights for deployment

Question 7

A company is using Amazon Bedrock to offer a multi-tenant summarization service. They notice high latency during peak hours. The team wants to reduce per-request latency without degrading quality. Which combination of actions would be MOST effective?

Accepted Answer

Enable model caching and right-size the model selection to a smaller, faster model. Enabling model caching allows frequently used prompt contexts to be reused without recomputation, directly reducing latency. Right-sizing to a smaller, faster model (e.g., from a 70B to an 8B parameter model) reduces inference time per request while maintaining acceptable quality for summarization tasks, addressing peak-hour latency without degrading output.

Answer

Enable model caching and switch to a larger model

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Use batch inference and disable guardrails

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Increase the chunk size in the Knowledge Base and use a vector store

Question 8

A company is using Amazon Bedrock to deploy a generative AI application. They want to implement guardrails to prevent the model from generating harmful or offensive content. Which feature of Bedrock Guardrails should they configure?

Accepted Answer

Configure denied topics and content filters. Amazon Bedrock Guardrails provides configurable safeguards including content filters and denied topics. Content filters block harmful categories (hate, violence, sexual, insults, misconduct) based on configurable strength, while denied topics let you define specific subjects the model must refuse to discuss. Together they directly prevent harmful or offensive output at inference time.

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Enable data privacy settings in the model invocation

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Use prompt engineering to instruct the model to be safe

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Set up invocation logging to review all outputs

Question 9

A healthcare company is deploying a model to predict patient readmission risk using Amazon SageMaker. The model processes protected health information (PHI). The company must adhere to HIPAA regulations, which require that all PHI is encrypted at rest and in transit, and that access to the data is logged and audited. The data is stored in an S3 bucket encrypted with SSE-S3. The SageMaker training job uses a custom Docker container that reads data from the S3 bucket and writes model artifacts back to another S3 bucket. The security team notices that the model artifacts in the output bucket are not encrypted. Also, the training logs in CloudWatch Logs contain PHI because the training script logs patient features. The company must fix these issues without delaying the deployment. What should the company do?

Accepted Answer

Modify the training container to avoid logging PHI, and configure the output S3 bucket to use SSE-KMS encryption.. It directly addresses both issues: modifying the training container to avoid logging PHI ensures CloudWatch Logs no longer contain protected health information, and configuring the output S3 bucket to use SSE-KMS encryption ensures model artifacts are encrypted at rest. SSE-KMS is preferred over SSE-S3 for HIPAA compliance because it provides additional controls like key rotation and audit trails. This approach fixes the problems without delaying deployment, as it requires only code and configuration changes.

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Implement client-side encryption for the output bucket, and update the training script to mask PHI before logging.

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Change the output S3 bucket to use SSE-KMS encryption, and enable server access logs for the bucket.

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Configure the training job to use a VPC with private subnets, and enable data encryption in transit using TLS.

Question 10

A company wants to build a model to forecast monthly sales. The data is a time series with trend and seasonality. Which SageMaker algorithm is most appropriate?

Accepted Answer

DeepAR. DeepAR is the most appropriate algorithm because it is specifically designed for time series forecasting, handling both trend and seasonality through autoregressive recurrent neural networks. It learns from multiple related time series and produces probabilistic forecasts, making it ideal for monthly sales prediction.

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XGBoost

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K-Means

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Linear Learner

Question 11

A company is building a RAG solution using Amazon Bedrock Knowledge Bases. Which TWO steps are essential in the document ingestion pipeline? (Select TWO.)

Accepted Answer

Generating embeddings for each chunk. Option D is correct because chunking documents into smaller segments is a required preprocessing step in a Bedrock Knowledge Bases ingestion pipeline; splitting source documents into manageable chunks allows the system to process and index content effectively. Option C is correct because generating embeddings for each chunk is essential: Bedrock invokes an embedding model (for example, Amazon Titan Embeddings or Cohere Embed) to convert each chunk into a vector, which is then stored in the vector store for semantic retrieval during RAG queries. Option A is not essential to the ingestion pipeline because a model endpoint for real-time inference relates to query-time generation, not document ingestion. Option B is incorrect because Bedrock Guardrails apply content filtering and safety policies at inference time and are not a required ingestion step. Option E is incorrect because fine-tuning the model on the documents is a separate model-customization activity and is not part of the Knowledge Bases ingestion pipeline, which relies on embeddings and vector storage rather than fine-tuning.

Answer

Setting up a model endpoint for real-time inference

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Creating a Bedrock Guardrail for the documents

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Fine-tuning the model on the documents

Question 12

A data scientist at a retail company is tasked with building a model to predict customer churn. The dataset contains 100,000 records with features such as age, purchase history, customer support interactions, and a binary label indicating whether the customer churned in the past. The team needs a model that can be deployed for real-time inference with low latency. They have limited time and want to use a built-in algorithm from Amazon SageMaker that is optimized for classification tasks. Which approach should they take?

Accepted Answer

Use Amazon SageMaker XGBoost algorithm. Amazon SageMaker's built-in XGBoost algorithm is optimized for classification tasks like binary churn prediction, supports real-time inference with low latency via SageMaker endpoints, and can handle the dataset size of 100,000 records efficiently. It is a supervised learning algorithm that directly uses the binary label for training, making it the correct choice for this scenario.

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Use Amazon SageMaker PCA algorithm

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Use Amazon SageMaker K-Means algorithm

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Use Amazon SageMaker BlazingText algorithm

Question 13

A financial services company is deploying a machine learning model that must comply with SOC 2 and PCI DSS. They need to ensure that the model artifacts and training data are encrypted, access is audited, and the environment is protected from network threats. Which THREE AWS services should they implement?

Accepted Answer

Amazon GuardDuty. Amazon GuardDuty is a threat detection service that continuously monitors for malicious activity and unauthorized behavior, which helps protect the environment from network threats as required by SOC 2 and PCI DSS. By analyzing VPC Flow Logs, DNS logs, and CloudTrail events, it can detect anomalies such as port scanning or data exfiltration, directly addressing the need for network threat protection in a compliant ML deployment.

Answer

Amazon DynamoDB Accelerator (DAX)

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AWS WAF

Question 14

Refer to the exhibit. A developer wants to choose a model that can generate text (not just embeddings) and has the lowest cost. Based on the exhibit, which model should they select?

Accepted Answer

Titan Text Lite. Titan Text Lite is the correct choice because it is designed for text generation tasks and is explicitly positioned as the lowest-cost option among Amazon's Titan text generation models. Unlike Titan Embed Text, which only produces embeddings and cannot generate text, Titan Text Lite offers a cost-efficient solution for generating text while meeting the requirement of lowest cost.

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Titan Embed Text

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Titan Text Express

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Question 15

A machine learning engineer notices that a generative AI model occasionally produces biased outputs. Which AWS feature can automatically filter harmful content before it reaches users?

Accepted Answer

Amazon Bedrock Guardrails. Amazon Bedrock Guardrails is specifically designed to implement safeguards for generative AI applications, including the ability to filter harmful, biased, or inappropriate content before it reaches users. It allows you to define denied topics, content filters, and sensitive information filters that are applied at inference time, directly addressing the need to automatically filter biased outputs from a generative AI model.

Answer

Amazon CloudWatch alarms

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Amazon SageMaker Clarify

Answer

AWS Identity and Access Management (IAM) policies