AIF-C01 Practice Test 43 — 15 Questions

Question 1

An IAM policy allows creation of SageMaker training jobs only if they use a specific VPC security group. A user tries to create a training job without specifying that security group. What will happen?

Accepted Answer

The request will be denied with an AccessDenied error. IAM policies are evaluated before any AWS API action is executed. If the policy includes a condition that requires a specific VPC security group for SageMaker training jobs, and the user's request does not include that security group, the condition is not met, resulting in an explicit deny (AccessDenied error). AWS IAM denies the request by default if the condition in a policy is not satisfied, regardless of whether the condition is marked as optional in the API.

Answer

The request will succeed but SageMaker will ignore the condition

Answer

The request will succeed because the condition is optional

Answer

The request will be denied because the training job resource ARN is invalid

Question 2

A developer encounters the error shown above when using Amazon Bedrock. What is the most likely cause?

Accepted Answer

The IAM role lacks the required permission. The error indicates an access denied or authorization failure when invoking the Amazon Bedrock model. The most likely cause is that the IAM role used by the developer does not have the required permission, such as `bedrock:InvokeModel`, attached to its policy. Without this permission, the API call to Bedrock is rejected regardless of model availability or service status.

Answer

The model is not available in the region

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The request is throttled

Answer

The model is out of service

Question 3

A data science team is fine-tuning a Llama 2 7B model on Amazon SageMaker for a text classification task. After the first training run, they notice the loss is not decreasing and the model is overfitting to the small training set. What should the team change to mitigate overfitting?

Accepted Answer

Add dropout layers and reduce the learning rate.. Adding dropout layers introduces regularization by randomly dropping neurons during training, which prevents the model from relying too heavily on specific features and reduces overfitting. Reducing the learning rate helps the model converge more smoothly and avoid oscillating around a suboptimal minimum, which is especially important when fine-tuning a large model like Llama 2 7B on a small dataset. Together, these changes address the core issues of overfitting and non-decreasing loss.

Answer

Increase the number of epochs to allow the model to learn more patterns.

Answer

Increase the batch size and use gradient accumulation.

Answer

Remove dropout layers from the model architecture.

Question 4

A data scientist is using Amazon Bedrock to generate product descriptions. They notice the output is often repetitive and lacks creativity. Which combination of parameter adjustments is MOST likely to produce more diverse and less repetitive output?

Accepted Answer

Increase temperature and increase top-p. Increasing temperature raises the probability of sampling lower-probability tokens, which increases randomness and diversity. Increasing top-p (nucleus sampling) expands the set of tokens considered for sampling, further reducing repetitiveness. Together, these adjustments encourage the model to explore a wider range of possible continuations, producing more creative and less repetitive output.

Answer

Decrease temperature and decrease top-p

Answer

Decrease temperature and increase top-p

Answer

Increase temperature and decrease top-p

Question 5

A bank uses an AI system to detect fraudulent transactions. The model has high precision but low recall for small transactions, potentially missing fraud. Which approach aligns with responsible AI?

Accepted Answer

Tune the model to achieve an acceptable balance between recall and precision. Responsible AI requires balancing competing objectives like precision and recall to align with ethical principles and business needs. In fraud detection, high precision with low recall means many fraudulent transactions are missed, which can lead to significant financial losses and erode customer trust. Tuning the model to achieve an acceptable trade-off ensures that the system is both effective and fair, minimizing harm while maintaining operational viability.

Answer

Send all flagged transactions to customers for confirmation

Answer

Focus only on precision to minimize false positives

Answer

Increase the detection threshold to reduce false positives

Question 6

A company uses Amazon Bedrock to generate code snippets for internal tools. They notice that the generated code often contains security vulnerabilities such as SQL injection and cross-site scripting. The security team has compiled a comprehensive list of secure coding guidelines and examples of vulnerable patterns. The development team wants to reduce vulnerabilities without significantly slowing down the code generation process. They have tried adding the guidelines to the system prompt, but the model still produces insecure code occasionally. The team is considering additional measures. Which action should they take to most effectively eliminate security vulnerabilities in the generated code?

Accepted Answer

Implement a post-processing step using Amazon CodeGuru or a similar static analysis tool to scan the generated code for vulnerabilities and reject or fix insecure code.. It introduces a deterministic, post-generation validation layer that catches vulnerabilities the model might miss. Amazon CodeGuru Reviewer or similar static analysis tools can scan generated code for patterns like SQL injection and XSS, then reject or fix insecure code without modifying the generation process itself. This approach directly addresses the security team's guidelines while maintaining generation speed, as the model's inference latency is unaffected.

Answer

Use a larger, more expensive foundation model that specializes in code generation.

Answer

Include the complete secure coding guidelines in every prompt.

Answer

Increase the temperature parameter of the foundation model to promote more diverse outputs.

Question 7

An e-commerce company uses Amazon Personalize to provide product recommendations. The business team observes that the recommendations are dominated by popular items and rarely suggest niche products, even for users with long purchase histories. Which Personalize recipe or configuration change would BEST address this issue?

Accepted Answer

Use the aws-user-personalization recipe and enable the explore-holdoff feature. The aws-user-personalization recipe includes an explore-holdoff feature that controls the balance between exploiting known user preferences and exploring less popular items. Enabling this feature reduces the dominance of popular items by allowing the model to recommend niche products, even for users with long purchase histories, addressing the bias toward popular items.

Answer

Increase the minimum interaction threshold for item inclusion

Answer

Decrease the learning rate for the model

Answer

Switch from aws-user-personalization recipe to aws-popularity-count recipe

Question 8

A company is deploying a real-time chatbot using Amazon Bedrock and expects high traffic during business hours. They want to minimise inference costs while maintaining low latency. Which combination of strategies would be MOST effective?

Accepted Answer

Enable model caching and use a smaller, faster foundation model. Model caching can serve repeated queries quickly without re-invocation, and batch inference is cost-effective for non-real-time workloads but not for real-time. Right-sizing means choosing a model that balances cost and performance.

Answer

Increase the context length to handle more conversations per prompt

Answer

Use batch inference for all requests and provision a large model

Answer

Disable content filtering to reduce processing time

Question 9

An e-commerce company uses a foundation model to generate personalized email subject lines. The marketing team notices that the subject lines sometimes contain product recommendations that are out of stock. Which action would best reduce the generation of out-of-stock recommendations without retraining the model?

Accepted Answer

Add a system prompt that explicitly instructs the model to only recommend products that are in stock.. Adding a system prompt that explicitly instructs the model to only recommend in-stock products directly constrains the model's output at inference time without requiring retraining. This leverages the model's instruction-following capability to filter its generated content based on the provided context, which is a lightweight and immediate solution.

Answer

Implement a post-processing step to replace out-of-stock recommendations with in-stock alternatives.

Answer

Fine-tune the model on a dataset of past successful subject lines that only include in-stock products.

Answer

Use a retrieval-augmented generation (RAG) approach to retrieve a list of in-stock products and include it in the prompt.

Question 10

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?

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

Answer

AWS Shield

Answer

Amazon Inspector

Question 11

A data scientist is fine-tuning a foundation model on Amazon Bedrock for a custom summarization task. Which THREE practices should they follow to optimize the fine-tuning process?

Accepted Answer

Start with a base model that is already strong in the domain.. Starting with a base model that is already strong in the domain (Option A) is correct because it reduces the amount of fine-tuning data and compute required. Amazon Bedrock provides access to various foundation models (e.g., Anthropic Claude, Amazon Titan) that have been pre-trained on diverse corpora; selecting one that is already proficient in the target domain (e.g., legal or medical summarization) means the model's existing knowledge can be adapted with fewer training steps, leading to better performance and lower risk of catastrophic forgetting.

Answer

Use the default hyperparameters without tuning.

Answer

Train for as many epochs as possible.

Question 12

A healthcare company is deploying a machine learning model on Amazon SageMaker to analyze patient records. The model requires access to a DynamoDB table containing patient data. Which combination of AWS services and features should the company use to restrict access to only the necessary resources?

Accepted Answer

Create an IAM role with a policy granting read-only access to the specific DynamoDB table and attach it to the SageMaker notebook instance. It follows the AWS principle of least privilege by creating an IAM role with a policy that grants read-only access to the specific DynamoDB table, then attaching that role to the SageMaker notebook instance. This ensures the notebook can only perform read operations on the required table without exposing long-term credentials or granting broader permissions.

Answer

Attach a DynamoDB resource-based policy to the table allowing access from the SageMaker notebook

Answer

Store AWS access keys in the notebook and use those credentials to access DynamoDB

Answer

Launch the SageMaker notebook in a VPC with a security group that allows access to DynamoDB

Question 13

A healthcare company is using Amazon SageMaker to train and deploy a model that predicts patient readmission risk. The model uses sensitive protected health information (PHI). The company must ensure that data is encrypted at rest and in transit, and that access to the model endpoint is restricted to authorized applications only. The security team has configured AWS KMS customer managed keys for encryption, and IAM roles for SageMaker execution. However, during a security audit, it was discovered that the model endpoint is accessible from the internet and that the data used for training was stored in an S3 bucket with default encryption enabled. The compliance team requires that all PHI data be encrypted with a key that is rotated annually, and that no public access is allowed to the endpoint or training data. Which combination of actions should the ML engineer take to remediate these issues?

Accepted Answer

Enable S3 bucket encryption with a customer managed KMS key, disable public access on the SageMaker endpoint by deploying it in a VPC, and configure the KMS key to rotate annually.. It addresses all compliance requirements: enabling S3 bucket encryption with a customer managed KMS key ensures PHI is encrypted at rest with a key that can be rotated annually, deploying the SageMaker endpoint in a VPC removes public internet access, and configuring annual KMS key rotation satisfies the rotation policy. This combination ensures encryption at rest and in transit (via VPC), restricts endpoint access to authorized applications only, and meets the key rotation requirement.

Answer

Use a SageMaker notebook instance with a lifecycle configuration to encrypt data with a customer managed KMS key, and restrict endpoint access using an IAM policy.

Answer

Enable S3 bucket encryption with SSE-S3, attach a bucket policy denying public access, and use an AWS Lambda function to rotate the S3 bucket key every year.

Answer

Apply SSE-KMS with an AWS managed key to the S3 bucket, and use a Lambda function to rotate the key every year. Disable public access to the endpoint using a VPC endpoint.

Question 14

A company is building a multi-modal application that processes images and text to answer questions about product defects. Which foundation model approach is BEST?

Accepted Answer

Use a multi-modal foundation model that processes both images and text. Multi-modal foundation models (e.g., CLIP, Flamingo, GPT-4V) are specifically designed to jointly process and reason over images and text in a unified architecture. This allows the model to directly correlate visual defects with textual descriptions without intermediate lossy transformations, making it the most effective approach for a multi-modal QA task.

Answer

Use an image captioning model and then analyze the caption text

Answer

Use a text-to-image generation model and analyze the generated image

Answer

Use a separate image analysis model and a text model, then combine outputs

Question 15

A company is deploying a generative AI model on Amazon Bedrock. The model is accessed by an application running on Amazon ECS Fargate. The security team requires that all data sent to and from the Bedrock model be encrypted in transit, and that the ECS task does not have internet access. The ECS tasks are launched in a private VPC subnet with a VPC endpoint for Bedrock configured. However, when the application attempts to invoke the Bedrock model, the call fails with a timeout error. The VPC endpoint policy is set to allow all actions from all principals. What is the most likely cause of the failure?

Accepted Answer

The security group associated with the VPC endpoint does not allow inbound HTTPS traffic from the ECS tasks. The timeout error indicates a network connectivity issue, not an authentication or authorization problem. Since the ECS tasks are in a private subnet without internet access and a VPC endpoint for Bedrock is configured, the most likely cause is that the security group associated with the VPC endpoint does not allow inbound HTTPS (port 443) traffic from the ECS tasks. Without this inbound rule, the TCP handshake fails, resulting in a timeout.

Answer

The ECS tasks are not configured to use a NAT gateway to reach the Bedrock endpoint

Answer

The VPC endpoint policy does not grant the ECS task IAM role permission to invoke Bedrock

Answer

The ECS task IAM role does not have the bedrock:InvokeModel permission