AIF-C01 Practice Test 23 — 15 Questions

Question 1

An insurance company uses a machine learning model to adjust premiums. During a review, the model is found to be penalizing customers based on zip codes correlated with racial demographics, leading to potential discrimination. Which combination of actions best addresses this fairness issue while maintaining business value?

Accepted Answer

Re-engineer features to avoid proxies for protected attributes and rebalance training data. The most effective approach is to re-engineer features to remove proxy variables for protected attributes (like race) AND to rebalance the training data to ensure fair representation. Simply removing zip code may not eliminate proxies, and ignoring the issue is not acceptable. Using a different model without data changes may not solve the problem.

Answer

Remove the zip code feature from the model and retrain

Answer

Continue using the model but add a disclaimer about potential bias

Answer

Replace the model with a simpler linear model

Question 2

Which TWO actions are essential for ensuring accountability in AI systems according to AWS responsible AI guidelines?

Accepted Answer

Establish clear human oversight and decision-making authority. Accountability requires clear ownership and documentation. Establishing human oversight and maintaining version control of models are key. Removing humans reduces accountability. Using only automated decisions removes oversight. Sharing raw data broadly may violate privacy.

Answer

Automate all decisions to ensure consistency

Answer

Remove all human review processes to eliminate bias

Answer

Share raw training data publicly for transparency

Question 3

A data science team needs to choose a machine learning approach for a project that requires predicting customer churn based on historical data. The team has a labeled dataset with 10,000 records and needs to interpret the model's decisions to provide business insights. Which machine learning technique should the team prioritize?

Accepted Answer

Random forest.. Option C is correct because random forests are ensemble methods that offer feature importance and decision paths, making them interpretable for churn prediction with labeled data. Option A (deep neural network) is less interpretable and may overfit with limited data. Option B (linear regression) is for regression tasks, not classification. Option D (K-means clustering) is unsupervised and not suitable for predicting churn with labeled data.

Answer

K-means clustering.

Answer

Linear regression.

Answer

Deep neural network with multiple hidden layers.

Question 4

Refer to the exhibit. A data scientist trained an XGBoost model using Amazon SageMaker. Which TWO actions should the data scientist take to improve the model's performance based on the exhibited training job metrics and resource configuration?

Accepted Answer

Decrease the learning rate and increase the number of training rounds.. Option A is correct because increasing the maximum number of leaf nodes allows the model to capture more complex patterns, potentially improving AUC. Option C is correct because a lower learning rate with more training rounds often leads to better convergence and performance. Option B (distributed training) primarily reduces training time, not model performance. Option D (switching instance type) also speeds up training but doesn't directly improve metrics. Option E (reducing volume size) saves cost but does not enhance performance.

Answer

Reduce the VolumeSizeInGB to save costs.

Answer

Use a distributed training strategy by increasing InstanceCount to 4.

Answer

Switch to a more powerful instance type to reduce training time.

Question 5

A company is deploying a machine learning model using Amazon SageMaker. They need to ensure that the training data, which contains personally identifiable information (PII), is encrypted both in transit and at rest. They also need to control access to the model endpoints. Which TWO actions should the company take?

Accepted Answer

Use AWS Certificate Manager (ACM) to attach an SSL/TLS certificate to the SageMaker endpoint.. Option A ensures data at rest encryption using KMS for the S3 bucket and SageMaker notebook instance. Option D ensures data in transit encryption by attaching an SSL/TLS certificate to the SageMaker endpoint via ACM. Option B addresses network-level access control, not encryption. Option C is for model monitoring, not encryption. Option E is a data preparation tool, not encryption.

Answer

Enable SageMaker Data Wrangler for data preparation.

Answer

Enable SageMaker Model Monitor to detect data drift.

Answer

Use a VPC with security groups to restrict access to the SageMaker endpoint to only known IP addresses.

Question 6

Refer to the exhibit. A data scientist is training a model in SageMaker using a KMS-encrypted dataset. The training job fails with the error shown. Which action should be taken to resolve this issue?

Accepted Answer

Add the SageMaker execution role to the KMS key policy with the kms:Decrypt permission.. The error indicates the SageMaker execution role lacks permission to decrypt the KMS key. The most direct solution is to add the role to the KMS key policy with the kms:Decrypt permission. Option B is unnecessary; Option C changes encryption approach unnecessarily; Option D is also valid but less direct because the key policy must allow the role anyway, and modifying the key policy is the standard practice for SageMaker.

Answer

Create a new KMS key and update the bucket policy to use the new key.

Answer

Attach an IAM policy to the SageMaker execution role that allows kms:Decrypt on the key.

Answer

Disable server-side encryption on the S3 bucket and use client-side encryption.

Question 7

A developer is trying to invoke the Claude v2 model in Amazon Bedrock from a Lambda function. The Lambda function's IAM role has the following policy attached:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": "bedrock:InvokeModel",
      "Resource": "*"
    }
  ]
}

When the Lambda function runs, it receives the error shown in the exhibit. Which additional step is most likely needed to resolve this issue?

Accepted Answer

Request access to the Anthropic Claude model through the Amazon Bedrock console.. In Amazon Bedrock, even with IAM permissions allowing access to all models, you must also request access to specific foundation models through the AWS console. The AccessDeniedException here indicates the model is not enabled for the account. Option A is correct. Option B is incorrect because the policy already allows all models. Option C is incorrect because the region is irrelevant to this error. Option D is incorrect because the API call is correct.

Answer

Change the AWS region to one where Claude v2 is available.

Answer

Use a different model ID such as 'anthropic.claude-v1'.

Answer

Add a condition to the IAM policy to specify the model ARN.

Question 8

A company is deploying a customer service chatbot using a large language model (LLM) via Amazon Bedrock. The application must meet high accuracy for domain-specific queries, low latency, and be cost-effective. Which TWO strategies should the company adopt to achieve these goals? (Choose two.)

Accepted Answer

Store user prompts in a shared cache to reuse common queries.. Retrieval-Augmented Generation (RAG) provides domain-specific context without full fine-tuning, reducing cost and latency. Caching responses for common queries reduces latency. Option A is not necessarily cost-effective; fine-tuning is expensive and may be overkill. Option B is not good practice; it reduces security. Option D is overkill for latency; model choice should be driven by capability, not just throughput.

Answer

Fine-tune the model on a large corpus of customer service transcripts to improve domain knowledge.

Answer

Select a smaller, faster model that trades some accuracy for throughput.

Answer

Increase the model's maximum token limit to handle longer customer queries.

Question 9

Which TWO of the following are key advantages of using Amazon Bedrock for building generative AI applications?

Accepted Answer

Ability to fine-tune models using your own data without managing underlying infrastructure.. Amazon Bedrock provides access to multiple foundation models from different providers via a single API, and it allows you to fine-tune models using your own data without managing infrastructure. Automatic prompt optimization is not a built-in feature; model outputs are not guaranteed to be identical; and data preprocessing is still required.

Answer

Automatic optimization of prompts for all models without user intervention.

Answer

Eliminates the need for any data preprocessing before model invocation.

Answer

Guaranteed identical outputs from all models for the same prompt.

Question 10

Refer to the exhibit. A developer has attached this IAM policy to their user. When trying to invoke the Anthropic Claude v2 model using the Bedrock runtime, they receive an AccessDeniedException. Which change to the policy would resolve the issue?

Accepted Answer

Change the Resource to arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-v2. The policy grants access only to the Titan model resource. To invoke Claude v2, the resource must match the Claude model ARN. Adding other actions or removing the resource condition would not grant the correct access; listing models does not allow invocation.

Answer

Add the bedrock:InvokeModelWithResponseStream action

Answer

Change the Action to bedrock:ListFoundationModels

Answer

Remove the Resource element and set Effect to Deny

Question 11

A retail company is deploying a machine learning model to analyze customer reviews and predict sentiment. The team wants to follow responsible AI guidelines to ensure fairness, transparency, and accountability. Which TWO actions should the team take? (Choose TWO.)

Accepted Answer

Use SageMaker Clarify to evaluate bias in the training data.. Option A: SageMaker Clarify detects bias in training data, which is a core fairness practice. Option C: SageMaker Model Cards document the model's intended use, limitations, and evaluation results, promoting transparency and accountability. Option B: Model Monitor tracks data drift, not directly a responsible AI practice. Option D: Rekognition is for image moderation, not relevant for text sentiment. Option E: Debugger optimizes training, not responsible AI.

Answer

Use SageMaker Debugger to optimize training performance.

Answer

Use SageMaker Model Monitor to automatically retrain the model when drift is detected.

Answer

Use Amazon Rekognition to detect personally identifiable information (PII) in the review text.

Question 12

Refer to the exhibit. A data scientist used SageMaker Clarify to evaluate bias in a binary classification model predicting loan approval. The exhibit shows bias metrics for the female facet. What does the analysis indicate about the model's impact on the female group?

Accepted Answer

The model has a post-training accuracy difference indicating a negative bias against the female group.. The post-training Accuracy Difference (AD) of -0.22 indicates that the model's accuracy for the female group is 22% lower than for the male group, representing a negative bias. Pre-training metrics show some imbalance (CI=0.2) and DPL=-0.15 indicating underrepresentation and lower proportion of positive labels, but the post-training metric directly shows performance disparity. Option A is incorrect because AD is present. Option C is incorrect because AD is negative. Option D is incorrect because the threshold typically is 0.1 for bias detection, and -0.22 exceeds it.

Answer

The metrics are within acceptable thresholds, so no action is needed.

Answer

The model shows a high positive bias toward the female group.

Answer

The model exhibits a pre-training class imbalance but no post-training bias.

Question 13

A multinational corporation uses a foundation model via Amazon Bedrock to translate internal communication documents from English to multiple languages. They notice that the translations often miss company-specific jargon and acronyms, leading to confusion. The company has a glossary of approved translations for terms like 'Project Atlas' and 'Operation Synergy.' They want to improve translation accuracy quickly and with minimal effort. What approach should they take?

Accepted Answer

Use prompt engineering to include the glossary in each translation request.. Option B is correct because including the glossary in the prompt is a simple and effective method: the model can use the provided translations for specific terms. Option A (fine-tuning) requires data preparation and training. Option C (Amazon Translate with custom terminology) is a different service not using the FM. Option D (larger model) may not address specific jargon.

Answer

Use a larger foundation model that has better language understanding.

Answer

Fine-tune the foundation model on a corpus of bilingual company documents.

Answer

Switch to Amazon Translate with custom terminology.

Question 14

A company uses Amazon Bedrock to power a generative chatbot for employee onboarding. Recently, some employees reported that the chatbot occasionally provides responses that contain biased or offensive language. The company has a strict policy for respectful communication. They want to implement a solution quickly without retraining the model. Which action should they take?

Accepted Answer

Enable Amazon Bedrock's built-in content moderation filters.. Option B is correct because Amazon Bedrock's built-in content moderation filters (Guardrails) can be applied immediately to filter biased or offensive content without retraining. Option A (fine-tuning) is time-consuming and requires a dataset. Option C (switch model) may not be quick and still could produce biased outputs. Option D (human reviewer) is slow and not scalable.

Answer

Add a human reviewer to approve every response.

Answer

Use a different foundational model known for unbiased outputs.

Answer

Fine-tune the model on a dataset of polite conversations.

Question 15

A marketing agency uses a foundation model to generate images for social media campaigns. Some generated images have contained violent or inappropriate content, damaging the brand. The agency needs to prevent such content from being displayed automatically. They are using Amazon Bedrock for image generation with Stable Diffusion. What is the most effective way to filter out inappropriate images?

Accepted Answer

Enable the safety checker in Amazon Bedrock's image generation models.. Option B is correct because Stable Diffusion models in Bedrock include a safety checker that can detect and block NSFW content before output. Option A (Amazon Rekognition) introduces additional cost and latency. Option C (manual review) is not scalable. Option D (restrict prompt) is unreliable as the model can still generate inappropriate content from safe prompts.

Answer

Use Amazon Rekognition to analyze images after generation.

Answer

Manually review all images before posting.

Answer

Restrict the prompt to avoid triggering keywords.