AIF-C01 Practice Exam 2 — 20 Questions

Question 1

A company is building a model to predict loan default. They have historical data with 5% default rate. The model must minimize false negatives (missed defaults) because each default costs $50,000. False positives (incorrectly flagged defaults) cost $500 in customer service time. The model currently has a recall of 0.70 and precision of 0.80. Which of the following actions would MOST likely reduce the total cost?

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

Accepted Answer

Increase the model's recall by lowering the classification threshold. The cost of a false negative ($50,000) is 100 times greater than a false positive ($500). Lowering the classification threshold increases recall (reduces false negatives) at the expense of precision (increases false positives). Given the extreme cost asymmetry, the net expected cost will decrease even if many more false positives occur, because each additional true positive saves $50,000 while each extra false positive costs only $500. Option D directly increases recall, which is the correct lever for this cost structure.

Answer

Increase the model's precision by raising the classification threshold

Answer

Use a different algorithm that trades off recall for precision

Answer

Add more features to the model without changing the threshold

Question 2

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 3

A developer invokes an Amazon Bedrock model and receives the above response. What does the 'stopReason' field indicate?

Accepted Answer

The model reached a defined stop sequence.. The 'stopReason' field in an Amazon Bedrock response indicates why the model stopped generating tokens. When set to 'stop', it means the model encountered a defined stop sequence (such as a special token like <|endoftext|> or a user-specified string) and halted generation normally. This is the expected behavior for a successful, complete response.

Answer

The model encountered an error.

Answer

The model hit the maximum token limit.

Answer

The model stopped due to a safety filter.

Question 4

A company wants to use a large language model to generate code based on natural language descriptions. They need to minimize latency and control costs by running inference on their own infrastructure. Which approach is most suitable?

Accepted Answer

Use Amazon SageMaker to deploy a custom LLM. Amazon SageMaker allows you to deploy a custom large language model (LLM) on your own infrastructure, giving you full control over inference latency and cost. By using SageMaker endpoints with auto-scaling and instance selection, you can optimize for low-latency responses while avoiding per-token API charges from managed services.

Answer

Use Amazon Bedrock API

Answer

Use Amazon Comprehend

Answer

Use Amazon Lex

Question 5

A data scientist needs to preprocess categorical data with high cardinality (e.g., zip code with 50,000 unique values). Which technique is most appropriate?

Accepted Answer

Target encoding. Target encoding is the most appropriate technique for high-cardinality categorical features like zip codes because it replaces each category with the mean of the target variable for that category, effectively compressing 50,000 unique values into a single numeric feature while retaining predictive signal. This avoids the dimensionality explosion of one-hot encoding and the arbitrary ordering of label or ordinal encoding, which can mislead models.

Answer

Label encoding

Answer

Ordinal encoding

Answer

One-hot encoding

Question 6

A machine learning engineer notices that a SageMaker training job failed due to insufficient permissions to access a KMS-encrypted S3 bucket. The training job's IAM role has S3 access permissions. What should be done to resolve the issue?

Accepted Answer

Add a kms:Decrypt permission to the IAM role for the specific KMS key used to encrypt the S3 bucket. The training job failed because the IAM role lacks permission to decrypt the KMS key used for S3 server-side encryption. Even with S3 access permissions, SageMaker cannot read encrypted objects without the kms:Decrypt action on the specific KMS key. Adding kms:Decrypt to the role's policy for that key resolves the issue.

Answer

Create a new KMS key and re-encrypt the data

Answer

Assign the SageMakerFullAccess policy to the role

Answer

Change the S3 bucket's default encryption to S3-managed keys (SSE-S3)

Question 7

A company uses Amazon Bedrock to generate marketing content. They want to reduce costs while maintaining response quality. Which action is most effective?

Accepted Answer

Select a smaller foundation model that still meets accuracy requirements.. The most effective cost-reduction strategy because smaller foundation models (FMs) have fewer parameters, resulting in lower compute and inference costs per request. If the smaller model still meets the required accuracy benchmarks for the marketing content task, it directly reduces operational expenditure without sacrificing quality. Amazon Bedrock offers a range of FMs (e.g., from large models like Claude 3 Opus to smaller ones like Claude 3 Haiku), allowing you to match model size to task complexity.

Answer

Fine-tune a larger model to improve accuracy and reduce retries.

Answer

Increase the temperature parameter to get shorter responses.

Answer

Cache previous responses to reuse for similar prompts.

Question 8

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

Answer

Use zero-shot prompting without any examples

Answer

Decrease the temperature to 0.0 to eliminate randomness

Question 9

A data scientist trains a linear regression model to predict housing prices. The model achieves a low training error but a high test error. Which concept does this BEST illustrate?

Accepted Answer

Overfitting. The scenario describes a model that performs well on training data but poorly on unseen test data, which is the classic definition of overfitting. Overfitting occurs when the model learns noise and specific patterns in the training set rather than the underlying generalizable relationship, leading to high variance and poor test performance.

Answer

Bias-variance tradeoff

Answer

Regularization

Answer

Underfitting

Question 10

Which of the following is a benefit of using cross-validation during model training?

Accepted Answer

It provides a more reliable estimate of model performance on unseen data. Cross-validation partitions the dataset into multiple folds, training on some and validating on others iteratively. This process yields a more reliable estimate of model performance on unseen data by reducing the variance associated with a single train-test split and ensuring the model is evaluated across different subsets of the data.

Answer

It reduces training time by using all data for training simultaneously

Answer

It eliminates the need for feature engineering

Answer

It guarantees that the model will achieve high accuracy on the test set

Question 11

A company is using Amazon Bedrock's Converse API to build a conversational agent. They want the agent to maintain context across multiple turns. The agent should also be able to call external APIs to retrieve real-time data when needed. Which combination of features should they use?

Accepted Answer

Use the Converse API with system prompts and tool configuration. The Converse API is designed for multi-turn conversations and natively supports system prompts to set agent behavior and tool configuration for invoking external APIs. Option C correctly combines these features to maintain context across turns and enable real-time data retrieval via tool calls, which is exactly what the scenario requires.

Answer

Use the Converse API with streaming enabled and set max tokens to 4096

Answer

Use the InvokeModel API with a conversational prompt template and tool use configuration

Answer

Use the InvokeModel API with a system prompt and store conversation history client-side

Question 12

Which of the following is a key advantage of using a diffusion model for image generation compared to a GAN?

Accepted Answer

Diffusion models produce more diverse and higher-quality images with stable training. Diffusion models offer a key advantage over GANs because they are trained with a stable, non-adversarial objective—denoising score matching—which avoids the mode collapse and training instability common in GANs. This leads to more diverse outputs and, with sufficient steps, higher-quality images that can rival or exceed GANs, especially in large-scale text-to-image tasks.

Answer

Diffusion models generate images faster than GANs during inference

Answer

Diffusion models are inherently conditional and do not require labels

Answer

Diffusion models require less training data than GANs

Question 13

What is the main purpose of a system prompt in a large language model?

Accepted Answer

To define the high-level instructions that set the model's behavior and persona. The system prompt in a large language model (LLM) defines the high-level instructions that set the model's behavior, persona, and constraints for the entire conversation. Unlike user prompts, which are task-specific, the system prompt establishes the model's role (e.g., 'You are a helpful assistant') and governs how it interprets all subsequent interactions, ensuring consistent and aligned outputs.

Answer

To increase the temperature for more creative responses

Answer

To provide an example of the desired output format

Answer

To list all possible tokens to be used in the response

Question 14

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 15

A developer needs to ensure that a generative AI application on Amazon Bedrock does not produce harmful or inappropriate content. Which feature should they configure?

Accepted Answer

Guardrails for Amazon Bedrock. Guardrails for Amazon Bedrock is the correct feature because it allows developers to define policies that filter and block harmful or inappropriate content in both user inputs and model outputs. This includes configurable thresholds for topics, content filters (e.g., hate, insults, sexual, violence), and denied topics, ensuring the generative AI application adheres to safety and responsible AI requirements.

Answer

Provisioned Throughput

Answer

Model Invocation Logging

Answer

Knowledge Bases for Amazon Bedrock

Question 16

Refer to the exhibit. A developer receives an error when trying to invoke the Claude Instant model from an application. The application uses the IAM role 'MyAppRole'. Which IAM policy statement should be added to the role to resolve the error?

Accepted Answer

{"Effect":"Allow","Action":"bedrock:InvokeModel","Resource":"arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-instant-v1"}. The error occurs when the application tries to invoke the Claude Instant model via the Bedrock InvokeModel API, and the IAM role 'MyAppRole' lacks the necessary permission. The required action is 'bedrock:InvokeModel', and the resource must be scoped to the specific foundation model ARN, which for Claude Instant in us-east-1 is 'arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-instant-v1'. This grants least-privilege access to invoke exactly that model.

Answer

{"Effect":"Allow","Action":"bedrock:GetFoundationModel","Resource":"*"}

Answer

{"Effect":"Allow","Action":"bedrock:InvokeModel","Resource":"arn:aws:bedrock:us-east-1::foundation-model/*"}

Answer

{"Effect":"Allow","Action":"bedrock:*","Resource":"*"}

Question 17

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

Answer

Increase model size to improve accuracy at the expense of interpretability

Answer

Use only synthetic data for training to avoid privacy issues

Question 18

A developer is building a chatbot using Amazon Bedrock and Claude. They notice that the model sometimes generates harmful or biased responses. Which AWS service can they use to implement guardrails?

Accepted Answer

Amazon Bedrock Guardrails. Amazon Bedrock Guardrails is the correct choice because it is a native feature of Amazon Bedrock designed specifically to implement safety controls, content filters, and topic policies for foundation models like Claude. It allows developers to define denied topics, filter harmful content (e.g., hate speech, violence), and redact sensitive information, directly addressing the need to prevent harmful or biased responses in a chatbot built on Bedrock.

Answer

AWS WAF

Answer

Amazon GuardDuty

Answer

AWS Shield

Question 19

A financial services company is deploying a generative AI application using Amazon Bedrock. They need to ensure that the model does not generate responses containing personally identifiable information (PII) such as credit card numbers or Social Security numbers. The company also wants to block certain topics like investment advice. Which feature should they configure?

Accepted Answer

Amazon Bedrock Guardrails. Amazon Bedrock Guardrails is the correct feature because it provides configurable safeguards to filter and block undesirable content in foundation model responses, including PII (e.g., credit card numbers, Social Security numbers) and specific topics like investment advice. It operates at the application layer within Bedrock, allowing you to define denied topics and sensitive information filters that are enforced during inference, without requiring separate infrastructure or manual post-processing.

Answer

AWS WAF

Answer

Amazon Macie

Answer

AWS Identity and Access Management (IAM) policies

Question 20

Which Amazon Titan model is specifically designed to convert text into numerical vectors for use in semantic search and Retrieval-Augmented Generation (RAG)?

Accepted Answer

Amazon Titan Embeddings. Amazon Titan Embeddings is the correct model because it is specifically designed to convert text into numerical vectors (embeddings) that capture semantic meaning. These vectors are essential for semantic search and Retrieval-Augmented Generation (RAG), where they enable similarity comparisons and efficient retrieval of relevant documents from a vector database.

Answer

Amazon Titan Text Express

Answer

Amazon Titan Multimodal Embeddings

Answer

Amazon Titan Image Generator