AIF-C01 Practice Test 2 — 25 Questions

Question 1

A security engineer creates the above IAM policy to allow a user to invoke an Amazon Bedrock model. However, invocation fails. What is the issue?

Accepted Answer

The ARN should use "foundation-model" instead of "model".. Option C is correct because the IAM policy's resource ARN incorrectly uses 'model' in the path, but Amazon Bedrock requires 'foundation-model' to reference foundation models. The correct ARN format for invoking a Bedrock foundation model is 'arn:aws:bedrock:region::foundation-model/model-id'. Using 'model' instead of 'foundation-model' causes the policy to not match any valid Bedrock resource, resulting in an invocation failure.

Answer

The action should be "bedrock:InvokeModelWithResponseStream".

Answer

The resource ARN is missing the account ID.

Answer

The statement is missing a condition for the model ID.

Question 2

A research team is using Amazon Bedrock to analyze scientific papers. They want the model to generate answers based only on papers published after 2023. Which approach should they use?

Accepted Answer

Use Amazon Bedrock Knowledge Bases with a metadata filter to retrieve only papers published after 2023, and generate responses based on retrieved content.. Option D is correct because Amazon Bedrock Knowledge Bases with a metadata filter allows you to restrict retrieval to only documents that match specific metadata criteria, such as publication year. By filtering the vector search to only include papers published after 2023, the model generates responses based solely on that retrieved content, ensuring it does not rely on pre-2023 data. This approach is the only one that guarantees the model's answers are grounded exclusively in the specified time range.

Answer

Fine-tune the model on a dataset of post-2023 papers and deploy it.

Answer

Set the maxTokens to a low value to force the model to rely on recent context.

Answer

Include a system prompt instructing the model to ignore data before 2023.

Question 3

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

Answer

The request is throttled

Answer

The model is out of service

Question 4

A company is using Amazon Bedrock to generate images. They want to ensure the outputs comply with content policies. Which TWO AWS services can help? (Choose two.)

Accepted Answer

Amazon Augmented AI (A2I). Amazon Augmented AI (A2I) is correct because it enables human review of model predictions to ensure compliance with content policies. For image generation on Bedrock, A2I can route outputs that fall below a confidence threshold to human reviewers, providing a safety net for policy adherence.

Answer

Amazon GuardDuty

Answer

AWS WAF

Answer

Amazon Comprehend

Question 5

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 foundation models have a fixed maximum context window (e.g., 4,096 tokens for GPT-3.5). By breaking a long document into smaller chunks and summarizing each independently, you avoid truncation and ensure the model can process the entire content without losing information. This chunking strategy is a standard preprocessing technique for handling documents that exceed the model's context length.

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 6

A company wants to use a foundation model to classify customer feedback into positive, neutral, negative. They have a small labeled dataset. What approach yields best results?

Accepted Answer

Fine-tune a foundation model on their dataset. Option C is correct because fine-tuning a foundation model on a small labeled dataset allows the model to adapt its pre-trained knowledge specifically to the company's sentiment classification task, achieving higher accuracy than zero-shot or generic API approaches. Fine-tuning adjusts the model's weights using the labeled examples, making it sensitive to domain-specific language and nuance in customer feedback, which is critical for a three-class sentiment task.

Answer

Use a pre-built sentiment analysis API

Answer

Fine-tune a foundation model on their dataset

Answer

Use zero-shot classification

Question 7

A data scientist is fine-tuning a foundation model on a custom dataset using Amazon SageMaker. After training, the model shows high accuracy on training data but poor on validation. Which action should be taken?

Accepted Answer

Reduce training epochs or add regularization. The model is overfitting, as indicated by high training accuracy but poor validation performance. Reducing training epochs or adding regularization (e.g., L1/L2 weight decay) directly addresses overfitting by limiting the model's capacity to memorize noise. In Amazon SageMaker, this can be implemented via hyperparameter tuning or by modifying the training script to include regularization terms.

Answer

Add dropout layers

Answer

Increase learning rate

Answer

Use a different foundation model

Question 8

A data scientist uses Amazon Bedrock. The model responses are too long. Which parameter should they adjust to limit the output length?

Accepted Answer

max_tokens. The `max_tokens` parameter directly controls the maximum number of tokens (words or subwords) the model can generate in a single response. By reducing this value, the data scientist caps the output length, preventing overly long responses. Temperature and top_p affect randomness and diversity, not length, while stop sequences define when generation halts but do not enforce a hard token limit.

Answer

temperature

Answer

stop sequences

Answer

top_p

Question 9

A team is fine-tuning a foundation model using SageMaker. They want to minimize training time while keeping the model's original knowledge. Which technique is BEST suited?

Accepted Answer

Use Parameter Efficient Fine-Tuning (PEFT) such as LoRA. Parameter Efficient Fine-Tuning (PEFT) methods like LoRA (Low-Rank Adaptation) are best suited because they freeze the pre-trained model weights and inject trainable low-rank matrices into specific layers, drastically reducing the number of trainable parameters. This minimizes training time and computational cost while preserving the model's original knowledge, as only a small fraction of parameters are updated during fine-tuning.

Answer

Use distributed training across multiple GPUs

Answer

Use prompt engineering instead of fine-tuning

Answer

Full fine-tuning on the new dataset

Question 10

Which TWO of the following are valid methods to reduce the risk of foundation models generating harmful or biased content?

Accepted Answer

Use a content filter. Option B is correct because content filters act as a safety layer that intercepts and blocks harmful or biased outputs before they reach the user. These filters can be rule-based or use a separate classifier model trained to detect toxic, hateful, or biased language, reducing the risk of harmful content generation without altering the underlying model.

Answer

Use a smaller model

Answer

Fine-tune the model on a biased dataset

Answer

Disable all logging

Question 11

A company uses Amazon Bedrock to build a chatbot. The chatbot needs to answer questions based on internal company documents. Which AWS service should be integrated with Bedrock to enable Retrieval Augmented Generation (RAG) without managing infrastructure?

Accepted Answer

Amazon Kendra. Amazon Kendra is a fully managed intelligent search service that can be directly integrated with Amazon Bedrock to implement Retrieval Augmented Generation (RAG) without any infrastructure management. It indexes internal company documents and retrieves relevant passages, which are then passed to the foundation model as context to generate accurate, grounded answers.

Answer

Amazon OpenSearch Service

Answer

Amazon DynamoDB

Answer

Amazon RDS

Question 12

A company is using Amazon Bedrock to generate code snippets. Developers report that the generated code sometimes contains security vulnerabilities. Which action should the team take to mitigate this risk?

Accepted Answer

Add a system prompt that instructs the model to follow security best practices and avoid known vulnerabilities.. Option C is correct because adding a system prompt that instructs the model to follow security best practices and avoid known vulnerabilities directly influences the model's output at inference time. Amazon Bedrock supports system prompts that act as high-level instructions to guide the foundation model's behavior, making this a proactive, scalable mitigation that does not require manual intervention or architectural changes.

Answer

Deploy the model in a sandbox environment to limit its access to sensitive systems.

Answer

Implement a manual code review process after generation.

Answer

Reduce the temperature parameter to 0 to make the output deterministic.

Question 13

Refer to the exhibit. A data scientist created this endpoint config for a foundation model in Amazon SageMaker. However, the endpoint fails to scale under load. What is the most likely reason?

Accepted Answer

Missing AutoScaling configuration. The endpoint fails to scale under load because the endpoint configuration shown lacks an AutoScaling policy. Without AutoScaling, SageMaker will not automatically adjust the number of instances based on traffic, so even if the initial instance count is 1, the endpoint cannot add more instances to handle increased load. AutoScaling must be explicitly configured via Application Auto Scaling to define scaling policies and target tracking metrics.

Answer

Variant weight is 1.0

Answer

Instance type is too small

Answer

InitialInstanceCount is 1

Question 14

A company uses Amazon Bedrock to generate marketing copy. They want to measure the quality of generated text compared to reference text. Which metric is most appropriate?

Accepted Answer

BLEU. BLEU (Bilingual Evaluation Understudy) is the most appropriate metric for evaluating the quality of generated text against reference text in tasks like machine translation and text generation. It measures n-gram precision between the generated and reference texts, making it ideal for assessing marketing copy generated by Amazon Bedrock.

Answer

F1 score

Answer

RMSE

Answer

Accuracy

Question 15

A company is building a chatbot using Amazon Bedrock. They want to ensure the model's responses are grounded in company-specific data and that harmful content is filtered out. Which two services or features should they use? (Choose TWO.)

Accepted Answer

Amazon Kendra. Amazon Kendra is correct because it provides a managed search service that indexes company-specific data sources, enabling the Bedrock chatbot to retrieve relevant documents and ground its responses in authoritative information. Bedrock Guardrails is correct because it allows you to define content filters and topic policies to block harmful or undesirable outputs, ensuring the chatbot adheres to safety and compliance requirements.

Answer

Bedrock Agents

Answer

Amazon Comprehend

Answer

Amazon SageMaker JumpStart

Question 16

Refer to the exhibit. A developer runs this command but gets an error: 'An error occurred (AccessDeniedException) when calling the ListFoundationModels operation'. What is the most likely cause?

Accepted Answer

The IAM role does not have bedrock:ListFoundationModels permission. The error 'AccessDeniedException' when calling ListFoundationModels indicates that the IAM role or user executing the AWS CLI command lacks the required permission to list foundation models in Amazon Bedrock. The specific permission needed is bedrock:ListFoundationModels, which must be attached to the IAM identity via a policy. Without this permission, the API call is denied regardless of other factors like region or CLI version.

Answer

The AWS CLI version is outdated

Answer

The foundation model is not available in us-west-2

Answer

The region us-west-2 does not support Bedrock

Question 17

Refer to the exhibit. A developer deploys this CloudFormation stack but the agent fails to query the knowledge base. What is a likely cause?

Accepted Answer

The agent role does not have permissions to invoke the knowledge base. The correct answer is B because the agent role must have an IAM policy that grants the `bedrock:Retrieve` and `bedrock:RetrieveAndGenerate` permissions on the knowledge base. Without these permissions, the agent cannot invoke the knowledge base, even if the KnowledgeBaseId is correctly passed and the embedding model is available.

Answer

The KnowledgeBaseId is not passed correctly

Answer

The embedding model is not available in the region

Answer

The OpenSearch collection type should be SEARCH not VECTORSEARCH

Question 18

Refer to the exhibit. You receive this response from Amazon Bedrock. What is the most likely cause of the incomplete information?

Accepted Answer

The max_tokens limit was reached. The response from Amazon Bedrock shows an incomplete sentence that cuts off mid-thought, which is a classic symptom of hitting the max_tokens limit. When the generated output reaches the specified maximum number of tokens, the model stops generating immediately, resulting in truncated text. This is the most likely cause because the output is syntactically incomplete but otherwise coherent up to the cutoff point.

Answer

The prompt was too short

Answer

The temperature was too high

Answer

The model lacks knowledge about capitals

Question 19

A data science team is fine-tuning a foundation model on Amazon SageMaker. Which THREE steps are part of the best practice? (Choose three.)

Accepted Answer

Monitor for catastrophic forgetting during fine-tuning.. Option B is correct because catastrophic forgetting is a known risk when fine-tuning foundation models, where the model loses previously learned knowledge while adapting to new data. Monitoring for this during fine-tuning on SageMaker allows the team to detect performance degradation on the original task and adjust training accordingly, ensuring the model retains its general capabilities.

Answer

Increase model size to improve performance.

Answer

Deploy the model to production immediately after fine-tuning.

Question 20

A company uses Amazon Bedrock to generate product descriptions. They notice that the output sometimes contains incorrect information. What should they do to improve accuracy?

Accepted Answer

Implement Retrieval-Augmented Generation (RAG).. Option B is correct because Retrieval-Augmented Generation (RAG) enhances the accuracy of foundation model outputs by grounding the generation in authoritative, up-to-date external knowledge sources. Instead of relying solely on the model's parametric memory, RAG retrieves relevant documents or data from a vector database (e.g., Amazon OpenSearch Serverless) and injects them into the prompt context, reducing hallucinations and incorrect information in product descriptions.

Answer

Increase the temperature parameter.

Answer

Use a larger foundation model.

Answer

Use AWS WAF to filter outputs.

AIF-C01 Practice Test 2 — 25 Questions

Exhibit

AIF-C01 Practice Test 2 — 25 Questions

Exhibit