AIF-C01 Practice Test 48 — 15 Questions

Question 1

A developer receives the above response from invoking a Bedrock model. Which field indicates that the model completed its response normally?

Accepted Answer

stop_reason. The `stop_reason` field in the Bedrock response indicates why the model stopped generating text. A value of `"stop"` or `"end_turn"` (depending on the model) signals that the model completed its response normally, as opposed to hitting a token limit, content filter, or other interruption.

Answer

output

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text

Answer

role

Question 2

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".. 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".

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The resource ARN is missing the account ID.

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The statement is missing a condition for the model ID.

Question 3

A company fine-tunes a foundation model on SageMaker using a custom dataset. They notice the training job takes too long. Which optimization technique is specifically designed to reduce training time for foundation models?

Accepted Answer

Distributed training using SageMaker Data Parallelism. SageMaker Data Parallelism distributes the training workload across multiple GPUs or instances, splitting the data and synchronizing gradients using optimized all-reduce algorithms. This specifically reduces training time for large foundation models by enabling parallel computation, which is the most direct technique for accelerating training at scale.

Answer

Using a smaller instance type

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Using Spot Instances

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Reducing batch size

Question 4

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.

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The prompt was too short

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The temperature was too high

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The model lacks knowledge about capitals

Question 5

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

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

Answer

The region us-west-2 does not support Bedrock

Question 6

A developer is using Amazon Bedrock's Claude model to summarize long documents. The developer notices that the summaries sometimes miss key points. Which parameter adjustment is most likely to improve summary completeness?

Accepted Answer

Increase the max_tokens parameter.. Increasing max_tokens allows the model to generate longer outputs, which is essential when summarizing long documents because the summary may need more tokens to capture all key points. If max_tokens is too low, the model truncates the response, potentially omitting important details. This directly addresses the issue of missing key points by providing sufficient output length for a complete summary.

Answer

Increase the top_k parameter.

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Increase the temperature parameter.

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Increase the top_p parameter.

Question 7

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.. 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.

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

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Implement a manual code review process after generation.

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

Question 8

A company is using Amazon Bedrock to generate code snippets. They want to ensure the generated code is secure. Which TWO practices should they implement?

Accepted Answer

Use guardrails to block insecure code patterns.. Option B is correct because Amazon Bedrock Guardrails let you define denied topics, content filters, and (critically for this scenario) sensitive-information and custom word/regex filters that can detect and block insecure code patterns such as hardcoded credentials or known dangerous constructs before the response is returned. Option D is correct because no generative model guarantees secure output; generated code must be treated as untrusted and go through human review plus SAST/DAST and unit testing in the CI/CD pipeline before deployment, which is the standard secure-SDLC control for AI-generated artifacts. Option A does not belong because raising the max token limit only affects output length, not security posture. Option C does not belong because temperature 0 makes sampling more deterministic but does not make code secure. Option E does not belong because a larger model may improve accuracy or fluency, but accuracy is not a security control and does not prevent insecure code.

Answer

Increase the max token limit to generate longer code.

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Set the temperature to 0 for deterministic output.

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Use a larger model for better accuracy.

Question 9

A financial services company is deploying a foundation model to analyze customer sentiment from call transcripts. The model outputs must be consistent and deterministic for auditing purposes. Which parameter configuration should the company use?

Accepted Answer

Set temperature to 0 and top_p to 1.. Setting temperature to 0 and top_p to 1 forces the model to always select the highest-probability token at each step, producing deterministic and repeatable outputs. This is essential for auditing and compliance in financial services, where consistency is required. Any nonzero temperature introduces randomness, which undermines determinism.

Answer

Set temperature to 0.1 and top_p to 0.9.

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Set temperature to 0.7 and top_p to 1.0.

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Set temperature to 0.5 and top_p to 0.5.

Question 10

A data scientist is using Amazon Bedrock to build a question-answering system over a large corpus of technical manuals. They want to ensure that the model's answers are grounded in the retrieved documents and that the model does not hallucinate. Which feature should they enable?

Accepted Answer

Bedrock Guardrails with grounding support. Bedrock Knowledge Bases provides source attribution, and when combined with model inference, the model can be instructed to answer only from the retrieved chunks. However, Bedrock Guardrails' grounding check specifically verifies that the model's response is supported by the retrieved context, reducing hallucination.

Answer

Embedding model with higher dimensionality

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Larger chunk sizes in the knowledge base

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Bedrock Agents with a multi-step reasoning prompt

Question 11

A company wants to document their machine learning model's intended use, limitations, and ethical considerations. Which TWO practices should they adopt? (Choose two.)

Accepted Answer

Create a model card that describes the model's purpose, performance, and fairness metrics. Option A is correct because a model card is the standard artifact for documenting a model's intended use, limitations, performance, and fairness/ethical considerations, directly satisfying the requirement to document purpose and ethics. Option C is correct because Amazon SageMaker Model Cards is the AWS service purpose-built to create, version, and share this structured model documentation (including intended use, risk ratings, and evaluation results) with stakeholders. Option B is incorrect because weekly automatic retraining is an MLOps maintenance activity that improves freshness but does not document intended use, limitations, or ethics. Option D is incorrect because CloudWatch alarms monitor operational metrics such as accuracy drift or latency and provide no documentation of purpose or ethical considerations. Option E is incorrect because A/B testing compares model versions for performance or business impact and does not produce the required documentation.

Answer

Automatically retrain the model weekly

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Set up Amazon CloudWatch alarms to monitor model accuracy

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Conduct A/B testing between different model versions

Question 12

A data science team uses Amazon SageMaker to train models. To comply with SOC 2, they must ensure that access to training data is logged, that the data is encrypted at rest, and that model training jobs are isolated from each other. Which THREE actions should they take? (Choose three.)

Accepted Answer

Enable server-side encryption on the S3 bucket containing training data using SSE-KMS.. Enabling server-side encryption on the S3 bucket containing training data using SSE-KMS ensures data at rest is encrypted, which is a direct requirement for SOC 2 compliance. SSE-KMS provides envelope encryption with a customer-managed AWS KMS key, allowing fine-grained access control and audit trails for the encryption keys.

Answer

Enable Amazon Inspector to scan training instances for vulnerabilities.

Answer

Use SageMaker Debugger to monitor training jobs.

Question 13

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.. Amazon Bedrock requires explicit user access approval for third-party foundation models like Anthropic Claude before they can be invoked. Even with a valid IAM policy allowing bedrock:InvokeModel on all resources, the model itself must be granted access via the Bedrock console's 'Model access' section. Without this step, the API returns an access denied error regardless of IAM permissions.

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 14

An organization is using Amazon Bedrock to power a customer service chatbot. They notice that the chatbot occasionally generates hallucinated information about product specifications. Which strategy should be implemented to reduce hallucinations?

Accepted Answer

Integrate a Retrieval Augmented Generation (RAG) system with the product catalog.. Retrieval Augmented Generation (RAG) grounds the model's responses in authoritative, up-to-date product catalog data, directly reducing hallucinations by ensuring the chatbot references verified facts rather than relying solely on its parametric memory. This is the most effective strategy because it provides a retrieval-based factual foundation that fine-tuning or prompt engineering alone cannot guarantee.

Answer

Fine-tune the model on a dataset of product specification conversations.

Answer

Use more detailed prompts with explicit instructions to avoid speculation.

Answer

Increase the temperature parameter to make outputs more conservative.

Question 15

A company uses Amazon Bedrock to deploy a foundation model for a real-time chat application. Users report that responses are slow. Which optimization is MOST likely to reduce latency without degrading quality?

Accepted Answer

Enable response streaming using the Converse API or InvokeModelWithResponseStream. Enabling response streaming with the Converse API or InvokeModelWithResponseStream allows the model to send tokens to the client as they are generated, rather than waiting for the full response. This reduces the user's perceived latency because the first token appears much sooner, even though the total generation time remains similar. This optimization directly addresses the real-time chat requirement without degrading output quality.

Answer

Use a larger model variant to improve inference speed

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Increase the temperature to make the model generate faster

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Switch from a text generation model to an embedding model