AIF-C01 Practice Test 9 — 10 Questions

Question 1

A financial services company must comply with regulatory requirements that mandate explainability of credit scoring models. They have deployed a model using SageMaker and need to generate reports showing feature importance for each prediction. Which combination of services should they use to automate this?

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

Accepted Answer

SageMaker Clarify + SageMaker Pipelines. SageMaker Clarify provides built-in explainability capabilities, including feature importance for individual predictions via SHAP values, which directly addresses the regulatory requirement for model explainability. SageMaker Pipelines automates the end-to-end workflow, allowing you to schedule and run Clarify processing jobs to generate reports on a recurring basis without manual intervention.

Answer

SageMaker Model Monitor + Amazon QuickSight

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SageMaker Ground Truth + AWS Lambda

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SageMaker Data Wrangler + SageMaker Studio

Question 2

A company uses Amazon SageMaker to train sensitive ML models. Which AWS service should they use to encrypt the training data and model artifacts at rest?

Accepted Answer

AWS Key Management Service (KMS). AWS Key Management Service (KMS) allows customers to create and manage encryption keys used to encrypt data at rest in SageMaker, including training data and model artifacts.

Answer

AWS Secrets Manager

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AWS CloudHSM

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AWS Certificate Manager

Question 3

A company is building a text classification system using embeddings. They need to choose between Amazon Titan Text Embeddings and Cohere Embed. The documents are in multiple languages, and the team requires strong cross-lingual performance without additional training. Which model is optimized for multilingual use cases?

Accepted Answer

Cohere Embed. Cohere Embed is explicitly optimized for multilingual use cases, supporting over 100 languages with strong cross-lingual performance out of the box. Amazon Titan Text Embeddings, while powerful for English-centric tasks, does not offer the same level of native multilingual optimization. Therefore, for a system requiring robust cross-lingual performance without additional training, Cohere Embed is the correct choice.

Answer

Amazon Titan Text Embeddings

Answer

Neither model supports multilingual text

Answer

Both are equally multilingual

Question 4

A company uses a generative AI model to create marketing copy. They want to ensure that customers know the content is AI-generated. Which practice directly addresses this transparency requirement?

Accepted Answer

Add a watermark or disclaimer stating 'This content was generated by AI'. Transparency in AI-generated content involves clearly disclosing to users that the content was produced by AI. This can be done through disclaimers, labeling, or other communication methods.

Answer

Implement AWS CloudTrail to log all model inference calls

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Store all generated content in Amazon S3 with versioning enabled

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Use a more powerful model to generate more natural-sounding text

Question 5

After deploying a model, a company notices that the distribution of the input features has shifted compared to the training data. Which feature of Amazon SageMaker Model Monitor can alert them to this change?

Accepted Answer

Data quality monitoring. Amazon SageMaker Model Monitor's data quality monitoring feature is specifically designed to detect changes in the distribution of input features compared to the training data. It uses statistical tests (e.g., Kolmogorov-Smirnov, Chi-squared) to compare baseline and live data distributions, alerting when drift is detected. This directly addresses the scenario of input feature distribution shift.

Answer

Model quality monitoring

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Bias drift monitoring

Answer

Feature importance drift

Question 6

Which Amazon Bedrock feature allows you to invoke a model and receive the response token by token as it is generated, reducing perceived latency for the end user?

Accepted Answer

Streaming responses. Streaming responses in Amazon Bedrock allow the model to send back partial results token by token as they are generated, rather than waiting for the entire response to be complete. This reduces perceived latency for end users by enabling them to see the output incrementally, which is especially important for real-time applications like chatbots or interactive assistants.

Answer

Model invocation API

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Provisioned throughput

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Batch inference

Question 7

A media company uses a foundation model on Amazon Bedrock to generate article summaries. The model occasionally omits important details. Which prompt engineering technique is most likely to improve completeness?

Accepted Answer

Include a list of required key points in the prompt. Explicitly listing required key points in the prompt guides the foundation model to cover all specified elements, directly addressing the omission issue. This technique, often called 'constrained generation' or 'structured prompting,' forces the model to attend to each required detail, improving completeness without altering model parameters.

Answer

Use a lower temperature setting

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Increase the max tokens limit

Answer

Add 'Be concise' to the prompt

Question 8

A company uses Amazon SageMaker Clarify to monitor a deployed model for bias. After running an analysis, they find that the model's predictions have a disparate impact on a protected group. What is the MOST appropriate next step?

Accepted Answer

Investigate the root cause of bias, then use techniques such as reweighing training data or applying bias mitigation algorithms before redeploying. Discovering bias requires investigation and mitigation. SageMaker Clarify can help identify bias, but mitigation typically involves retraining with balanced data or adjusting model outputs.

Answer

Modify the SageMaker endpoint to add a random noise to predictions for the protected group

Answer

Ignore the results because SageMaker Clarify is still in preview

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Immediately delete the model and retrain from scratch using only data from the protected group

Question 9

A company wants to track API calls made to Amazon SageMaker for audit purposes. Which AWS service should they enable?

Accepted Answer

AWS CloudTrail. AWS CloudTrail is the correct service because it records API activity across AWS services, including Amazon SageMaker. By enabling CloudTrail, the company can capture all SageMaker API calls (e.g., CreateModel, InvokeEndpoint) for audit, compliance, and security analysis. CloudTrail logs provide details such as the identity of the caller, the time of the call, and the request parameters, which are essential for auditing.

Answer

Amazon Macie

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AWS Config

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Amazon CloudWatch Logs

Question 10

A company has trained a model using Amazon SageMaker and stored the model artifacts in S3 with SSE-KMS encryption. The development team wants to grant cross-account access to the model artifacts so a partner can deploy the model in their own account. Which steps are required?

Accepted Answer

Create an S3 bucket policy allowing the partner account to read the objects, and ensure the KMS key policy allows the partner account to use the key. To share SSE-KMS encrypted objects cross-account, you must grant the partner account access to both the S3 object (via bucket policy) and the KMS key (via key policy). The partner must also have the correct IAM permissions.

Answer

Create a new IAM role in the partner account with S3 read permissions

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Use AWS Lake Formation to share the data

Answer

Copy the model artifacts to a public S3 bucket