AIF-C01 Practice Test 4 — 25 Questions

Question 1

A developer is using Amazon Bedrock to build a chatbot that answers customer queries. The chatbot must only respond based on the provided company documentation. Which approach best meets this requirement?

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

Accepted Answer

Use a RAG architecture with the company documentation as the knowledge base.. Retrieval-Augmented Generation (RAG) architecture retrieves relevant chunks from the company documentation at query time and injects them into the prompt, ensuring the model's response is grounded solely in the provided documents. This approach prevents the model from relying on its internal training data or generating information outside the documentation, which is critical for a closed-domain chatbot.

Answer

Use prompt engineering to instruct the model to only use documentation.

Answer

Fine-tune a foundation model on the company documentation.

Answer

Use a text classification model to filter responses.

Question 2

A company operates a customer service platform that uses Amazon Bedrock with a foundation model to generate automated responses. The system has been in production for three months. Recently, customers have reported that responses are becoming repetitive and less relevant over time. The development team notices that the model's performance has degraded, especially for queries about newer products that were added after the initial deployment. The team currently uses a static prompt with a fixed knowledge base that was set up at launch. The model is invoked via the Bedrock API with standard settings. The team wants to improve response quality without incurring high costs or extensive re-engineering. What should the team do?

Accepted Answer

Implement a feedback loop to periodically update the knowledge base with new product information and use a dynamic prompt that includes recent interactions.. The core issue is that the static knowledge base and prompt have become stale as new products were added. Implementing a feedback loop to periodically update the knowledge base with new product information and using a dynamic prompt that includes recent interactions directly addresses the root cause of degradation—outdated context—without requiring costly fine-tuning or model swaps. This approach leverages Amazon Bedrock's native capabilities for retrieval-augmented generation (RAG) to keep responses relevant and non-repetitive.

Answer

Increase the temperature parameter to 0.9 to introduce more randomness and reduce repetition.

Answer

Fine-tune the model every week on the latest customer interactions using Amazon SageMaker.

Answer

Switch to a larger foundation model to handle the increased complexity of new products.

Question 3

A company is using Amazon Comprehend to analyze customer feedback. They need to ensure that the documents are encrypted at rest. What should they do?

Accepted Answer

No action is needed; Amazon Comprehend automatically encrypts data at rest using AES-256. Amazon Comprehend automatically encrypts all data at rest using AES-256 encryption by default, with no additional configuration required. This encryption covers both the documents processed by the service and any models or artifacts stored internally. Therefore, no action is needed from the customer to enable encryption at rest.

Answer

Enable encryption using AWS KMS in the Comprehend console

Answer

Store documents in an encrypted S3 bucket and use a VPC endpoint

Answer

Use SSL/TLS for all API calls to Comprehend

Question 4

During a security review, it is found that an Amazon SageMaker notebook instance has outbound internet access, which could lead to data exfiltration. The notebook must only access resources within the VPC. Which step should be taken to restrict internet access?

Accepted Answer

Configure the notebook instance in a VPC with no internet gateway or NAT device, and set the notebook's 'Direct Internet Access' option to 'Disabled'. Disabling 'Direct Internet Access' on a SageMaker notebook instance and placing it in a VPC without an internet gateway or NAT device ensures the notebook cannot reach the public internet. This configuration forces all traffic to stay within the VPC, preventing data exfiltration via outbound internet connections while still allowing access to VPC resources.

Answer

Modify the notebook instance's IAM role to deny s3:GetObject

Answer

Attach a security group that denies all outbound traffic to 0.0.0.0/0

Answer

Disable the SageMaker notebook instance's root volume encryption

Question 5

A financial services company uses Amazon Bedrock to power a customer-facing chatbot that provides investment advice. The company must ensure that the chatbot's responses comply with regulatory standards, meaning that the model should not generate advice that is speculative or promises returns. The company has implemented Bedrock Guardrails with content filters. However, during testing, the chatbot still generates responses that violate the guidelines. A review of the guardrail configuration shows that the content filters are set to the lowest sensitivity. The company wants to enforce stricter filtering without completely blocking legitimate responses. What should the company do?

Accepted Answer

Increase the sensitivity of the content filters in the Bedrock Guardrails configuration.. Increasing the sensitivity of the content filters in Bedrock Guardrails directly addresses the issue: the current filters are set to the lowest sensitivity, allowing speculative or promise-based responses to pass through. By raising the sensitivity, the guardrails will block more non-compliant content while still permitting legitimate investment advice, striking the required balance between regulatory compliance and functionality.

Answer

Use a different foundational model that has built-in compliance filters.

Answer

Configure the chatbot to route all responses to a human reviewer before delivering to the customer.

Answer

Add a deny topic for investment advice to completely block that topic.

Question 6

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?

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

Answer

SageMaker Ground Truth + AWS Lambda

Answer

SageMaker Data Wrangler + SageMaker Studio

Question 7

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

Answer

AWS CloudHSM

Answer

AWS Certificate Manager

Question 8

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 9

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

Answer

Store all generated content in Amazon S3 with versioning enabled

Answer

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

Question 10

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

Answer

Bias drift monitoring

Answer

Feature importance drift

Question 11

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

Answer

Provisioned throughput

Answer

Batch inference

Question 12

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

Answer

Increase the max tokens limit

Answer

Add 'Be concise' to the prompt

Question 13

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

Answer

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

Question 14

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

Answer

AWS Config

Answer

Amazon CloudWatch Logs

Question 15

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

Answer

Use AWS Lake Formation to share the data

Answer

Copy the model artifacts to a public S3 bucket

Question 16

A data scientist needs to grant an IAM user access to a specific Amazon SageMaker notebook instance. The user should only be able to start and stop the notebook instance, but not delete it. Which IAM policy statement should be used?

Accepted Answer

{"Effect":"Allow","Action":["sagemaker:StartNotebookInstance","sagemaker:StopNotebookInstance"],"Resource":"arn:aws:sagemaker:us-east-1:123456789012:notebook-instance/MyNotebook"}. It uses the specific actions `sagemaker:StartNotebookInstance` and `sagemaker:StopNotebookInstance` with a resource ARN that targets only the intended notebook instance. This grants the least privilege required to start and stop the instance while explicitly preventing deletion, as no delete action is included. The resource ARN restricts the policy to a single notebook instance, ensuring the user cannot affect other resources.

Answer

{"Effect":"Allow","Action":["sagemaker:Start*","sagemaker:Stop*"],"Resource":"*"}

Answer

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

Answer

{"Effect":"Allow","Action":"sagemaker:*","Resource":"arn:aws:sagemaker:us-east-1:123456789012:notebook-instance/MyNotebook"}

Question 17

A financial services firm must extract text from scanned loan application forms to automate data entry. The forms are in various languages. Which combination of AWS AI services should be used?

Accepted Answer

Amazon Textract and Amazon Translate. Amazon Textract is designed to extract text and data from scanned documents, including forms, using OCR (Optical Character Recognition). Amazon Translate then converts the extracted text from various languages into a target language, enabling automated data entry across multilingual loan applications. Together, they directly address the requirement of extracting text from scanned forms in multiple languages.

Answer

Amazon Transcribe and Amazon Polly

Answer

Amazon Kendra and Amazon Lex

Answer

Amazon Rekognition and Amazon Comprehend

Question 18

A data scientist is using SageMaker to train a model on a dataset with many features. They suspect some features are redundant. Which feature engineering technique would help?

Accepted Answer

Principal Component Analysis (PCA). Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms the original correlated features into a smaller set of uncorrelated principal components, effectively removing redundancy while preserving most of the variance in the data. In SageMaker, PCA can be applied via the built-in PCA algorithm or as a preprocessing step in a scikit-learn container to reduce feature space and eliminate multicollinearity.

Answer

Feature scaling

Answer

One-hot encoding

Answer

Polynomial features

Question 19

A company is deploying a generative AI application that creates marketing copy. They want to ensure the outputs do not include harmful or inappropriate content. Which AWS service can enforce content policies and filter undesirable outputs?

Accepted Answer

Amazon Bedrock Guardrails. Amazon Bedrock Guardrails lets you define content filters, denied topics, word filters, and sensitive information filters that are applied to both inputs and outputs of foundation models. It is purpose-built to enforce responsible AI policies and block harmful or inappropriate content in generative AI applications. This directly addresses the requirement to filter undesirable outputs from a marketing copy generator.

Answer

AWS WAF

Answer

Amazon Comprehend

Answer

AWS Shield

Question 20

A data scientist is training a model using Amazon SageMaker and notices the training loss is decreasing but validation loss starts increasing after a few epochs. Which technique should they apply to address this?

Accepted Answer

Add regularization (e.g., L1 or L2). The scenario describes overfitting, where the model memorizes training data but fails to generalize to validation data. Adding regularization (L1 or L2) penalizes large weights, reducing model complexity and improving generalization. This is a standard technique in SageMaker training jobs, often configured via the `regularizer` hyperparameter in frameworks like TensorFlow or MXNet.

Answer

Increase batch size

Answer

Increase the learning rate

Answer

Add more training data