AIF-C01 Practice Test 47 — 15 Questions

Question 1

A data scientist needs to restrict access to a SageMaker notebook instance to only the corporate network. Which configuration should they use?

Accepted Answer

Place the notebook instance in a VPC and configure security groups to allow only corporate IP ranges. To restrict access to a SageMaker notebook instance to only the corporate network, the best approach is to place the notebook instance in a VPC and configure security groups to allow inbound traffic only from corporate IP ranges. This network-level control ensures that only traffic from specified IPs can reach the notebook.

Answer

Enable multi-factor authentication for the notebook

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Use an IAM policy to allow only corporate users

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Use a SageMaker lifecycle configuration to block external IPs

Question 2

A developer is building a chatbot that answers questions from a company's internal knowledge base. The knowledge base is updated frequently, and the chatbot must always provide the most current information without retraining the model. Which AWS service or feature is MOST suitable for this requirement?

Accepted Answer

Amazon Bedrock Knowledge Bases. Amazon Bedrock Knowledge Bases is the correct choice because it enables Retrieval-Augmented Generation (RAG), which allows the chatbot to query the latest data from a vector store or external knowledge base without retraining the model. When the knowledge base is updated, the embeddings are refreshed, and the foundation model retrieves the most current information at inference time, ensuring answers remain up-to-date.

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Amazon Bedrock Agents

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Amazon Bedrock Guardrails

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Fine-tuning a foundation model on the knowledge base

Question 3

A developer uses Amazon Bedrock to generate code. Some outputs contain syntax errors. What is the most likely cause?

Accepted Answer

The prompt lacks constraints or examples. Syntax errors in generated code typically arise when the prompt lacks sufficient constraints or examples to guide the model toward producing syntactically valid output. Amazon Bedrock's foundation models rely on clear instructions and few-shot examples to adhere to language syntax rules; without them, the model may generate plausible-looking but incorrect code.

Answer

The max_tokens is too low

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The temperature is too high

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The model lacks knowledge of the language

Question 4

A company wants to use a pre-trained generative AI model to analyze customer feedback. They need to adjust the model for their specific domain without retraining from scratch. Which approach is MOST suitable?

Accepted Answer

Fine-tuning the model on domain-specific data. Fine-tuning is the most suitable approach because it takes a pre-trained generative AI model and updates its weights using a smaller, domain-specific dataset (e.g., customer feedback transcripts). This allows the model to adapt to the company's specific terminology, sentiment patterns, and context without the massive computational cost and data requirements of training from scratch. It preserves the general language understanding from pre-training while specializing the model for the target domain.

Answer

Reinforcement Learning from Human Feedback (RLHF)

Answer

Training a new model from scratch on the domain data

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Using prompt engineering to provide context

Question 5

A startup wants to generate product descriptions from a few keywords using a foundation model. They need a fully managed serverless solution that requires no infrastructure setup. Which AWS service should they use?

Accepted Answer

Amazon Bedrock. Amazon Bedrock is a fully managed serverless service that provides access to foundation models (FMs) from leading AI providers via a simple API, making it ideal for generating product descriptions from keywords without any infrastructure management. It directly supports generative AI tasks like text generation, unlike other AWS services that focus on different ML or NLP capabilities.

Answer

Amazon SageMaker

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Amazon Comprehend

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AWS Lambda

Question 6

A company is building a chatbot using Amazon Bedrock to answer customer questions about their product catalog. The chatbot should only use information from the company's internal knowledge base and should not generate answers based on the model's pre-training data. Which feature should be enabled?

Accepted Answer

Configure a knowledge base with Retrieval Augmented Generation (RAG). Configuring a knowledge base with Retrieval Augmented Generation (RAG) allows the chatbot to retrieve relevant documents from the company's internal knowledge base and use them as context for generating answers. This ensures the model's responses are grounded solely in the provided data, preventing reliance on its pre-training knowledge.

Answer

Use prompt engineering to instruct the model to only use the knowledge base

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Enable model invocation logging to review responses

Answer

Fine-tune the model on the product catalog data

Question 7

A healthcare organization is developing a clinical decision support system using Amazon Bedrock with a large language model (LLM) to analyze patient symptoms and suggest potential diagnoses. The system must comply with HIPAA and internal responsible AI guidelines. During testing, the model occasionally generates diagnoses that are inconsistent with established medical guidelines and shows a tendency to recommend more aggressive treatments for patients from certain demographic groups. The team has already implemented data encryption, access controls, and basic content filtering. They need to further reduce biased and unsafe outputs without delaying the deployment timeline. What should the team do next?

Accepted Answer

Fine-tune the model using a curated dataset of anonymized patient records that is balanced across demographic groups and aligned with clinical guidelines.. Fine-tuning the model with a balanced, curated dataset directly addresses both the bias and clinical accuracy issues at the model level, which is the most effective approach for reducing biased and unsafe outputs without delaying deployment. This method adjusts the model's internal weights to align with established medical guidelines and demographic fairness, rather than relying on post-processing filters or logging that do not fix the root cause. Since the team has already implemented basic content filtering, fine-tuning provides a targeted, efficient solution that can be completed within a reasonable timeline.

Answer

Increase the logging of all model inputs and outputs to Amazon CloudWatch and set up alarms for any mentions of protected attributes.

Answer

Replace the current LLM with a different pre-trained model that has been benchmarked for lower bias on medical datasets.

Answer

Apply stronger content filtering rules using Amazon Comprehend Medical to block any diagnosis that contains demographic-related terms.

Question 8

A startup wants to build a product recommendation engine for their e-commerce platform. They have user purchase history and item metadata. They want a fully managed solution that can automatically train and deploy a recommendation model without needing to manage the underlying ML lifecycle. The solution should provide personalized recommendations based on collaborative filtering. Which AWS service should they use?

Accepted Answer

Use Amazon Personalize. Amazon Personalize is a fully managed service that enables you to build and deploy recommendation models without managing the underlying ML lifecycle. It supports collaborative filtering out of the box, using user purchase history and item metadata to generate personalized recommendations, which directly matches the startup's requirements.

Answer

Use Amazon Kendra

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Use Amazon Lex

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Use Amazon SageMaker built-in Factorization Machines algorithm

Question 9

A company is using Amazon Comprehend for sentiment analysis on customer reviews. They notice that the sentiment is often incorrect for negative reviews with sarcasm. What is the likely cause?

Accepted Answer

The pre-trained model cannot handle sarcasm well. Amazon Comprehend's pre-trained sentiment analysis models are trained on general text corpora and lack the ability to detect sarcasm, which relies on contextual cues, tone, and figurative language. Sarcasm often inverts the literal sentiment (e.g., 'Great job, as always' for a failure), and standard NLP models without explicit sarcasm detection or fine-tuning cannot reliably interpret this inversion. Therefore, the likely cause is that the pre-trained model cannot handle sarcasm well.

Answer

The model is not fine-tuned for the domain

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Insufficient training data

Answer

The input text is too long

Question 10

A company wants to automatically detect anomalies in their AWS CloudTrail logs to identify potential security threats. Which AWS service is specifically designed for this purpose?

Accepted Answer

Amazon GuardDuty. Amazon GuardDuty is a threat detection service that continuously monitors AWS accounts and workloads using machine learning, anomaly detection, and integrated threat intelligence. It specifically analyzes CloudTrail management and data events, VPC Flow Logs, and DNS logs to identify unauthorized behavior or potential security threats, making it the correct choice for automatically detecting anomalies in CloudTrail logs.

Answer

Amazon Macie

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AWS Config

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Amazon Inspector

Question 11

An e-commerce company uses Amazon Bedrock to generate product descriptions from keywords. Some descriptions contain inaccurate details about product specifications. Which approach should the company take to reduce factual errors?

Accepted Answer

Include the product specifications in the prompt and instruct the model to base the description on the provided data.. Providing the product specifications directly in the prompt and instructing the model to base the description on that data grounds the generation in factual information, reducing hallucinations. This technique, known as prompt engineering with in-context learning, ensures the model uses the given data rather than relying on its training data, which may contain inaccuracies.

Answer

Increase the maxTokens parameter to allow more detailed descriptions.

Answer

Use a different foundation model from Bedrock for each product category.

Answer

Deploy the model to a SageMaker endpoint and use human-in-the-loop validation.

Question 12

A company uses Amazon SageMaker to train a model. The training job fails with 'InsufficientInstanceCapacity' error. What is the most likely cause?

Accepted Answer

The requested instance type is not available in the specified region.. The 'InsufficientInstanceCapacity' error in Amazon SageMaker indicates that AWS does not currently have enough available capacity for the requested instance type in the specified region or Availability Zone. This is a common transient error when demand for a particular instance type exceeds supply, and it is not related to request rate, dataset size, or image compatibility.

Answer

The request rate is too high.

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The dataset size exceeds the instance storage limit.

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The training image is not compatible with the instance type.

Question 13

A developer is using Amazon Bedrock to generate text summaries. The output sometimes includes irrelevant information. What is the most effective prompt engineering technique to improve relevance?

Accepted Answer

Use few-shot examples with summaries. Few-shot examples provide the model with explicit patterns of desired output, directly guiding it to produce summaries that match the format and content of the examples. This technique is the most effective for improving relevance because it gives the model concrete reference points, reducing the likelihood of including irrelevant information.

Answer

Add a negative prompt specifying what to avoid

Answer

Increase max tokens

Answer

Decrease temperature

Question 14

A company is using Amazon Rekognition to detect objects in images. They find that the service sometimes mislabels objects. What is the best way to improve accuracy for their specific use case?

Accepted Answer

Use Amazon SageMaker to build a custom model. Amazon Rekognition is a pre-trained service that may not perform optimally for specialized or domain-specific use cases. By using Amazon SageMaker to build a custom model, you can train a model on your own labeled dataset, which directly addresses the mislabeling issue by tailoring the model to your specific images and objects.

Answer

Use a larger image size

Answer

Contact AWS support

Answer

Increase the confidence threshold

Question 15

A company wants to predict customer churn. They have historical data with features like usage minutes, support tickets, contract length. The target is binary: churn/not churn. Which ML algorithm is best suited?

Accepted Answer

Logistic regression. Logistic regression is the best choice because it is specifically designed for binary classification tasks like predicting churn (churn/not churn). It models the probability of the target class using a logistic (sigmoid) function, making it interpretable and efficient for this type of supervised learning problem with a categorical outcome.

Answer

Principal Component Analysis (PCA)

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Linear regression

Answer

K-means clustering