AIF-C01 Practice Test 2 — 25 Questions

Question 1

A data scientist is using Amazon SageMaker to train a large language model from scratch. Which AWS service is most suitable for managing the training infrastructure, including automatic scaling and spot instance recovery?

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

Accepted Answer

Amazon SageMaker Training job.. Amazon SageMaker Training jobs are the most suitable service for managing training infrastructure because they provide built-in automatic scaling, managed spot instance recovery, and distributed training orchestration. This allows the data scientist to focus on model development rather than provisioning and managing EC2 instances, load balancers, or recovery scripts.

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AWS Lambda function.

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Amazon SageMaker Notebook instance.

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Amazon EC2 with a custom setup.

Question 2

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.

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Reinforcement Learning from Human Feedback (RLHF)

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Training a new model from scratch on the domain data

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Using prompt engineering to provide context

Question 3

A company wants to extract text and data from scanned PDF invoices for automated processing. Which AWS service is MOST appropriate for this task?

Accepted Answer

Amazon Textract. Amazon Textract is specifically designed to extract text, handwriting, and data from scanned documents like PDF invoices. It uses machine learning to recognize and extract key-value pairs, tables, and form data, making it the most appropriate service for automated invoice processing.

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Amazon Rekognition

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Amazon Transcribe

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Amazon Comprehend

Question 4

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.

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

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Enable model invocation logging to review responses

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Fine-tune the model on the product catalog data

Question 5

An e-commerce company is using a foundation model to generate product descriptions. They want to reduce costs by caching frequently requested descriptions. Which AWS service should they use to implement a cache?

Accepted Answer

Amazon ElastiCache. Amazon ElastiCache is the correct choice because it provides an in-memory caching layer (using Redis or Memcached) that can store frequently requested product descriptions, reducing the need to invoke the foundation model repeatedly. This directly lowers inference costs and latency by serving cached responses instead of generating new ones each time.

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Amazon CloudFront

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Amazon DynamoDB

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Amazon S3

Question 6

A company wants to deploy a real-time inference endpoint for a custom model on SageMaker. The model has high latency (100ms) and they need to handle variable traffic with spikes. Which deployment strategy is most cost-effective?

Accepted Answer

Deploy on a single SageMaker endpoint with automatic scaling. A single SageMaker endpoint with automatic scaling allows the endpoint to dynamically adjust the number of instances based on traffic patterns, handling variable traffic and spikes cost-effectively. For a model with 100ms latency, automatic scaling can add instances during spikes and remove them during low traffic, ensuring you only pay for the compute resources you use while maintaining low inference latency.

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Deploy on a SageMaker multi-model endpoint

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Use batch transform

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Use a single large instance type

Question 7

A healthcare startup is using Amazon Bedrock to generate clinical notes. They must prevent the model from outputting any personally identifiable information (PII) such as patient names. What is the most effective approach?

Accepted Answer

Configure a guardrail in Amazon Bedrock to deny PII topics. Amazon Bedrock Guardrails provide a native, policy-based mechanism to deny the generation of PII topics at inference time, without requiring model retraining or external filtering. This approach directly intercepts and blocks prohibited content before it is returned, offering the most reliable and maintainable solution for compliance with healthcare privacy regulations like HIPAA.

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Fine-tune the model on de-identified data only

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Use a prompt engineering technique to instruct the model to avoid PII

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Post-process the output with a regex filter

Question 8

An AI system is used to detect fraudulent transactions. The system has a high false positive rate for a certain demographic group. To ensure fairness and reduce false positives, which mitigation strategy should be considered?

Accepted Answer

Re-weight the training data to give more importance to samples from the affected group. Re-weighting training samples from the affected group can help the model learn to reduce false positives for that group, addressing the bias.

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Increase the model complexity

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Reduce the decision threshold globally

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Remove the demographic attribute from the model

Question 9

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.

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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 10

A company wants to personalize product recommendations for its e-commerce website. The recommendation engine should adapt to each user's browsing and purchase history in real time. Which AWS service is MOST suitable?

Accepted Answer

Amazon Personalize. Amazon Personalize is the correct choice because it is a fully managed ML service specifically designed to build real-time personalized recommendation systems. It uses the same technology as Amazon's own recommendation engine, processing user-item interaction data (browsing and purchase history) to generate tailored product suggestions with sub-second latency via a real-time inference endpoint.

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Amazon Rekognition

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Amazon Comprehend

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Amazon Forecast

Question 11

Which THREE statements about Amazon SageMaker Ground Truth are correct? (Choose three.)

Accepted Answer

It provides built-in workflows for image classification and object detection.. Amazon SageMaker Ground Truth provides built-in workflows for common tasks like image classification and object detection, which simplifies the setup of labeling jobs. These pre-built templates handle the UI and data formatting, allowing users to focus on the labeling instructions rather than building the labeling interface from scratch.

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It can only be used for text data.

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It can only use a public workforce from Amazon Mechanical Turk.

Question 12

Which of the following is a benefit of using cross-validation during model training?

Accepted Answer

It provides a more reliable estimate of model performance on unseen data. Cross-validation partitions the dataset into multiple folds, training on some and validating on others iteratively. This process yields a more reliable estimate of model performance on unseen data by reducing the variance associated with a single train-test split and ensuring the model is evaluated across different subsets of the data.

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It reduces training time by using all data for training simultaneously

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It eliminates the need for feature engineering

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It guarantees that the model will achieve high accuracy on the test set

Question 13

A developer is using the Amazon Bedrock InvokeModel API with a model that has a context window of 8,000 tokens. The developer sends a prompt that is 7,500 tokens long and expects a response of about 1,000 tokens. The API call fails with an error indicating the input exceeds the model's context window. Why did this happen?

Accepted Answer

The context window includes both input and output tokens, so the total of 7,500 + 1,000 = 8,500 exceeds the 8,000 limit. The context window of a foundation model includes both the input prompt and the generated output tokens. The developer's prompt of 7,500 tokens plus the expected 1,000-token response totals 8,500 tokens, which exceeds the model's 8,000-token limit. The InvokeModel API enforces this combined limit, so even though the prompt alone is under the limit, the total token count causes the error.

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The API automatically reserves 2,000 tokens for output, reducing available input capacity

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The model requires a minimum of 512 tokens for internal processing

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The model's context window counts only input tokens; output tokens are separate

Question 14

An insurance company is using a machine learning model to approve claims. They want to ensure that the model's approval rate is similar across different demographic groups. Which fairness metric would directly measure whether the proportion of positive outcomes is equal across groups?

Accepted Answer

Demographic parity. Demographic parity, also known as statistical parity, requires that the probability of a positive outcome is the same for all groups. Equalized odds focuses on equal true positive and false positive rates, disparate impact measures the ratio of positive outcomes, but demographic parity directly compares proportions.

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Disparate impact ratio

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Equalized odds

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SHAP values

Question 15

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.

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Add a negative prompt specifying what to avoid

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Increase max tokens

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Decrease temperature

Question 16

A multinational corporation uses a foundation model via Amazon Bedrock to translate internal communication documents from English to multiple languages. They notice that the translations often miss company-specific jargon and acronyms, leading to confusion. The company has a glossary of approved translations for terms like 'Project Atlas' and 'Operation Synergy.' They want to improve translation accuracy quickly and with minimal effort. What approach should they take?

Accepted Answer

Use prompt engineering to include the glossary in each translation request.. Prompt engineering allows the company to inject the glossary directly into the context window of the foundation model with each translation request. This approach requires no model retraining or infrastructure changes, enabling rapid improvement by simply appending the approved translations as instructions or few-shot examples. It is the quickest and least effortful method to enforce company-specific terminology without altering the underlying model.

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Use a larger foundation model that has better language understanding.

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Fine-tune the foundation model on a corpus of bilingual company documents.

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Switch to Amazon Translate with custom terminology.

Question 17

A data scientist is preparing data for a classification model. The dataset contains missing values in several features. Which TWO approaches are appropriate for handling missing data? (Select TWO.)

Accepted Answer

Remove rows with any missing values. Imputation fills missing values with estimated values (e.g., mean, median, mode), allowing the model to use all samples. Removing rows with missing values is a simple deletion approach.

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Set missing values to zero

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Ignore missing values during training

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Replace missing values with -1

Question 18

A data scientist is building a binary classifier to predict customer churn. The dataset is highly imbalanced (95% non-churn, 5% churn). Which metric should be prioritized for model evaluation?

Accepted Answer

F1 score. In imbalanced datasets, accuracy can be misleading because a model that always predicts the majority class achieves 95% accuracy but fails to identify churners. The F1 score balances precision and recall, making it more informative for the minority class.

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Root Mean Squared Error (RMSE)

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Mean Absolute Error (MAE)

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Accuracy

Question 19

A company wants to forecast product demand across thousands of SKUs with different demand patterns. They have 3 years of historical sales data, plus external factors like holidays and promotions. Which combination of AWS services and approach would deliver the most accurate forecasts with minimal manual effort?

Accepted Answer

Use Amazon Forecast with the DeepAR+ algorithm and provide item metadata, holiday calendars, and promotion data. Amazon Forecast is purpose-built for time-series forecasting and automatically handles multiple SKUs, holidays, and promotions. SageMaker would require building custom models from scratch. Comprehend is NLP, and QuickSight is for visualization.

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Use Amazon Comprehend to analyze customer reviews and correlate with sales

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Upload data to Amazon QuickSight and use its built-in forecasting widget

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Train individual ARIMA models for each SKU using Amazon SageMaker built-in algorithms

Question 20

A developer is building an agent using Amazon Bedrock Agents to automate a multi-step workflow that involves querying several databases and APIs. The agent needs to handle intermediate results and decide the next step based on previous outputs. Which capability of Bedrock Agents enables this?

Accepted Answer

Multi-step reasoning. Amazon Bedrock Agents uses multi-step reasoning (often powered by the ReAct pattern—Reasoning and Acting) to decompose a complex user request into a sequence of logical steps, invoke the appropriate action groups or knowledge bases at each step, and use the output of one step as input to decide the next. This capability is essential for automating workflows that require intermediate results and dynamic decision-making based on previous outputs.

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Action groups

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Guardrails

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Knowledge bases