Practice Free AIF-C01 Exam Online Questions – Page 4

Question #31

A financial company stores patterns of fraudulent behavior in a database. The company uses this data to conduct investigations.

The company wants to use a graph-based ML solution to develop an AI tool that helps with these investigations.

Which AWS service will meet these requirements?

A . Amazon OpenSearch Service
B . Amazon Aurora
C . Amazon Neptune
D . Amazon MemoryDB

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Question #32

A large retail bank wants to develop an ML system to help the risk management team decide on loan allocations for different demographics.

What must the bank do to develop an unbiased ML model?

A . Reduce the size of the training dataset.
B . Ensure that the ML model predictions are consistent with historical results.
C . Create a different ML model for each demographic group.
D . Measure class imbalance on the training dataset. Adapt the training process accordingly.

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Question #33

A financial company is developing a generative AI application for loan approval decisions. The company needs the application output to be responsible and fair.

Which solution meets these requirements?

A . Review the training data to check for biases. Include data from all demographics in the training data.
B . Use a deep learning model with many hidden layers.
C . Keep the model’s decision-making process a secret to protect proprietary algorithms.
D . Continuously monitor the model’s performance on a static test dataset.

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Question #34

A company wants to ensure that its Retrieval Augmented Generation (RAG) system retrieves all relevant documents for user queries without missing important information.

Which metric should the company track?

A . Faithfulness
B . Recall
C . Mean reciprocal rank
D . Precision

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Correct Answer: B
B

Explanation:

The verified answer is B. Recall. In a Retrieval Augmented Generation system, the retrieval component must find the documents or passages that contain the information needed to answer a user query. The question specifically says the company wants to retrieve all relevant documents and avoid missing important information. That wording points directly to recall. Recall measures how many of the relevant items were successfully retrieved out of all relevant items that should have been retrieved. In practical RAG evaluation, high recall means the retriever is less likely to omit important source documents from the context sent to the language model.

Faithfulness is incorrect because it evaluates whether the generated answer is grounded in, and consistent with, the retrieved context. It is a generation-quality metric, not the primary retrieval-coverage metric. A response can be faithful to retrieved context even if the retriever failed to include some important documents. Mean reciprocal rank is incorrect because it measures how highly the first relevant result appears in the ranked list. It is useful for ranking quality, but it does not directly confirm that all relevant documents were retrieved. Precision is incorrect because precision measures how many retrieved documents are actually relevant. High precision reduces irrelevant results, but it can still miss relevant documents if the retriever returns only a narrow subset.

AWS documentation separates retrieval-focused evaluation metrics from response-generation metrics. For knowledge base retrieval-only evaluations, AWS lists built-in retrieval metrics such as context relevance and context coverage, while response generation evaluations include metrics such as faithfulness, correctness, completeness, and helpfulness. This distinction supports the principle that retrieval coverage and not missing relevant information is a retrieval-side concern. In standard information retrieval terminology, the metric that best captures “do not miss relevant documents” is recall.

Question #35

Which approach provides human-in-the-loop improvement of foundation models (FMs) throughout the ML lifecycle?

A . Using automated testing scripts to validate model outputs and implementing self-correction mechanisms without human intervention
B . Collecting human feedback during only the initial training phase and relying only on automated metrics for subsequent model iterations
C . Incorporating continuous human feedback across model development, training, and deployment phases and using performance evaluation to improve model accuracy
D . Implementing reinforcement learning algorithms that automatically adjust model parameters based on predefined success metrics without human oversight

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Correct Answer: C
C

Explanation:

The verified answer is C. Incorporating continuous human feedback across model development, training, and deployment phases and using performance evaluation to improve model accuracy. The question asks for human-in-the-loop improvement throughout the ML lifecycle, not a one-time review and not fully automated correction. AWS AI Practitioner guidance describes the foundation model lifecycle as including data selection, model selection, pre-training, fine-tuning, evaluation, deployment, and feedback. Because feedback is part of the lifecycle, human review should not be limited to the first training stage only.

AWS generative AI lifecycle guidance also emphasizes continuous improvement and feedback loops. It explains that operationalizing a generative AI application is not a one-time event, but an ongoing process driven by real-world usage, feedback systems, automated feedback loops, direct user feedback mechanisms, and strategic human-in-the-loop interventions. This supports option C because it combines human feedback across phases with performance evaluation to improve model behavior and accuracy over time.

Option A is incorrect because it removes human intervention. Automated testing is valuable, but it is not human-in-the-loop.

Option B is incorrect because collecting human feedback only during initial training is too narrow. It ignores post-training evaluation, deployment monitoring, user feedback, drift, and iterative improvement.

Option D is incorrect because it relies on automated reinforcement learning and predefined metrics without human oversight. That again contradicts the “human-in-the-loop” requirement.

The strongest clue is the phrase “throughout the ML lifecycle.” A correct human-in-the-loop strategy must include human review, human feedback, and evaluation during development, training, deployment, and ongoing improvement.

Therefore, option C is the only answer that satisfies both parts of the requirement: continuous human involvement and lifecycle-based model improvement.

Question #36

An education company wants to build a private tutor application. The application will give users the ability to enter text or provide a picture of a question. The application will respond with a written answer and an explanation of the written answer.

Which model type meets these requirements?

A . Computer vision model
B . Multimodal LLM
C . Diffusion model
D . Text-to-speech model

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Question #37

A company is building a large language model (LLM) question answering chatbot. The company wants to decrease the number of actions call center employees need to take to respond to customer questions.

Which business objective should the company use to evaluate the effect of the LLM chatbot?

A . Website engagement rate
B . Average call duration
C . Corporate social responsibility
D . Regulatory compliance

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Question #38

A company wants to create an application by using Amazon Bedrock. The company has a limited budget and prefers flexibility without long-term commitment.

Which Amazon Bedrock pricing model meets these requirements?

A . On-Demand
B . Model customization
C . Provisioned Throughput
D . Spot Instance

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Question #39

A company deployed an AI/ML solution to help customer service agents respond to frequently asked questions. The questions can change over time. The company wants to give customer service agents the ability to ask questions and receive automatically generated answers to common customer questions.

Which strategy will meet these requirements MOST cost-effectively?

A . Fine-tune the model regularly.
B . Train the model by using context data.
C . Pre-train and benchmark the model by using context data.
D . Use Retrieval Augmented Generation (RAG) with prompt engineering techniques.

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Question #40

A company wants to use AI for budgeting. The company made one budget manually and one budget by using an AI model. The company compared the budgets to evaluate the performance of the AI model. The AI model budget produced incorrect numbers.

Which option represents the AI model’s problem?

A . Hallucinations
B . Safety
C . Interpretability
D . Cost

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