Practice Free AIF-C01 Exam Online Questions – Page 6

Question #51

A company is building a job recommendation system based on job posting data and job seeker user profiles. The system shows bias in job recommendations based on gender for user profiles that are otherwise equivalent.

Which principle should the company follow to address this issue, according to AWS best practices for responsible AI?

A . Governance
B . Explainability
C . Controllability
D . Fairness

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Question #52

HOTSPOT

A company has multiple datasets that contain historical data. The company wants to use ML technologies to process each dataset.

Select the correct ML technology from the following list for each dataset. Select each ML technology one time or not at all. (Select THREE.)

– Computer vision

– Natural language processing (NLP)

– Reinforcement learning

– Time series forecasting

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Question #53

A company trained an ML model on Amazon SageMaker to predict customer credit risk. The model shows 90% recall on training data and 40% recall on unseen testing data.

Which conclusion can the company draw from these results?

A . The model is overfitting on the training data.
B . The model is underfitting on the training data.
C . The model has insufficient training data.
D . The model has insufficient testing data.

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Correct Answer: A
A

Explanation:

The ML model shows 90% recall on training data but only 40% recall on unseen testing data, indicating a significant performance drop. This discrepancy suggests the model has learned the training data too well, including noise and specific patterns that do not generalize to new data, which is a classic sign of overfitting.

Exact Extract from AWS AI Documents:

From the Amazon SageMaker Developer Guide:

"Overfitting occurs when a model performs well on training data but poorly on unseen test data, as it has learned patterns specific to the training set, including noise, that do not generalize. A large gap between training and testing performance metrics, such as recall, is a common indicator of overfitting."

(Source: Amazon SageMaker Developer Guide, Model Evaluation and Overfitting)

Detailed

Option A: The model is overfitting on the training data. This is the correct answer. The significant drop in recall from 90% (training) to 40% (testing) indicates the model is overfitting, as it performs well on training data but fails to generalize to unseen data.

Option B: The model is underfitting on the training data. Underfitting occurs when the model performs poorly on both training and testing data due to insufficient learning. With 90% recall on training data, the model is not underfitting.

Option C: The model has insufficient training data. Insufficient training data could lead to poor performance, but the high recall on training data (90%) suggests the model has learned the training data well, pointing to overfitting rather than a lack of data.

Option D: The model has insufficient testing data. Insufficient testing data might lead to unreliable test metrics, but it does not explain the large performance gap between training and testing, which is more indicative of overfitting.

Reference: Amazon SageMaker Developer Guide: Model Evaluation and Overfitting (https://docs.aws.amazon.com/sagemaker/latest/dg/model-evaluation.html)

AWS AI Practitioner Learning Path: Module on Model Performance and Evaluation

AWS Documentation: Understanding Overfitting and Underfitting (https://aws.amazon.com/machine-learning/)

Question #54

A company is developing a new model to predict the prices of specific items. The model performed well on the training dataset. When the company deployed the model to production, the model’s performance decreased significantly.

What should the company do to mitigate this problem?

A . Reduce the volume of data that is used in training.
B . Add hyperparameters to the model.
C . Increase the volume of data that is used in training.
D . Increase the model training time.

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Question #55

A company is building a generative AI application to help customers make travel reservations. The application will process customer requests and invoke the appropriate API calls to complete reservation transactions.

Which Amazon Bedrock resource will meet these requirements?

A . Agents
B . Intelligent prompt routing
C . Knowledge Bases
D . Guardrails

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Question #56

A company wants to group its customer base to understand different customer groups. The company has an unlabeled dataset that includes customer demographics, purchase history, and browsing behavior.

Which ML technique will meet these requirements?

A . Regression
B . Classification
C . Clustering
D . Reinforcement learning

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Question #57

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 #58

A company wants to use AI to protect its application from threats. The AI solution needs to check if an IP address is from a suspicious source.

Which solution meets these requirements?

A . Build a speech recognition system.
B . Create a natural language processing (NLP) named entity recognition system.
C . Develop an anomaly detection system.
D . Create a fraud forecasting system.

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Question #59

A company uses Amazon SageMaker for its ML pipeline in a production environment. The company has large input data sizes up to 1 GB and processing times up to 1 hour. The company needs near real-time latency.

Which SageMaker inference option meets these requirements?

A . Real-time inference
B . Serverless inference
C . Asynchronous inference
D . Batch transform

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Question #60

A company is building a new generative AI chatbot. The chatbot uses an Amazon Bedrock foundation model (FM) to generate responses. During testing, the company notices that the chatbot is prone to prompt injection attacks.

What can the company do to secure the chatbot with the LEAST implementation effort?

A . Fine-tune the FM to avoid harmful responses.
B . Use Amazon Bedrock Guardrails content filters and denied topics.
C . Change the FM to a more secure FM.
D . Use chain-of-thought prompting to produce secure responses.

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