Question # 1

An AI project team has completed an AI go/no-go assessment. They have discovered several technology and data factors to be insufficient. Which action should occur? 

A. Verify data quality and stakeholder alignment 
B. Proceed with development despite data issues 
C. Focus solely on technology upgrades, not data 
D. Launch the AI project without further assessment 

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Question # 2

A government agency is adopting an AI/machine learning (ML) model to analyze large sets of public data for policy making. It is crucial that the project team ensures the accuracy of the model's predictions. If the project team needs to validate the model, which action should they perform?

A. Ensure adherence to coding standards. 
B. Conduct a single comprehensive validation. 
C. Utilize a diverse set of test cases. 
D. Implement continuous integration testing. 

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Question # 3

A financial institution is planning to use AI capabilities to detect fraudulent transactions. The project manager needs to ensure that all necessary requirements are met before proceeding. What is a necessary initial task?

A. Evaluating the accuracy of current fraud detection methods 
B. Determining the scalability of AI solutions for transaction monitoring 
C. Identifying the primary stakeholders and their needs 
D. Assessing the ethical implications of using AI for fraud detection 

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Question # 4

To determine if an AI solution is appropriate for an upcoming project, the project manager needs to evaluate whether the project requires a cognitive approach. What should the project manager address? 

A. Existing well-defined business objectives 
B. Estimated project cost 
C. Required level of interpretability 
D. Potential non-cognitive alternatives 

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Question # 5

A fintech AI project uses third-party data sources for credit risk modeling. The project manager is concerned about compliance and accountability if the external data quality changes. Which control best supports responsible and trustworthy AI delivery? 

A. Establish data governance and supplier controls, including auditability and monitoring 
B. Remove all external data sources immediately 
C. Only document model performance once at launch 
D. Allow each team to apply its own data definitions 

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Question # 6

In an aerospace manufacturing project, engineers are preparing data to train an AI system for predictive maintenance. They need to transform the data from multiple sensors and ensure it is consistent and accurate before building the model. What should the project manager do to handle the inconsistencies?

A. Enhance the current data with additional sources 
B. Use data augmentation techniques to fill the gaps 
C. Implement a validation protocol for sensor data 
D. Identify and reconcile conflicting data points 

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Question # 7

An aerospace engineering firm is developing a machine learning model to predict component failures. The project manager needs help to ensure the training data is representative of real-world scenarios. Which method will meet the project manager’s objective?

A. Implementing real-time data monitoring 
B. Analyzing competitor data 
C. Relying solely on synthetic data 
D. Using historical data from multiple sources

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Question # 8

A project manager is preparing a contingency plan for an AI-driven customer service platform. They need to determine an effective strategy to handle potential system downtimes. Which strategy addresses the project manager’s objective?

A. Developing an automated fallback chatbot with limited capabilities
 B. Providing extensive training to customer service representatives on handling AI failures 
C. Creating a robust customer service logging system to quickly identify and resolve issues 
D. Implementing a manual override system for critical customer queries

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Question # 9

A project team is using a prompt engineering approach to improve AI/machine learning (ML) model outputs. They started with broad questions and then narrowed down the specific elements. If the team had provided insufficient context, what would be the result?

A. The model would generate more creative outputs. 
B. The responses would lack relevance. 
C. The model would perform more efficiently. 
D. The output would include higher accuracy.

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Question # 10

A manufacturing firm plans to use AI to predict equipment failures. The team can access sensor data but it contains many missing values and out-of-range readings. What should the project manager prioritize first?

A. Data understanding and quality assessment to characterize missingness and anomalies 
B. Deploy the model quickly and fix issues later 
C. Ignore the sensor data and use only expert opinion
 D. Focus only on UI design for the dashboard 

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