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Only need to practice for 20 to 30 hours

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PMI Certified Professional in Managing AI Sample Questions:

1. A hospital wants to develop a medical records system with the primary goal of minimizing or eliminating paper records. They have identified where the cognitive AI solution will be applied. In addition, business objectives have been quantified and key performance indicators (KPIs) have been determined.
What else needs to be done to progress to the next Cognitive Project Management for AI (CPMAI) phase?

A) Determine the project ROI
B) Create interdepartmental strategies
C) Explore external data sources
D) Begin prototype development

2. An aerospace company is evaluating whether their sensor data meets the requirements for an AI-based predictive maintenance system. The project team needs to ensure that the data's accuracy, resolution, and timeliness are adequate to predict equipment failures.
Which method addresses the requirements?

A) Implementing a data governance framework to ensure compliance
B) Analyzing data completeness and conducting feature engineering
C) Evaluating the data schema and integrating additional data sources
D) Performing a data quality assessment focusing on precision and latency

3. In a clustering analysis for data use, the project team finds that the clusters are not meaningful and do not provide actionable insights. Which activity should the project manager do with the project team?

A) Establish data governance protocols.
B) Identify the data gaps and address deficiencies.
C) Conduct an algorithm analysis on the data sources.
D) Assess the trade-offs of the various algorithms.

4. A government agency is implementing an AI-powered tool to enhance data security through anomaly detection. The project manager is assembling the team. To identify the subject matter experts (SMEs) who can provide the best insights and contributions to this project, the project manager needs to consider their experience and expertise in various technical domains.
Which method will help identify the qualified data SMEs?

A) Evaluating expertise with existing data architectures and their ability to optimize databases
B) Examining their expertise in neural network calibration and hyperparameter tuning
C) Conducting interviews to assess their knowledge in anomaly detection
D) Assessing proficiency in developing generative adversarial networks (GANs) and experience in successfully generating synthetic data

5. An organization's leadership team is concerned about the ethical implications of operationalizing their AI model. How should the project manager address these concerns in their presentation to the team?

A) Explain how the AI model complies with general data protection regulation (GDPR) and other regulations
B) Discuss the implementation of differential privacy and the algorithms used to protect data
C) Highlight the model's high performance metrics and low error rates
D) Demonstrate the use of bias detection tools to ensure fairness

Solutions: