CPMAI practice questions 161–170

1. Question 161 · 192 in bank

   A healthcare organization plans to develop an AI-driven diagnostic tool. To define the required data, the project manager needs to ensure data consistency and accessibility. Which method should the project manager use?

   • ALeveraging natural language processing (NLP) to standardize patient records
   • BPerforming a data quality assessment with extraction, transformation, and loading (ETL) processes
   • CEmploying a hybrid cloud strategy for scalable data storage
   • DIntegrating electronic health records (EHR) with AI through machine learning (ML) algorithms

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   Answer: B

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2. Question 162 · 192 in bank

   A project manager is preparing a final report on an AI project. The report must highlight lessons learned, focusing on ethical concerns and compliance with data regulations. In addition, the team has identified multiple ethical issues related to data privacy during the project. What is an effective approach to address the situation for future AI projects?

   • ADevelop a transparent data compliance usage policy.
   • BIncrease the frequency of compliance audits.
   • CProvide additional training on ethical AI practices.
   • DImplement a robust ethical data governance framework.

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   Answer: D

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3. Question 163 · 192 in bank

   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?

   • AFocus solely on technology upgrades, not data.
   • BLaunch the AI project without further assessment.
   • CVerify data quality and stakeholder alignment.
   • DProceed with development despite data issues.

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   Answer: C

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4. Question 164 · 192 in bank

   An AI project team needs to consider compliance with data regulations and explainability standards as requirements for a new AI solution. At what point in the project should the requirements be approached?

   • AAs part of the business understanding phase
   • BAs part of the data preparation phase
   • CAs optional guidelines based on project scope
   • DAs part of the final testing phase

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   Answer: A

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5. Question 165 · 192 in bank

   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?

   • AUse data augmentation techniques to fill the gaps.
   • BEnhance the current data with additional sources.
   • CIdentify and reconcile conflicting data points.
   • DImplement a validation protocol for sensor data.

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   Answer: C

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6. Question 166 · 192 in bank

   A national health insurance company is embarking on a complex AI project to assist in coordinating patient care across its multiple hospital network. The AI system will analyze large amounts of patient data to coordinate care, improve patient outcomes, and optimize resource allocation. Numerous healthcare providers’ data needs to be integrated. The data includes patient information which is private. The project needs to comply with data privacy regulations in various countries. Which critical step should be performed to optimize representative training data?

   • AImplement comprehensive bias detection metrics.
   • BImprove data understanding and preparation.
   • CIncrease the data set size without considering diversity.
   • DEnhance the key performance indicator (KPI) metrics.

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   Answer: B

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7. Question 167 · 192 in bank

   A project team is preparing to move to the next phase of their AI project. The team needs to ensure that all transparency and explainability requirements are met. Which activity should the project team perform?

   • AConduct a thorough data quality assessment.
   • BDocument the decision-making process of the AI model.
   • CEstablish a feedback mechanism for ongoing evaluation.
   • DDefine the ethical guidelines for the AI project.

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   Answer: B

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8. Question 168 · 192 in bank

   An AI project team has identified a gap in their data knowledge and experience. They need to address this issue in order to proceed with their AI implementation. What is the effective solution?

   • AUtilize an AI-specific data enhancement protocol to improve data quality.
   • BHire an external data consultant to provide targeted guidance and training.
   • CEngage in a comprehensive data immersion program to build internal capabilities.
   • DDeploy an adaptive data knowledge framework (ADKF) to bridge the expertise gap.

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   Answer: B

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9. Question 169 · 192 in bank

   A logistics company is operationalizing an AI system to improve delivery times. The project team needs to identify performance constraints that may impact the AI solution. Which method should the project manager use to meet the team’s objective?

   • AConducting a preliminary feasibility study
   • BTraining employees on AI ethics
   • CImplementing advanced data visualization tools
   • DBenchmarking against competitors

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   Answer: A

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10. Question 170 · 192 in bank

    The project team at an IT services company is working on an AI-based customer support chatbot. To help ensure the chatbot functions effectively, they need to define the required data. Which method meets the project requirements?

    • ADeveloping a new script based on anticipated customer queries
    • BGathering historical customer interaction logs for training data
    • CIntegrating feedback from beta customers to refine the model
    • DUsing synthetic data generated from sample customer conversations

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    Answer: B

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CPMAI practice, question 161. 192 questions available.