PMI CPMAI_V7 CERTIFICATION TEST ANSWERS - CPMAI_V7 RELIABLE CRAM MATERIALS

PMI CPMAI_v7 Certification Test Answers - CPMAI_v7 Reliable Cram Materials

PMI CPMAI_v7 Certification Test Answers - CPMAI_v7 Reliable Cram Materials

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The Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam CPMAI_v7 pdf questions and practice tests are designed and verified by a qualified team of CPMAI_v7 exam trainers. They strive hard and make sure the top standard and relevancy of Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam CPMAI_v7 Exam Questions. So rest assured that with the CPMAI_v7 real questions you will get everything that you need to prepare and pass the challenging Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam CPMAI_v7 exam with good scores.

PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q70-Q75):

NEW QUESTION # 70
Enhancing and cleaning data is an important action during which phase of CPMAI?

  • A. Phase I
  • B. Phase IV
  • C. Phase III
  • D. Phase II
  • E. Phase VI
  • F. Phase V

Answer: C

Explanation:
The CPMAI v7 methodology groups all data-centric preparation activities-including both data cleansing ("Clean data") and data augmentation ("Enhance & Augment data")-into Phase III: Data Preparation. In this phase, teams focus squarely on constructing the dataset to be used for modeling by performing all required cleaning, transformation, and enhancement operations.
Phase III: Data Preparation is defined in the Workbook's Table of Contents as covering Data Cleansing & Enhancement tasks ("Clean data" and "Enhance & Augment data") .
Under Phase III, the Generic Task Group: Data Cleansing & Enhancement explicitly lists "Task: Clean data" (bringing data quality to modeling-ready levels) and "Task: Enhance & Augment data" (producing derived attributes and new records) as core activities .
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NEW QUESTION # 71
In what way would you be using Generative AI if you used the results of the Generative AI solution to improve and accelerate your job?

  • A. As an Augmented Intelligence system
  • B. Used for Hyperpersonalization
  • C. As a programmatic approach for automation
  • D. As an autonomous system removing the human from the loop

Answer: A

Explanation:
The CPMAI Glossary defines Augmented Intelligence as "enhancing human abilities with AI," where AI outputs are leveraged by humans to improve decision-making or productivity. Using Generative AI to accelerate or improve your own work is precisely an Augmented Intelligence use case, distinct from full autonomy or simple automation .
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NEW QUESTION # 72
Leadership wants a new HR system built that will better handle potential candidate matching. The project manager assigned to this project believes that the project is well-suited for AI, however they are unsure which pattern of AI this would be.
What should the project manager do?

  • A. Determine which pattern of AI this project falls under so they can best collect the data needed and skill sets for the team.
  • B. Move forward without determining which pattern of AI this falls under.
  • C. Pick an algorithm that seems best suited for the problem and then determine which pattern of AI it is based on the algorithm selected.
  • D. Conduct a straw poll with stakeholders to determine which pattern of AI this project falls under so they can best collect the data needed and skill sets for the team.

Answer: A

Explanation:
In Phase I: Business Understanding, after performing the Go/No Go assessment, the CPMAI methodology requires teams to perform AI Pattern identification-mapping business objectives to one or more of the Seven Patterns of AI-so that the right data requirements, algorithms, and team skills can be scoped effectively. This early pattern identification helps accelerate design by leveraging best practices for that pattern .


NEW QUESTION # 73
You're running an image recognition project and realize that you do not have enough data of a certain type of vehicle. What is the best course of action to get the additional labeled data you need?

  • A. Perform Data Sampling
  • B. Perform Data Anonymization
  • C. Purchase the data from a third party
  • D. Perform Data Transformation & Multiplication

Answer: D

Explanation:
In CPMAI v7's Phase III: Data Preparation, teams are instructed to construct the final modeling dataset through a variety of enhancement activities-including data augmentation, which specifically covers transforming existing records or generating entirely new records to increase volume and variety. This
"augmentation" is described as "constructive data preparation operations such as the production of derived attributes or entire new records, or transformed values for existing attributes" .
Moreover, under the Training & Test Data Requirements task, the Workbook explicitly asks project teams to determine "What transformation or multiplication activities can be done to increase training data volume while maintaining quality" . Performing data transformation (e.g., image rotations, color jitter, cropping) and multiplication (synthetic record generation) directly addresses the lack of labeled samples without incurring the cost or delay of third-party purchases, making option B the correct approach.
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NEW QUESTION # 74
Your team is testing the NLP model they just created to make sure it's performing as expected. Some of your team members want to move this model to production and move to the next iteration.
What's wrong with this workflow?

  • A. You need to make sure the AI Go/No Go questions have been addressed
  • B. Nothing is wrong with this workflow. You can move to the next iteration
  • C. Team members should not be able to move to new projects until senior management signs off
  • D. Model Evaluation requires continuous model evaluation, retraining, and operationalization

Answer: A

Explanation:
Phase V of the CPMAI v7 methodology-Model Evaluation and Maintenance-includes a formal CPMAI Phase V Go/No-Go assessment before any model can be moved into production or on to the next iteration.
This checkpoint ensures that the model meets predefined business success criteria, quality metrics, and risk considerations prior to deployment. Skipping this Go/No-Go review bypasses critical governance questions and undermines the integrity of the AI lifecycle.
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NEW QUESTION # 75
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