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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q40-Q45):
NEW QUESTION # 40
Your team is working on an NLP model and has just operationalized the first model. Your team makes updates to the model, overwrites the original model, and puts this new model into operation. However, one of the teams using the model has seen a decrease in performance and is asking to use the original model.
What critical error did your team make?
Answer: C
Explanation:
In Phase VI: Model Operationalization of the CPMAI v7 methodology, project teams must explicitly plan for "model versioning and iteration" as part of deploying and maintaining models in production. Overwriting the original model without preserving its prior version prevents rollback and comparison, which is a core requirement for robust AI operations.
The Workbook states that operationalization considerations include "model versioning and iteration" to ensure that previous model artifacts are retained and that updates can be managed safely.
Additionally, under Edge Model Data Needs, teams are instructed to "Determine methods for model versioning and update" to support proper tracking and governance of model changes across iterations.
NEW QUESTION # 41
You're working on a computer vision application and realize that you do not have enough real world data for the project. You need additional data created to support your training needs. Specifically, the images you need are of people in different poses. What is the best way to obtain this data?
Answer: D
Explanation:
Synthetic data is "artificially generated data that mimics real-world data, used when actual data is scarce or sensitive." Generating synthetic training images of people in the required poses allows you to rapidly augment your dataset without logistical, privacy, or labeling overhead.
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NEW QUESTION # 42
Your model has been working fine for the last three months, however recently you notice the model's performance has greatly declined. What seems to have been overlooked in your workflow pipeline?
Answer: D
Explanation:
The CPMAI methodology's Model Iteration Approach (Phase V) explicitly calls out that "models will need continuous iteration, especially if they are only marginally providing the desired results" and requires teams to
"detail approach that will be used to iterate this model to improve on any of the results in this Phase" . Failing to include a model retraining pipeline means the model cannot adapt to new data distributions, leading to performance degradation over time.
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NEW QUESTION # 43
As the project manager, you are leading a brainstorming session with key stakeholders around a new Hyperpersonalization project. What's a key feature for this project that should happen to ensure success?
Answer: C
Explanation:
The Hyperpersonalization pattern is defined as tailoring experiences based on individual user characteristics or behavior-requiring each profile to learn and adapt continuously as more data arrives. Manually updating or pre-programming profiles undermines this dynamic learning capability.
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NEW QUESTION # 44
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?
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 # 45
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