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[General] High Pass-Rate PMI Practice CPMAI_v7 Exam Online | Try Free Demo before Purchase

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【General】 High Pass-Rate PMI Practice CPMAI_v7 Exam Online | Try Free Demo before Purchase

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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q43-Q48):NEW QUESTION # 43
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?
  • A. Model reevaluation
  • B. Model Drift
  • C. Model retraining
  • D. Model Operationalization
Answer: C
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.
=========

NEW QUESTION # 44
You are working on the data engineering pipeline for the AI project and you want to make sure to address the creation of pipelines to deal with model iteration. What part of the pipeline best deals with this step?
  • A. Feature Engineering
  • B. ELT Pipeline
  • C. Retraining Pipelines
  • D. Data Acquisition / Ingest / Capture
Answer: C
Explanation:
Model iteration requires regularly updating a deployed model with new data and configuration. The CPMAI Workbook's Task: Fine-Tuning / Re-training of Pre-Trained Models prescribes defining and documenting re- training pipelines as part of the model-building lifecycle to ensure seamless iteration and ongoing performance improvements.

NEW QUESTION # 45
Using machine learning and other cognitive approaches to understand how to take past/existing behavior and predict future outcomes or help humans make decisions about future outcomes using insight learned from past behavior/interactions/data is a core part to which pattern(s) of AI?
  • A. Goal Driven Systems
  • B. Recognition Pattern
  • C. Predictive Analytics & Decision Support
  • D. Predictive Analytics & Decision Support and Patterns and Anomalies
Answer: C
Explanation:
The Predictive Analytics & Decision Support pattern is defined as using historical data (past behavior) to forecast future events and provide decision support for human or automated processes. This is distinct from the Patterns & Anomalies pattern, which focuses on detecting unusual deviations rather than forecasting expected outcomes.
A CPMAI Glossary self-test question states that Predictive Analytics "uses historical data to forecast future outcomes" .
Another glossary question defines predictive analytics as aiming "to use historical data to forecast future outcomes" .
=========

NEW QUESTION # 46
Your team is looking to develop an RPA bot to help assist call center agents while on providing support. What type of bot should your team be creating?
  • A. RPA is not the right solution to this problem
  • B. Attended bot
  • C. Augmented Intelligence
  • D. Unattended bot
Answer: B
Explanation:
In the CPMAI Glossary, attended bots are defined as "software automation tools that work alongside humans (typically in front-office roles) to assist with tasks and boost productivity." Call-center assistance is a classic front-office scenario requiring a bot that human agents can invoke interactively.
=========

NEW QUESTION # 47
In order for Supervised Learning approaches to work, they must be fed clean, well-labeled data that the system can use to learn from examples. But how do you get Labeled Data?
As a team leader at a small startup, what approach would not be beneficial when trying to gather labeled data?
  • A. Contract with Third Party Data Labeling Firms
  • B. Hire a Contractor Workforce
  • C. Get your Users to Do it
  • D. Find a source of already labeled data
Answer: C
Explanation:
The Data Labeling task in Phase III: Data Preparation specifies that teams should identify labeling methods such as using internal staff, contracting third-party labelers, leveraging pre-existing labeled datasets, or combining those modes. Soliciting end-users to label data falls outside these recommended approaches and introduces uncontrolled variability and quality issues .
=========

NEW QUESTION # 48
......
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