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Creating machine learning models can be complicated. Your team wants to use tools called Automated Machine Learning (AutoML) to simplify the process. You know of another team that has used AutoML tools and it's saved the team a lot of time.
However, what's the one area you should not have the AutoML tool help with?
A. Automatic hyperparameter tuning
B. Automatic model assessment
C. Automatic model selection
D. Iterative modeling and evaluation
E. Automatic algorithm selection
Answer: D
Explanation:
CPMAI's Usage of AutoML task instructs teams to "Document how AutoML tools will be used for model creation" and to verify that the output can be integrated into the overall I/O flow . While AutoML excels at automating algorithm selection, model selection, hyperparameter tuning, and even preliminary performance metrics, CPMAI places iterative modeling and evaluation squarely under the manual Model Evaluation phase-where teams must interpret results against business success criteria and decide on next steps.
Entrusting that high-level, iterative decision-making to an AutoML black box would undermine the human- centric evaluation that CPMAI mandates.
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NEW QUESTION # 85
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 retraining
B. Model Drift
C. Model reevaluation
D. Model Operationalization
Answer: A
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 # 86
You are establishing the data requirements for the project. Which of the following tasks is the least likely to impact data requirements?
A. The quality of the data you collect
B. The makeup of your data team
C. The volume of the data you collect
D. The location/source of your data collection
Answer: B
Explanation:
In Phase II: Data Understanding, CPMAI's Generic Task Groups focus on:
Collecting initial data (identifying sources and volumes) and describing data (location/source) .
Verifying data quality to ensure completeness and correctness .
Team composition (the makeup of your data team) is addressed earlier under Phase I: Assess Situation, not during the Data Understanding phase where data requirements (quality, volume, source) are determined.
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NEW QUESTION # 87
You're looking to take an image and have a Generative AI solution generate additional content beyond the bounds of the current image size. What Generative AI approach can you use?
A. Use of Generative Outpainting
B. Use of super#resolution to enhance the existing image
C. Prompt engineering for new image generation
D. Use of inpainting to replace image components
Answer: A
Explanation:
Generative AI encompasses techniques that create new data based on learned patterns. Outpainting is the specific generative approach for extending an image's content beyond its original borders. It leverages the same learned representations as inpainting or super#resolution but focuses on generating entirely new regions around an existing image.
NEW QUESTION # 88
Your team is running a forecasting project and wants to use previous user data to better predict future outcomes. However your team doesn't have access to all the data it needs. What's the best course of action?
A. Move ahead as planned so you stay on time with your project.
B. Do not move forward until you have access to all the data you need.
C. Cautiously move forward knowing you may need to pause mid-project which is ok.
D. Move ahead as planned and hope you get access to the data once you need it. Since you're using an iterative approach you can always go back to steps as needed later on.
Answer: B
Explanation:
During Phase I: Business Understanding, the Data Feasibility task explicitly mandates a Go/No-Go decision on data availability and access: "Do you have access to the data you need? If not, what do you need for access to the data? Mark as a 'NoGo.'" Projects should not proceed until all essential data access requirements are met to avoid wasted effort and unresolvable blockages down the line
NEW QUESTION # 89
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