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NEW QUESTION # 50
Your team is working on an image recognition system to help identify plants. They have collected a large amount of data but need to get this data labeled.
Which phase of CPMAI is this done?
Answer: F
Explanation:
Phase III: Data Preparation includes the Data Labeling generic task group. Specifically, the Label data task covers "identifying methods for data labeling and engaging in data labeling efforts," which is essential for supervised learning workflows like image recognition.
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NEW QUESTION # 51
During CPMAI Phase II, it's important to not only understand the sources of your data but also what data is required for training as well as identifying the features that are required.
When looking to gather data, what approach is best when determining how much data you need?
Answer: D
Explanation:
Phase II: Data Understanding centers on identifying just the right amount of data for model training-neither too little (risking underfitting) nor too much (wasting resources and introducing noise). This balanced-
"Goldilocks"-approach ensures you collect sufficient high-quality, relevant records to meet cognitive objectives without incurring unnecessary cost or complexity.
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NEW QUESTION # 52
You have an Anomaly Detection project you're working on and you need a simple approach of clustering data into classified groups. Which algorithm is the best choice given this situation?
Answer: A
Explanation:
Clustering is defined as "an unsupervised process that partitions data into groups (clusters) based on similarity without preassigned labels." K-Means is the canonical unsupervised clustering algorithm, iteratively assigning points to K centroids to minimize within-cluster variance. This makes K-Means the simplest and most direct choice for grouping data in an anomaly-detection context.
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NEW QUESTION # 53
For AI projects the code and systems don't matter as much as the data. In fact, big data is what's powering much of this latest wave of AI. What's most important for your company to consider around data?
Answer: B
Explanation:
CPMAI emphasizes that data is only as valuable as the team's ability to manage, prepare, and harness it effectively. In Phase I: Business Understanding, one of the first tasks under Assess Situation is an "AI Skills Assessment," which ensures that the project team has the right mix of experience and tooling expertise to handle data- intensive AI work. Without skilled data engineers and AI practitioners, even the largest datasets cannot be transformed into business value.
The Workbook's Task Group: Assess Situation in Phase I explicitly calls out "AI Skills Assessment" alongside resource and tooling considerations, highlighting that team capability is a foundational requirement for any data-centric initiative.
Furthermore, in Domain IV: Data for AI of the CPMAI Exam Content Outline, managing data fundamentals and Big Data concepts hinges on having personnel who can "apply Big Data approaches to enhance AI capabilities", which presupposes the presence of experienced data professionals.
Thus, the single most critical factor is ensuring you have team members with the right experience and tool expertise to handle and derive value from massive volumes of data.
NEW QUESTION # 54
You are establishing the data requirements for the project. Which of the following tasks is the least likely to impact data requirements?
Answer: A
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 # 55
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