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PMI CPMAI_v7 Exam Syllabus Topics:
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NEW QUESTION # 19
The confusion matrix measures how the algorithm performs for a binary classification activity. As your team is running tests to evaluate model performance, they are seeing the model is incorrectly categorizing flowers as trees. Your model is provided the following:
- A. True Positive results
- B. True Negative results
- C. False Negative results
- D. False Positive results
Answer: D
Explanation:
A false positive occurs when the model predicts the positive class (e.g., "tree") but the actual label is negative (e.g., "flower"). The confusion matrix definition confirms that mislabeling a negative instance as positive maps to the false positive count.
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NEW QUESTION # 20
You have been receiving customer data for the past six months. However recently you notice that this data has drastically changed due to the upcoming holiday season.
What seems to be taking place?
- A. Data Drift
- B. Lack of stakeholder support
- C. Model Drift
- D. An incomplete milestone list
Answer: A
Explanation:
A sudden shift in the incoming data distribution-such as seasonal changes in customer behavior-is known as data drift. CPMAI defines model drift as "degradation in a model's performance over time as the underlying data distribution changes," implying that the root cause is the data itself shifting. Recognizing data drift is the first step in adapting both data pipelines and models to maintain performance .
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NEW QUESTION # 21
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 # 22
You're being told by upper management that you need to manage a new AI project. You need to determine the AI project fit to make sure you're actually solving a real business problem.
During Phase I: Business Understanding, you should consider at least one of the following (Select all that apply):
- A. Solves a previously unsolved problem
- B. Has the "cool" factor
- C. Explores a proof of concept for an AI project
- D. Enhance revenue
- E. Solves an already solved problem but does it better and cheaper
- F. Improve company competitiveness in the market
Answer: A,D,E,F
Explanation:
Phase I begins with Determine Business Objectives to ensure the AI initiative aligns to real business needs.
Teams evaluate whether the solution will enhance revenue, solve a previously unsolved problem, improve competitive positioning, or "provide enough ROI/impact...and be better/more impactful than the current, non- cognitive, heuristic approach" (i.e., do something already addressed but do it better and cheaper). Proof-of- concepts (A) and "cool" factors (E) are not valid business objectives under CPMAI's Business Understanding tasks.
NEW QUESTION # 23
Your team is working on an image recognition project, have collected the appropriate data for the project, and have picked a neural network algorithm. They are now ready to train their model.
In which phase of CPMAI is this done?
- A. Phase IV
- B. Phase V
- C. Phase II
- D. Phase VI
- E. Phase III
- F. Phase I
Answer: A
Explanation:
Phase IV: Model Development is explicitly where "Model Training / Model Building" occurs. This phase includes tasks for selecting modeling techniques, conducting hyperparameter optimization, and executing the actual training runs on the prepared datasets.
NEW QUESTION # 24
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?
- A. Neural Network
- B. Decision Tree
- C. Hidden Markov Model
- D. K-Means Clustering
Answer: D
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 # 25
Which of the following best describes the technical definition of Machine Learning?
- A. The application of pre-defined rules and algorithms to solve complex problems.
- B. The use of computing technology to enable machines to gain cognitive intelligence.
- C. An approach to using increasing levels of intelligence to solve greater cognitive needs from unintelligent automation to autonomous business process.
- D. A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.
Answer: D
Explanation:
Tom Mitchell's widely adopted formulation captures ML's essence: improvement on task T, measured by P, through experience E. This aligns with CPMAI's view that ML enables systems to learn from data and improve over time ("The ability of a machine to learn from data, improve with experience, and apply that learning to make predictions.") .
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NEW QUESTION # 26
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. Attended bot
- B. Unattended bot
- C. Augmented Intelligence
- D. RPA is not the right solution to this problem
Answer: A
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.
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NEW QUESTION # 27
You are leading a project to develop a new predictive maintenance solution. Together with your project team you determine your data needs, see if you have access to the data, and then begin working on the project.
Which phase best describes the work you are performing?
- A. Phase V
- B. Phase IV
- C. Phase II
- D. Phase VI
- E. Phase III
- F. Phase I
Answer: C
Explanation:
Phase II: Data Understanding is dedicated to identifying data requirements, collecting initial data, assessing data quality, and verifying that necessary datasets are accessible and fit for modeling. Determining what data you need and confirming access are the core activities of this phase .
NEW QUESTION # 28
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. Retraining Pipelines
- C. Data Acquisition / Ingest / Capture
- D. ELT Pipeline
Answer: B
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 # 29
The team is evaluating where the sources of the data for training are. What phase of CPMAI are they in?
- A. Phase V
- B. Phase IV
- C. Phase II
- D. Phase VI
- E. Phase III
- F. Phase I
Answer: C
Explanation:
Phase II, Data Understanding, is explicitly focused on identifying data needs and sources-including
"Identify appropriate datasets for machine learning" and "Evaluate training data requirements" under the Managing the Data Understanding Phase tasks. This is the phase where teams determine where and how they will collect the training data .
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NEW QUESTION # 30
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. 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.
- D. Pick an algorithm that seems best suited for the problem and then determine which pattern of AI it is based on the algorithm selected.
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 # 31
You've built your model and now need to see if it actually works as expected. In which phase of CPMAI is this done?
- A. Phase II
- B. Phase IV
- C. Phase V
- D. Phase VI
- E. Phase III
- F. Phase I
Answer: C
Explanation:
Phase V: Model Evaluation is dedicated to validating a trained model's performance against technical metrics and the business success criteria defined earlier. This phase encompasses tasks such as generating performance results, measuring KPIs, and deciding whether to retrain or proceed to operationalization.
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NEW QUESTION # 32
Your team has been asked to summarize and highlight patterns in historical purchasing data, identifying prior performance metrics and patterns. What type of analytics is most appropriate to apply for this need?
- A. Projective Analytics
- B. Descriptive Analytics
- C. Predictive Analytics
- D. Diagnostic Analytics
Answer: B
Explanation:
Descriptive analytics focuses on summarizing and reporting on past data-exactly what's needed to highlight historical purchasing patterns and performance metrics. The CPMAI Glossary defines analytics as "the use of statistical and computational methods to extract meaningful insights from data (related: predictive, descriptive, ...)" where descriptive analytics provides the retrospective view.
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NEW QUESTION # 33
The growth of Big Data has led to a desire to be able to do more to process and extract more value from Big Data. Simply storing data and providing analytics is no longer enough anymore to remain competitive.
To keep your organization competitive, you need to:
- A. Make sure senior management has deep understanding of big data and how best to extract value from big data to unleash it for competitive advantage.
- B. Make sure the technical team has deep understanding of big data and how best to extract value from big data to unleash it for competitive advantage.
- C. Make sure all senior leadership is data literate, understands the V's of big data, data's connections to your specific team, and how to extract value from big data to unleash it for competitive advantage.
- D. Make sure everyone on the team has an understanding of data, its connections to the organization, and how to extract value from big data to unleash it for competitive advantage.
Answer: C
Explanation:
CPMAI's Domain IV: Data for AI - Task 1: Managing Data Fundamentals and Big Data Concepts emphasizes that leaders-not just technical practitioners-must grasp the core characteristics of Big Data (the V's: volume, velocity, variety, veracity) and its strategic role in delivering business advantage. Ensuring senior leadership is data literate and understands how to leverage Big Data concepts across teams is critical for sustaining a competitive edge; merely upskilling the technical team or distributing data literacy unevenly will leave strategic gaps.
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NEW QUESTION # 34
Your company is insisting on running an automation project and applying AI best practices and methodologies to the project. You understand that automating things is just the act of using machines to repeat tasks, and does not require AI to achieve results. You think it is overkill but the project moves forward as planned.
What would likely have helped avoid this conflict?
- A. Applying a hybrid approach of automation and AI best practices would have achieved better results.
- B. Everyone on the team should understand the differences between automation and autonomous systems.
- C. Senior management should become involved in the project.
- D. Nothing - running automation projects like autonomous projects is the correct thing to do.
Answer: B
Explanation:
During Phase I's Cognitive Project Requirements tasks, CPMAI instructs teams to "Determine when to implement automation versus AI." Explicitly distinguishing between simple rule-based automation (RPA) and true cognitive solutions prevents misapplication of AI methodology to non-AI use cases. Ensuring everyone understands this distinction up front would have avoided misalignment on methodology.
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NEW QUESTION # 35
Your team has built a new robot that roams the halls at your organization and helps with various things such as small deliveries. However, you notice that many employees are opting not to use the robot. When you ask them why they tell you that the robot looks "creepy" and they would rather not interact with it. What's going on here?
- A. Lack of understanding the robot's usefulness
- B. Bias towards the robot
- C. Safety and reliability issues that impact bot usefulness
- D. The bot is falling into The "Uncanny Valley"
Answer: D
Explanation:
This reaction is a classic example of the Uncanny Valley phenomenon, where a nearly human-like robot triggers discomfort or eeriness in users because it sits in the valley between clearly robotic and convincingly human appearances. Although not explicitly named in the CPMAI glossary, addressing this user experience concern falls under Continuous Improvement and Respect for People, ensuring cognitive solutions are designed for positive user acceptance.
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NEW QUESTION # 36
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. Do not move forward until you have access to all the data you need.
- B. Cautiously move forward knowing you may need to pause mid-project which is ok.
- C. Move ahead as planned so you stay on time with your project.
- 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: A
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 # 37
As an organization building an AI solution for your current customers based in NYC, but with possible plans for future expansion, how should you handle worldwide AI laws and regulations?
- A. Make sure to follow relevant data, privacy, and other important laws as it pertains to the United States
- B. Make sure to follow relevant data, privacy, and other important laws both in the US and where you're likely to expand to in the coming year
- C. Make sure to follow relevant data, privacy, and other important laws as it pertains to NYC
- D. You're too small of an organization to be worried about laws at the moment
Answer: B
Explanation:
CPMAI's Trustworthy AI - Navigating AI Regulations and Frameworks tasks require continuously monitoring "AI-relevant data privacy laws and regulations" both where you operate today and where you plan to expand . Furthermore, the Workbook's Task: Required Compliance with Regulations and Laws instructs teams to identify all laws and regulations that might apply based on location, industry, and other factors, ensuring legal and liability risks are addressed before operationalization .
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NEW QUESTION # 38
An organization is to undertake a multi-pattern AI project. They want to build a robot that is able to roam the halls as well as converse with employees and answer basic questions.
What is the best approach for handling this project?
- A. Run it as one project, combining teams, data requirements, and project needs
- B. Run it as a hybrid approach and some phases are run separately while other phases are combined together
- C. Run each pattern in isolation, with separate teams
- D. Run each pattern as its own project, with their own CPMAI phase iterations, data requirements, and project needs
Answer: A
Explanation:
Under Domain I: Evaluating AI Applications and Patterns, CPMAI instructs practitioners to "Integrate multiple AI patterns for comprehensive applications" when solutions span more than one cognitive pattern.
Treating a multi-pattern system as a single, cohesive project ensures aligned data streams, shared infrastructure, and unified governance.
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NEW QUESTION # 39
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?
- A. They did not have a model retraining pipeline that took into account models
- B. They did not practice model iteration and properly iterate on the model
- C. They did not have data governance in place
- D. They did not practice model versioning and keep all versions of the model
Answer: D
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 # 40
The growth of Big Data has led to a desire to be able to do more to process and extract more value from Big Data. Simply storing data and providing analytics is no longer enough anymore to remain competitive.
To keep your organization competitive, you need to:
- A. Make sure senior management has deep understanding of big data and how best to extract value from big data to unleash it for competitive advantage.
- B. Make sure the technical team has deep understanding of big data and how best to extract value from big data to unleash it for competitive advantage.
- C. Make sure all senior leadership is data literate, understands the V's of big data, data's connections to your specific team, and how to extract value from big data to unleash it for competitive advantage.
- D. Make sure everyone on the team has an understanding of data, its connections to the organization, and how to extract value from big data to unleash it for competitive advantage.
Answer: C
Explanation:
CPMAI's Domain IV: Data for AI - Task 1: Managing Data Fundamentals and Big Data Concepts emphasizes that leaders-not just technical practitioners-must grasp the core characteristics of Big Data (the V's: volume, velocity, variety, veracity) and its strategic role in delivering business advantage. Ensuring senior leadership is data literate and understands how to leverage Big Data concepts across teams is critical for sustaining a competitive edge; merely upskilling the technical team or distributing data literacy unevenly will leave strategic gaps.
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NEW QUESTION # 41
Your team is looking for a short term ROI project and decides that an AI-enabled chatbot will be the project to start with. During Phase I of CPMAI you go through the AI Go/No Go decision chart and realize that you have not answered yes to all the business feasibility questions. You and the team have not determined a clear problem definition.
What's the best course of action with how to proceed?
- A. Do not move forward until you can determine a clear problem definition.
- B. Move forward with the project as planned. The problem definition will become clear later on in the project.
- C. Do not move forward and cancel the project altogether.
- D. Cautiously move forward as planned. You do not need to answer yes to all the questions in the AI Go
/No Go decision chart to start your project.
Answer: A
Explanation:
In Phase I's AI Go/No Go task group, the Business Feasibility step mandates that every business-feasibility question-including a clear problem definition-must be answered "Go" before proceeding. If any critical feasibility criteria remain unanswered or "No Go," the project must pause and resolve those uncertainties rather than advance prematurely.
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NEW QUESTION # 42
Recently your company has been getting a large number of spam emails and some employees have been clicking on these suspicious emails causing a headache for IT. The head of IT wants to create a more robust spam filter and your team has been tasked with this project.
What type of algorithm would you select for this problem?
- A. Multiclass Classification
- B. Clustering
- C. Binary (or Binomial) Classification
- D. Regression
Answer: C
Explanation:
A spam filter must decide between exactly two categories-spam or not spam-making it a binary (or binomial) classification task. The CPMAI Glossary defines binary classification as "a classification task where data is categorized into one of two classes (e.g., spam vs. not spam)."
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NEW QUESTION # 43
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