[May-2026] PMI CPMAI_v7 Exam Practice Test Questions - TorrentExam
Updated Certification Exam CPMAI_v7 Dumps - Practice Test Questions
NEW QUESTION # 54
You are working for a large multinational organization and have been assigned to a new project. For your new ML project you need to make sure you're managing data privacy and security as you're working with sensitive customer data.
What critical security issues do you need to make sure you address? (Select all that apply.)
- A. Compliance with Data Privacy Laws even if they are out of your physical jurisdiction
- B. Securing model data and metadata
- C. Securely storing all data collected for training purposes
- D. Securing data at rest
Answer: A,B,C,D
Explanation:
Under Domain VI: Trustworthy AI - Task 2: Implementing AI Privacy and Security, CPMAI mandates that teams must:
Apply data privacy principles and "ensure compliance with General Data Protection Regulation (GDPR)" and other relevant laws regardless of location .
Identify and protect Personally Identifiable Information (PII) and "develop comprehensive AI safety and security protocols," which encompasses securing both model data and metadata and enforcing security monitoring for production systems .
Implement best practices for data anonymization, defense against adversarial attacks, and the secure handling of datasets-this includes securing data at rest and securely storing training data in accordance with organizational and regulatory requirements .
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NEW QUESTION # 55
Your team is using a neural network algorithm to generate a Machine Learning Model. What specific artifacts need to be included? (Select all that apply.)
- A. Bias-variance tradeoff
- B. Hyperparameter settings
- C. The algorithm code
- D. Supporting training data
Answer: B,C,D
Explanation:
Algorithm selection/code must be documented under the Select Modeling Technique task, where teams
"document the actual algorithm/modeling technique to be used" .
Supporting training data pipelines are a core artifact of Phase III: Data Cleansing, which mandates "create a reusable data pipeline to collect, ingest, and prepare data for training purposes" .
Hyperparameter settings are captured in the Hyperparameter Optimization task, where teams "list the final, optimized settings" used for model building .
The bias-variance tradeoff is a conceptual consideration during evaluation but is not a discrete artifact to include in the project deliverables.
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NEW QUESTION # 56
Your team was given a large dataset and has been tasked with organizing the data by type to make better insights from the results. You are facing problems with the approach that the previous project lead used which was a regression algorithm.
What type of algorithm is the best approach for this project?
- A. Binary (or Binomial) Classification
- B. Regression
- C. Multiclass Classification
- D. Clustering
Answer: D
Explanation:
When the goal is to group or organize unlabeled data into meaningful categories, CPMAI specifies the use of unsupervised clustering algorithms-for example, "if it is determined that an unsupervised clustering algorithm such as K-means will be used, the tool should support such needs" . Clustering is the appropriate method for discovering groupings by type.
NEW QUESTION # 57
Recently, you implemented an augmented intelligence application at work to help employees do their job better. However, employees have been resistant to this change and aren't using the application as expected.
What could have been done better to get the team to feel comfortable with this technology and use it? (Select all that apply.)
- A. Ask end users what information and technology they need to help them do their job better and build the tool to help with these pain points.
- B. Provide training for everyone to have all employees feel more comfortable using the technology even if they aren't using the technology yet.
- C. Have the team that built the technology relay to employees this tool is to augment, and not replace their jobs.
- D. Have upper management relay to employees this tool is to augment, and not replace their jobs.
Answer: A,B,C,D
Explanation:
The Continuous Improvement and Respect for People principle in CPMAI stresses involving end users early- gathering their pain points (A), clarifying that AI will augment rather than replace roles (B & C), and providing thorough training to build confidence (D). Engaging stakeholders throughout the project lifecycle and prioritizing user-centered design are key to adoption.
NEW QUESTION # 58
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. Nothing - running automation projects like autonomous projects is the correct thing to do.
- B. Applying a hybrid approach of automation and AI best practices would have achieved better results.
- C. Everyone on the team should understand the differences between automation and autonomous systems.
- D. Senior management should become involved in the project.
Answer: C
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 # 59
Your team is planning an AI-enabled chatbot project to help reduce call center load. They are currently determining if the project can get off the ground and working through the AI Go/No Go feasibility questions.
What stage of CPMAI is the team currently working on?
- A. Phase V
- B. Phase VI
- C. Phase II
- D. Phase I
- E. Phase III
- F. Phase IV
Answer: D
Explanation:
The AI Go/No Go assessment is part of Phase I: Business Understanding under the Cognitive Project Requirements generic task group. In Phase I, teams perform business-feasibility, data-feasibility, and execution-feasibility checks before proceeding with any AI work .
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NEW QUESTION # 60
Your team is working on a project and is running into some issues. You need someone on the team who is able to solve problems in environments of uncertainty, can deal with failure, and has the math and data visualization skills needed to communicate the results with others so the issues can get resolved.
- A. Data Engineer
- B. Project Manager
- C. Data Scientist
- D. Citizen Data Scientist
Answer: C
Explanation:
CPMAI defines a Data Scientist as the role responsible for "formulating data-driven hypotheses, selecting and applying statistical algorithms, interpreting model results, and communicating insights to stakeholders," all of which require critical thinking under uncertainty, advanced mathematics, and strong data-visualization skills .
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NEW QUESTION # 61
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 each pattern as its own project, with their own CPMAI phase iterations, data requirements, and project needs
- B. Run each pattern in isolation, with separate teams
- C. Run it as a hybrid approach and some phases are run separately while other phases are combined together
- D. Run it as one project, combining teams, data requirements, and project needs
Answer: D
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 # 62
You're working with petabytes of data and need to make this dataset more manageable. To do this, you want to reduce the number of variables under consideration. What is the name for this process?
- A. Data selection
- B. Dimensionality Reduction
- C. Gradient Descent
- D. Multivariate regression
Answer: B
Explanation:
The process of reducing a dataset's feature set while retaining its most informative components is formally known as dimensionality reduction. CPMAI describes techniques such as Principal Component Analysis (PCA) and t#distributed Stochastic Neighbor Embedding (t-SNE) under this category, enabling teams to simplify high-dimensional data for more efficient modeling.
NEW QUESTION # 63
During CPMAI Phase II of your project, your team is going through their data collection needs. One team member wants to make use of pre-trained models while another member is adamantly against it.
As the project lead, what should you do?
- A. Evaluate your data and use only what you have and build all models in house.
- B. Evaluate your data and see if using pre-trained models make sense. If so, have the team do research to find the ones that best suit your project.
- C. Evaluate your data and see if using pre-trained models make sense. If so, have the team see what pre- trained models your company already owns and use those.
- D. Have one team build all models in-house and the other team use pre-trained models and see which team' s models perform better.
Answer: B
Explanation:
The Pre-Trained and Third-Party Model Usage task in Phase II: Data Understanding directs teams to first assess whether external or foundation models are appropriate given the current data and objectives. If so, they should then research and select the specific pre-trained models that best align with the project's domain, performance needs, and integration constraints. This ensures suitability before committing to fine-tuning or ensemble strategies.
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NEW QUESTION # 64
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 algorithm selection
- B. Iterative modeling and evaluation
- C. Automatic model selection
- D. Automatic model assessment
- E. Automatic hyperparameter tuning
Answer: B
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 # 65
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. Lack of stakeholder support
- B. Model Drift
- C. An incomplete milestone list
- D. Data Drift
Answer: D
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 # 66
Your team is working on a new facial recognition application. Since this technology has the potential to be mis-used you think it's important to set guidelines for the proper use of this application and you want to make sure the AI system is built for some positive purpose. What area of Trustworthy AI does this best fall under?
- A. Responsible AI
- B. Transparent AI
- C. Explainable AI
- D. Governed AI
Answer: A
Explanation:
Under Domain VI: Trustworthy AI in the CPMAI Exam Content Outline, Responsible AI covers establishing policies, guidelines, and governance that ensure AI solutions are developed for positive, ethical use and prevent misuse. Defining proper-use guidelines and embedding ethical intent into facial recognition directly align with Responsible AI practices .
NEW QUESTION # 67
You have just joined a team and they are working on a new project. The project lead isn't sure what type of technology should be used on this project-AI or a traditional software development approach. What is the best way to determine if you have the criteria for a good AI/ML Project?
- A. Determine the long-term need for the organization and build the project to that long-term goal.
- B. Determine if the project fits within the scope, budget, and timeline set out.
- C. Determine whether the project has a cognitive technology component and meets a short-term need.
- D. Evaluate whether the solution can be done with automation.
Answer: D
Explanation:
During Phase I: Business Understanding, one of the foundational CPMAI tasks is to "determine when to implement automation versus AI," ensuring that rule-based or non-cognitive alternatives are considered first and AI is only selected when those approaches won't suffice.
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NEW QUESTION # 68
You're testing your model and it is overly sensitive to the fluctuations of data and having trouble generalizing.
What type of problem is this?
- A. You are overfitting the data
- B. You are underfitting the data
- C. You have selected the wrong data
- D. You have selected the wrong algorithm
Answer: A
Explanation:
Overfitting occurs when a model learns not only the underlying patterns but also the noise in the training data, causing it to perform well on seen data but poorly on unseen data. The CPMAI Glossary defines overfitting as "a modeling error where a model learns the training data too well, including its noise, resulting in poor performance on new data."
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NEW QUESTION # 69
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