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NEW QUESTION # 23
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. Hyperparameter settings
- B. Supporting training data
- C. The algorithm code
- D. Bias-variance tradeoff
Answer: A,B,C
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 # 24
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. Provide training for everyone to have all employees feel more comfortable using the technology even if they aren't using the technology yet.
- B. 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.
- C. Have upper management relay to employees this tool is to augment, and not replace their jobs.
- D. Have the team that built the technology 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 # 25
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 # 26
Your organization wants to keep an eye on AI systems for Governance purposes. What are the most crucial things to consider? (Select all that apply.)
- A. Human chain of accountability
- B. ROI determination
- C. Key Performance Indicators (KPIs)
- D. AI System testing requirements
- E. Data source identification
- F. Continuous System monitoring
- G. Vendor procurement methods
- H. Algorithm selection
Answer: A,C,E,F
Explanation:
Continuous System monitoring (C): Phase VI's "Monitoring and maintenance plan" requires teams to define
"What continuous monitoring and management approach and tools will be used for the model in this iteration?" to ensure the model continues to provide expected results in operation .
Data source identification (D): In Phase II: Data Understanding, teams must "Describe Data," including "Data source formats" and "Training data identification," to maintain visibility into where the model's inputs originate-essential for governance and troubleshooting .
Human chain of accountability (F): The "Model Governance Framework" task directs project teams to document "Determination of Governance Team," identifying members who will serve as the "owners" of the model and be responsible for its usage, soliciting feedback, and addressing concerns-establishing a clear accountability structure .
Key Performance Indicators (KPIs) (G): Domain V's "KPI Measurement" task mandates that teams "Align model performance with business key performance indicators" and implement ongoing KPI evaluation as part of quality assurance, providing the metrics by which governance bodies assess model health and business impact .
Options A, B, E, and H fall outside the core ongoing governance activities defined in CPMAI v7. Continuous monitoring of deployed models, clear data lineage, defined human accountability, and KPI tracking are the pillars of robust AI governance.
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NEW QUESTION # 27
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 as its own project, with their own CPMAI phase iterations, data requirements, and project needs
- D. Run each pattern in isolation, with separate teams
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 # 28
When looking to implement AI to help break the Digital Transformation logjam, it's important to:
- A. Have the right culture for AI
- B. Figure out which pattern(s) of AI are needed to incorporate intelligent systems into non-digital processes
- C. Invest in technology solutions to solve the problem
- D. Start with basic automation and introduce AI only as needed
Answer: A
Explanation:
CPMAI emphasizes that the largest barriers to AI adoption are organizational and cultural, not technical. In Phase I's Assess Situation task group, teams inventory not only tools and data but also resources, constraints, and the cultural readiness for AI-ensuring leadership and staff embrace data-driven decision-making and experimentation. Without the right AI culture in place, even the best technology investments fail to deliver value .
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NEW QUESTION # 29
Which of the following best describes the technical definition of Machine Learning?
- A. An approach to using increasing levels of intelligence to solve greater cognitive needs from unintelligent automation to autonomous business process.
- B. 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.
- C. The application of pre-defined rules and algorithms to solve complex problems.
- D. The use of computing technology to enable machines to gain cognitive intelligence.
Answer: B
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 # 30
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?
- A. Phase I
- B. Phase IV
- C. Phase II
- D. Phase VI
- E. Phase III
- F. Phase V
Answer: E
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 # 31
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 have selected the wrong data
- B. You have selected the wrong algorithm
- C. You are underfitting the data
- D. You are overfitting the data
Answer: D
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 # 32
In the case that an algorithm you want to use isn't algorithmically explainable, AI systems should try to do the following:
- A. Provide a means to have contestability of the algorithm selected
- B. Provide a means to interpret AI results so that cause and effect can be represented.
- C. Provide a means to reverse-engineer the algorithm to inspect its performance
- D. Provide a means to have a different team on the project
Answer: B
Explanation:
Under Required AI Explainability Considerations, CPMAI mandates that when a chosen model is a "black- box" with limited native interpretability, teams must implement post-hoc interpretability techniques (e.g., feature#importance plots, surrogate models) to "interpret AI results so that cause and effect can be represented," ensuring stakeholders understand why the model makes its predictions.
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NEW QUESTION # 33
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. Cautiously move forward knowing you may need to pause mid-project which is ok.
- B. Move ahead as planned so you stay on time with your project.
- C. 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.
- D. Do not move forward until you have access to all the data you need.
Answer: D
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 # 34
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. Multivariate regression
- B. Gradient Descent
- C. Data selection
- D. Dimensionality Reduction
Answer: D
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 # 35
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. RPA is not the right solution to this problem
- D. Augmented Intelligence
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 # 36
A team is getting ready to begin working on a ML project. They need to build a data preparation pipeline and someone on the team suggests they reuse the same pipeline they created for their last project.
What's wrong with this suggestion?
- A. Pipelines are pattern and model need specific.
- B. There is no issue. Pipelines can be reused as needed between projects.
- C. Pipelines are model operationalization need specific.
- D. Pipelines are pattern needs specific so as long as it's the same pattern then you can reuse the pipeline.
Answer: A
Explanation:
In Phase III: Data Preparation, CPMAI specifies that data pipelines must be designed to address the specific modeling pattern and model requirements of the current project. Even if two projects use similar ingestion or cleaning steps, the pipeline must be tailored for the exact feature transformations, label mappings, and data schemas of the new model. Therefore, pipelines are pattern- and model-specific, and blindly reusing one from a prior project without adaptation will likely break downstream model training or inference requirements.
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NEW QUESTION # 37
During CPMAI Phase IV: Model Development, which of the following is not done during this phase?
- A. Model tuning
- B. Algorithm Selection
- C. Model Selection
- D. Model training
Answer: C
Explanation:
The Phase IV: Model Development generic tasks include:
Select Modeling Technique (algorithm selection)
Generate model test design
Model Training / Model Building
Hyperparameter Optimization (model tuning)
Final Model Selection (choosing the best candidate against business criteria) is performed in Phase V: Model Evaluation, not in Phase IV .
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NEW QUESTION # 38
Upper management is looking to roll out a new product and wants to see if there are any patterns and insights that can be discovered from customer data. Your team has been tasked to discover these potential patterns and structures within this data.
Which type of machine learning approach would be most appropriate to pick for this problem?
- A. Supervised Learning
- B. Unsupervised Learning
- C. Reinforcement Learning
- D. All would work equally well
Answer: B
Explanation:
When the goal is to uncover hidden structures or groupings in unlabeled data, unsupervised learning-notably clustering algorithms-is the appropriate choice. CPMAI describes clustering as "an unsupervised process that partitions data into groups based on similarity" and calls for applying these methods to discover patterns in unlabeled datasets .
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NEW QUESTION # 39
Your team is working on a new loan decision model that takes a number of factors and data points into consideration and then automatically approves or denies a loan. After a month in operation someone does a review and notices that the system is denying a large number of loans from a certain demographic when all other factors from people in other regions (such as age, salary, and credit score) are the same.
What is most likely happening here?
- A. Biased data sets leading to algorithmic discrimination
- B. Generative AI models hallucinating data results
- C. Nothing is wrong, algorithmic decisions will never be 100%
- D. Data privacy issues leading to data sharing concerns
Answer: A
Explanation:
When training data under-represents or skews certain groups, the resulting model can systematically discriminate against those groups-a phenomenon termed algorithmic discrimination in the CPMAI Glossary.
Such bias in outcomes arises directly from biased training data.
Moreover, the CPMAI Exam Content Outline emphasizes that Trustworthy AI must apply laws pertaining to AI ethics, bias, and fairness to detect and remediate these issues early in the lifecycle.
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NEW QUESTION # 40
You recently completed an image recognition project at your company that was focused on identifying different types of cars. You have now been assigned a new image recognition project that is focused on identifying different types of animals. You know you can shortcut model development by using a specific technique.
What is this technique called?
- A. Pre-Trained Models
- B. Generative AI
- C. Transfer Learning
- D. Reinforcement Learning
Answer: C
Explanation:
Transfer learning is defined as a technique that "leverages a pretrained model (trained on a large, relevant data set) as a starting point for a new, related task," allowing you to reuse learned representations (e.g., convolutional layers trained on one image domain) when training on another domain (like cars # animals) .
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NEW QUESTION # 41
Your team is tasked with selecting an algorithm for a supervised learning classification project. Which algorithm might you choose?
- A. K-nearest neighbor
- B. Q learning
- C. K-means
- D. Gaussian mixture
Answer: A
Explanation:
K-nearest neighbor (KNN) is a classic supervised classification algorithm that assigns a class label based on the majority label of the K closest training samples in feature space. In contrast, Gaussian Mixture Models and K-means are unsupervised clustering techniques, and Q-learning is a reinforcement-learning algorithm- not a supervised classifier .
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NEW QUESTION # 42
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. Retraining Pipelines
- B. Feature Engineering
- C. ELT Pipeline
- D. Data Acquisition / Ingest / Capture
Answer: A
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 # 43
A team has started working on their first AI project and they are running this project like a traditional software development project. About two months into the project the team is hitting some major issues, and you're tasked with coming in to help manage this project. Immediately you realize that AI projects need to be treated like data-centric projects.
What's the next best course of action?
- A. Bring in data centric methodology best practices to get this project back on track
- B. Hire an entirely new team making sure there is at least one data scientist on this new team
- C. Hire an outside consulting firm to handle the technical aspects while you train the team yourself on data centric best practices
- D. Get the existing team up to speed and make sure existing Agile approaches can support the AI effort
Answer: A
Explanation:
Domain II of the CPMAI Exam Content Outline highlights the need to "adapt traditional methodologies for data-centric projects" and "implement continuous AI project lifecycles" rather than treating AI as conventional software development. Bringing in CPMAI's data-centric best practices-phased, iterative, and focused on data understanding/preparation-directly addresses the root causes of AI project failures and realigns the team to proven AI project management frameworks.
NEW QUESTION # 44
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. Find a source of already labeled data
- B. Hire a Contractor Workforce
- C. Get your Users to Do it
- D. Contract with Third Party Data Labeling Firms
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 .
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NEW QUESTION # 45
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 # 46
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. You're too small of an organization to be worried about laws at the moment
- 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 the United States
- D. Make sure to follow relevant data, privacy, and other important laws as it pertains to NYC
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 # 47
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 I
- B. Phase II
- C. Phase IV
- D. Phase VI
- E. Phase V
- F. Phase III
Answer: C
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 # 48
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PMI CPMAI_v7 Exam Syllabus Topics:
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