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NEW QUESTION # 16
A project manager is reviewing the performance of an AI model used for predictive analytics in sales. The model's accuracy is within acceptable limits; however, its precision is low. What is the cause for the precision issue?
- A. The model is underfitting the validation data.
- B. The feature selection process is flawed.
- C. The model is overfitting the training data.
- D. The training data is unbalanced.
Answer: D
Explanation:
Unbalanced training data can cause the model to favor the majority class and produce more false positives for the target outcome. This lowers precision because fewer of the model's positive predictions are actually correct.
NEW QUESTION # 17
A project team is working on an AI initiative that requires ensuring the explainability and transparency of their selected algorithms. They have identified the business requirements and stakeholders. What is an effective way to address the project objectives?
- A. Implementing a chain-of-thought prompting technique
- B. Adopting a principle of least privilege for data access
- C. Using a rule-based approach to maintain simplicity
- D. Leveraging generative AI for advanced data analysis
Answer: C
Explanation:
Using a rule-based approach supports explainability and transparency because the decision logic is explicit, traceable, and easier for stakeholders to understand. This makes it suitable when the project requires clear reasoning behind algorithmic outputs.
NEW QUESTION # 18
A project manager is overseeing the quality assurance and quality control of an AI/machine learning (ML) model. The model has been trained and initial tests have shown promising results.
However, the project manager is concerned about the long-term performance and reliability of the model in real-world scenarios. What should the project manager do?
- A. Implement additional data augmentation techniques.
- B. Establish continuous monitoring and feedback loops.
- C. Set up cross-validation with a larger dataset.
- D. Perform a comprehensive hyperparameter tuning.
Answer: B
Explanation:
Establishing continuous monitoring and feedback loops helps track model performance after deployment, detect drift or degradation, and support timely updates. This is essential for maintaining long-term reliability in real-world conditions.
NEW QUESTION # 19
A project manager meets with a customer for initial discussions about an upcoming project. At the end of the meeting, the customer asks the project manager for a rough estimate of the project duration. Based on her experience with three similar projects, the project manager provides an estimate of 8-10 months.
What's wrong with this timeframe?
- A. It's underestimating the project timeline by 3 months
- B. It's not accounting for data preparation timelines
- C. It fits into a waterfall timeframe, but not an agile project timeframe
- D. It's not accounting for potential project delays
Answer: D
NEW QUESTION # 20
An IT services company is working on a project to develop an AI-based customer support system.
During data preparation, the project manager needs to clean and transform customer interaction logs. What is an effective technique to handle any missing data?
- A. Ignore missing data if it seems insignificant.
- B. Fill missing values with zeros without analysis.
- C. Remove records with missing values if minimal.
- D. Duplicate existing data to fill in missing gaps.
Answer: C
Explanation:
Removing records with missing values can be effective when the amount of missing data is minimal and its removal will not distort the dataset. This helps maintain clean and reliable customer interaction logs before transformation and model training.
NEW QUESTION # 21
Using machine learning and other cognitive approaches to understand how to take past / existing behavior and predict future outcomes or help humans make decisions about future outcomes using insight learned from past behavior / interactions / data is a core part to which pattern(s) of AI?
- A. Recognition Pattern
- B. Predictive Analytics & Decision Support
- C. Predictive Analytics & Decision Support and Patterns and Anomalies
- D. Goal Driven Systems
Answer: B
Explanation:
Using machine learning to analyze past behavior and predict future outcomes or assist decision- making is central to the Predictive Analytics & Decision Support pattern in AI.
NEW QUESTION # 22
One of the key elements of a data-centric methodology is the data requirements phase. During CPMAI Phase II, several unexpected issues have developed and are now threatening the data collection efforts.
What course of action might make the issue worse?
- A. See if you already have access to enough data to continue with the project
- B. See if you can purchase the data needed to continue with the project
- C. See if you can adjust the scope of this interaction to continue with the project
- D. See if you can expand the scope to continue with the project
Answer: C
NEW QUESTION # 23
A project manager is evaluating the readiness of the team for an AI operationalization project.
They have identified a lack of experience with AI and data knowledge. If this issue is neglected, what is the outcome?
- A. The project will face significant delays due to learning curves.
- B. The project will proceed smoothly with existing knowledge.
- C. The project will achieve its objectives with external consultant help.
- D. The project will require frequent adjustments to the AI models.
Answer: A
Explanation:
A lack of AI and data knowledge creates skill gaps that slow down planning, data preparation, model validation, deployment, and troubleshooting. If not addressed, the team will likely experience delays as they learn required concepts and practices during execution.
NEW QUESTION # 24
You have been tasked at your organization to manage a large language model (LLM) project.
Identify what LLMs are useful for. (Select all that apply.)
- A. Machine Translation
- B. Code generation
- C. Classify and categorize content
- D. Improve search quality
- E. Text summarization
- F. Process automation
Answer: A,B,C,D,E,F
NEW QUESTION # 25
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 IV
- C. Phase I
- D. Phase III
- E. Phase II
- F. Phase VI
Answer: C
Explanation:
Phase I of CPMAI focuses on business understanding, including assessing AI project feasibility through Go/No Go decisions before development begins.
NEW QUESTION # 26
During the configuration management of an AI/machine learning (ML) model, the team has observed inconsistent performance metrics across different test datasets. What will cause the inconsistency issue?
- A. Overfitting the training data
- B. Incorrect data preprocessing steps
- C. Insufficient model complexity
- D. Low variance in the test results
Answer: A
Explanation:
Overfitting causes the model to learn patterns that are too specific to the training data, so performance may look strong in one dataset but become inconsistent when tested against different datasets.
NEW QUESTION # 27
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 interpret AI results so that cause and effect can be represented
- B. Provide a means to have contestability of the algorithm selected
- 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: A
Explanation:
When algorithms lack inherent explainability, providing interpretable AI results helps users understand cause and effect, improving trust and transparency.
NEW QUESTION # 28
A project team is evaluating whether an AI initiative should proceed beyond discovery.
Stakeholders are aligned on objectives, but the team has not confirmed data access, quality, or legal constraints.
What is the most appropriate next action?
- A. Move directly to deployment planning
- B. Purchase additional compute infrastructure
- C. Conduct a go/no-go assessment using readiness criteria
- D. Begin model development using sample data
Answer: C
Explanation:
PMI-CPMAI explicitly includes conducting AI go/no-go assessments as a gated decision mechanism to determine whether conditions are sufficient to proceed. In CPMAI-aligned practice, stakeholder alignment on objectives is necessary but not sufficient; readiness must also cover data availability, permissions, privacy/legal constraints, and the feasibility of meeting acceptable performance metrics. A go/no-go assessment brings these prerequisites into a structured review, allowing the project manager to document assumptions, identify critical gaps (e.g., data rights, retention limits, PII handling), and decide whether to proceed, pivot, or stop before incurring avoidable cost and rework.
NEW QUESTION # 29
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. Data Acquisition / Ingest / Capture
- C. Retraining Pipelines
- D. ELT Pipeline
Answer: C
Explanation:
Retraining pipelines are designed to handle continuous model iteration by automating data updates, model retraining, and deployment to maintain model performance over time.
NEW QUESTION # 30
A capital markets firm is exploring the use of AI to enhance its trading algorithms. The firm expects the AI solution will increase trading accuracy and profitability. The project manager needs to create a business case to justify the AI investment. Which method will provide results that meet the firm's goals and objectives?
- A. Conducting a market trend analysis.
- B. Performing a scenario analysis
- C. Consulting with AI vendors
- D. Developing a financial impact assessment
Answer: D
Explanation:
Developing a financial impact assessment quantifies the expected costs, benefits, profitability improvements, and return on investment from the AI trading solution. This directly supports the business case by showing whether the investment aligns with the firm's goals for increased trading accuracy and financial performance.
NEW QUESTION # 31
You're running an image recognition project and realize that you do not have enough data of a certain type of vehicle. What is the best course of action to get the additional labeled data you need?
- A. Purchase the data from a third party
- B. Perform Data Transformation & Multiplication
- C. Perform Data Anonymization
- D. Perform Data Sampling
Answer: B
NEW QUESTION # 32
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
NEW QUESTION # 33
Data Engineering is 80%+ of most AI projects, so building a good Data Engineering Environment is key to AI Project Success. As the manager of this project, you need to make sure you have correct staffing needs. What's the most critical role to staff for in the Big Data / Data Engineering Environment?
- A. Senior management
- B. Data Engineering
- C. Data Engineering and Data Scientists
- D. Data Scientists
- E. All roles are critical to staff in the Four different AI Tech environments
Answer: B
Explanation:
Data engineering is critical for building and maintaining pipelines and infrastructure, which form the foundation for successful AI projects, making data engineers essential in the environment.
NEW QUESTION # 34
You're working with an inexperienced team and this is all their first AI project. You're trying to work on a supervised learning binary classification problem to determine if emails are spam or not. What is the best approach for this project?
- A. Pick an ensemble method since you're not sure which algorithm will perform best
- B. Pick a simple algorithm such as Gaussian mixture
- C. Pick a simple algorithm such a naive bayes
- D. Pick a neural network algorithm since you know this works well for supervised learning approaches
Answer: C
Explanation:
For a supervised binary classification problem like spam detection, starting with a simple, well- understood algorithm like naive Bayes is best for an inexperienced team due to its ease of implementation and effectiveness on text classification tasks.
NEW QUESTION # 35
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