Cognitive Project Management in AI CPMAI v7 - Training & Certification Version: Demo [ Total Questions: 10] Web: www.dumpscafe.com Email: support@dumpscafe.com PMI CPMAI_v7 IMPORTANT NOTICE Feedback We have developed quality product and state-of-art service to ensure our customers interest. If you have any suggestions, please feel free to contact us at feedback@dumpscafe.com Support If you have any questions about our product, please provide the following items: exam code screenshot of the question login id/email please contact us at and our technical experts will provide support within 24 hours. support@dumpscafe.com Copyright The product of each order has its own encryption code, so you should use it independently. Any unauthorized changes will inflict legal punishment. We reserve the right of final explanation for this statement. PMI - CPMAI_v7 Pass Exam 1 of 6 Verified Solution - 100% Result A. B. C. D. A. Category Breakdown Category Number of Questions CPMAI Methodology 1 Data for AI 3 Managing AI 2 Trustworthy AI 3 Machine Learning 1 TOTAL 10 Question #:1 - [CPMAI Methodology] During CPMAI Phase IV: Model Development, which of the following is not done during this phase? Algorithm Selection Model training Model tuning Model Selection Answer: D 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 . ========= Question #:2 - [Data for AI] 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? Phase I PMI - CPMAI_v7 Pass Exam 2 of 6 Verified Solution - 100% Result B. C. D. E. F. A. B. C. D. A. B. Phase II Phase III Phase IV Phase V Phase VI Answer: C 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. ========= Question #:3 - [Managing AI] 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? Data Acquisition / Ingest / Capture Retraining Pipelines Feature Engineering 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. Question #:4 - [Trustworthy AI] You’re working on a project and are working with personally identifiable information (PII). What’s the best approach to take when it comes to collecting and using this data? Use noise reduction techniques to reduce all forms of data noise Implement a new data privacy policy PMI - CPMAI_v7 Pass Exam 3 of 6 Verified Solution - 100% Result C. D. A. B. C. D. A. Store the data in a data warehouse If this data is not needed, use Data anonymization techniques to remove it before feeding to models Answer: D Explanation Under CPMAI Phase III: Data Preparation, the Data Format task includes “Data anonymization” as a core activity to remove or mask PII when it is not required for modeling, thereby protecting privacy while retaining data utility. ========= Question #:5 - [Machine Learning] You have been tasked with creating a model that will recommend products based on what other customers have similarly purchased. Which algorithm is the best choice given this situation? K Nearest Neighbor K-means Neural Network Hyperpersonalization Answer: A Explanation CPMAI’s Generic Task Group: Select Modeling Technique in Phase IV: Model Development outlines common cognitive algorithms. For recommendation systems—which rely on finding similar user or item profiles—the K-Nearest Neighbor algorithm is the canonical choice, using customer purchase vectors to locate “nearest neighbors.” In contrast, K-means is purely unsupervised clustering, Neural Networks are more complex and not necessary for basic collaborative filtering, and Hyperpersonalization is an AI pattern, not an algorithm. ========= Question #:6 - [Trustworthy AI] 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? Biased data sets leading to algorithmic discrimination PMI - CPMAI_v7 Pass Exam 4 of 6 Verified Solution - 100% Result B. C. D. A. B. C. D. A. Generative AI models hallucinating data results Nothing is wrong, algorithmic decisions will never be 100% 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. ========= Question #:7 - [Managing AI] 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? Lack of stakeholder support An incomplete milestone list Data Drift Model Drift Answer: C 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 . ========= Question #:8 - [Data for AI] The team is working to build a data preparation pipeline for the conversational chatbot project. Which phase of CPMAI is this done? PMI - CPMAI_v7 Pass Exam 5 of 6 Verified Solution - 100% Result A. B. C. D. E. F. A. B. C. D. A. Phase I Phase II Phase III Phase IV Phase V Phase VI Answer: C Explanation Phase III: Data Preparation focuses on constructing and documenting reusable data pipelines—including training and inference pipelines—alongside cleansing, augmentation, and labeling tasks to prepare data for modeling . This is where teams build the end-to-end data preparation workflows for AI solutions such as chatbots. Question #:9 - [Data for AI] You’re working on a computer vision application and realize that you do not have enough real world data for the project. You need additional data created to support your training needs. Specifically, the images you need are of people in different poses. What is the best way to obtain this data? Make use of this data by having employees pose in the positions required Make use of data from different departments Make use of this data from surveillance footage Make use of Synthetic Training Data Answer: D Explanation Synthetic data is “artificially generated data that mimics real-world data, used when actual data is scarce or sensitive.” Generating synthetic training images of people in the required poses allows you to rapidly augment your dataset without logistical, privacy, or labeling overhead. ========= Question #:10 - [Trustworthy AI] 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.) Vendor procurement methods PMI - CPMAI_v7 Pass Exam 6 of 6 Verified Solution - 100% Result B. C. D. E. F. G. H. AI System testing requirements Continuous System monitoring Data source identification Algorithm selection Human chain of accountability Key Performance Indicators (KPIs) ROI determination Answer: C D F G 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. ========= About dumpscafe.com dumpscafe.com was founded in 2007. 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