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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Identify Data Needs | 26% | - Ensure data quality, privacy, security, and compliance - Plan data collection, storage, and infrastructure - Define data requirements and sources |
| Topic 2: Manage AI Model Development and Evaluation | 16% | - Oversee model design, training, and validation - Monitor performance, accuracy, and reliability - Address model drift, explainability, and limitations |
| Topic 3: Identify Business Needs and Solutions | 26% | - Align AI initiatives with organizational strategy - Define requirements, scope, and success criteria - Evaluate feasibility and value of AI solutions |
| Topic 4: Support Responsible and Trustworthy AI Efforts | 15% | - Manage bias, risk, compliance, and societal impact - Establish ethical and governance frameworks - Ensure fairness, transparency, accountability |
| Topic 5: Operationalize AI Solution | 17% | - Establish monitoring, maintenance, and improvement processes - Manage change, adoption, and governance post-launch - Deploy AI systems into production |
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NEW QUESTION # 207
Major factors for the project you are currently working on is around the training time, cost, and complexity of training your models. Which algorithm is not the best choice given these constraints?
Answer: D
NEW QUESTION # 208
An aerospace company's project team is evaluating data quality before preparing data for AI models to predict maintenance needs. They are facing challenges with streaming data. If the project team were dealing with batch data, how would the result be different?
Answer: A
Explanation:
PMI-CPMAI emphasizes defining data needs with attention to data types/formats, and especially temporal and granularity requirements, because these drive how data must be collected, processed, and governed. Streaming data introduces continuous inflow, near-real-time processing, and greater operational complexity for validation, monitoring, and pipeline reliability.
By contrast, batch data arrives in discrete, scheduled loads (e.g., nightly dumps), which generally makes it easier to control the ingestion window, validate completeness, reconcile anomalies, and correct issues before data is used for model training or scoring. This aligns with PMI's expectation that teams define data flow and processing requirements and set acceptance criteria for data quality--activities that are typically simpler when inflow is periodic rather than continuous. In CPMAI practice, batch processing also supports stronger governance checkpoints: teams can run standardized quality checks, maintain versioning of datasets, and document preprocessing steps more consistently--helpful for auditability and accountability. While batch data can still contain conflicts or inconsistencies, those issues are not inherently "greater" than streaming; the key difference is that batch ingestion tends to be more manageable operationally because timing and volume are more predictable.
NEW QUESTION # 209
An organization is planning their digital transformation initiatives by building an AI solution to focus on data-collection needs. The goal is to reduce the manual handling of data. Which approach should be prioritized to achieve the objective?
Answer: B
Explanation:
Implementing intelligent systems that can autonomously process and analyze data directly reduces manual data handling. This supports the digital transformation goal by automating data collection, processing, and analysis through AI-enabled capabilities.
NEW QUESTION # 210
In a logistics project, a company is integrating an AI-driven supply chain optimization tool. The project manager needs to address the risk of external dependencies affecting project success.
Which method is a next step for the project manager?
Answer: C
Explanation:
Establishing contingency plans for critical external dependencies helps the project manager prepare fallback actions if vendors, data providers, platforms, integrations, or infrastructure services create delays or disruptions. This directly reduces the impact of external dependency risks on the AI supply chain optimization project.
NEW QUESTION # 211
A fraud detection model is evaluated on a dataset in which 0.4 percent of transactions are fraudulent. The model reports 99.6 percent accuracy.
How should the project manager interpret this result?
Answer: B
Explanation:
With a severely imbalanced class distribution, a model that predicts the majority class for every record would achieve similar accuracy while detecting no fraud. Precision, recall, and F1 score reveal whether the model actually identifies the minority class of interest.
NEW QUESTION # 212
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