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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q37-Q42):
NEW QUESTION # 37
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. Perform Data Transformation & Multiplication
- B. Perform Data Sampling
- C. Perform Data Anonymization
- D. Purchase the data from a third party
Answer: A
Explanation:
In CPMAI v7's Phase III: Data Preparation, teams are instructed to construct the final modeling dataset through a variety of enhancement activities-including data augmentation, which specifically covers transforming existing records or generating entirely new records to increase volume and variety. This
"augmentation" is described as "constructive data preparation operations such as the production of derived attributes or entire new records, or transformed values for existing attributes" .
Moreover, under the Training & Test Data Requirements task, the Workbook explicitly asks project teams to determine "What transformation or multiplication activities can be done to increase training data volume while maintaining quality" . Performing data transformation (e.g., image rotations, color jitter, cropping) and multiplication (synthetic record generation) directly addresses the lack of labeled samples without incurring the cost or delay of third-party purchases, making option B the correct approach.
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NEW QUESTION # 38
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. Data Drift
- D. An incomplete milestone list
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 .
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NEW QUESTION # 39
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. The use of computing technology to enable machines to gain cognitive intelligence.
- C. 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.
- D. The application of pre-defined rules and algorithms to solve complex problems.
Answer: C
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 # 40
The confusion matrix measures how the algorithm performs for a binary classification activity. As your team is running tests to evaluate model performance, they are seeing the model is incorrectly categorizing flowers as trees. Your model is provided the following:
- A. False Negative results
- B. False Positive results
- C. True Positive results
- D. True Negative results
Answer: B
Explanation:
A false positive occurs when the model predicts the positive class (e.g., "tree") but the actual label is negative (e.g., "flower"). The confusion matrix definition confirms that mislabeling a negative instance as positive maps to the false positive count.
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NEW QUESTION # 41
Enhancing and cleaning data is an important action during which phase of CPMAI?
- A. Phase I
- B. Phase II
- C. Phase IV
- D. Phase VI
- E. Phase III
- F. Phase V
Answer: E
Explanation:
The CPMAI v7 methodology groups all data-centric preparation activities-including both data cleansing ("Clean data") and data augmentation ("Enhance & Augment data")-into Phase III: Data Preparation. In this phase, teams focus squarely on constructing the dataset to be used for modeling by performing all required cleaning, transformation, and enhancement operations.
Phase III: Data Preparation is defined in the Workbook's Table of Contents as covering Data Cleansing & Enhancement tasks ("Clean data" and "Enhance & Augment data") .
Under Phase III, the Generic Task Group: Data Cleansing & Enhancement explicitly lists "Task: Clean data" (bringing data quality to modeling-ready levels) and "Task: Enhance & Augment data" (producing derived attributes and new records) as core activities .
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NEW QUESTION # 42
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