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| Section | Objectives |
|---|---|
| Topic 1: Ethics and Risk in AI Testing | - Ethical considerations
|
| Topic 2: Testing AI-Based Systems | - Test design techniques
|
| Topic 3: AI System Lifecycle and Operations | - Continuous learning systems
|
| Topic 4: Machine Learning Fundamentals for Testing | - Model types
|
| Topic 5: Introduction to AI Testing | - AI systems overview
|
| Topic 6: Data Quality and Bias | - Data quality assurance
|
| Topic 7: AI Quality Characteristics | - Quality attributes
|
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NEW QUESTION # 44
"AllerEgo" is a product that uses sell-learning to predict the behavior of a pilot under combat situation for a variety of terrains and enemy aircraft formations. Post training the model was exposed to the real-world data and the model was found to be behaving poorly. A lot of data quality tests had been performed on the data to bring it into a shape fit for training and testing.
Which ONE of the following options is least likely to describes the possible reason for the fall in the performance, especially when considering the self-learning nature of the Al system?
Answer: A
Explanation:
Defining criteria for improvement is a challenge in the acceptance of AI models, but it is not directly related to the performance drop in real-world scenarios. It relates more to the evaluation and deployment phase rather than affecting the model's real-time performance post-deployment.
NEW QUESTION # 45
A company producing consumable goods wants to identify groups of people with similar tastes for the purpose of targeting different products for each group. You have to choose and apply an appropriate ML type for this problem.
Which ONE of the following options represents the BEST possible solution for this above-mentioned task?
SELECT ONE OPTION
Answer: B
Explanation:
A . Regression
Regression is used to predict a continuous value and is not suitable for grouping people based on similar tastes.
B . Association
Association is used to find relationships between variables in large datasets, often in the form of rules (e.g., market basket analysis). It does not directly group individuals but identifies patterns of co-occurrence.
C . Clustering
Clustering is an unsupervised learning method used to group similar data points based on their features. It is ideal for identifying groups of people with similar tastes without prior knowledge of the group labels. This technique will help the company segment its customer base effectively.
D . Classification
Classification is a supervised learning method used to categorize data points into predefined classes. It requires labeled data for training, which is not the case here as we want to identify groups without predefined labels.
Therefore, the correct answer is C because clustering is the most suitable method for grouping people with similar tastes for targeted product marketing.
NEW QUESTION # 46
The stakeholders of a machine learning model have confirmed that they understand the objective and purpose of the model, and ensured that the proposed model aligns with their business priorities. They have also selected a framework and a machine learning model that they will be using.
What should be the next step to progress along the machine learning workflow?
Answer: B
Explanation:
Themachine learning (ML) workflowfollows a structured sequence of steps. Once stakeholders have agreed on theobjectives, business priorities, and the framework/model selection, the next logical step is to prepare and pre-process the databefore training the model.
* Data Preparationis crucial becausemachine learning models rely heavily on the quality of input data. Poor data can result in biased, inaccurate, or unreliable models.
* The process involvesdata acquisition, cleaning, transformation, augmentation, and feature engineering.
* Preparing the dataensures it is in the right format, free from errors, and representative of the problem domain, leading to better generalization in training.
* A (Tune the ML Algorithm):Hyperparameter tuning occursafter the model has been trainedand evaluated.
* C (Agree on Acceptance Criteria):Acceptance criteria should already have been defined in theinitial objective-setting phasebefore framework and model selection.
* D (Evaluate the Framework and Model):The selection of the framework and ML model has already been completed. The next step isdata preparation, not reevaluation.
* ISTQB CT-AI Syllabus (Section 3.2: ML Workflow - Data Preparation Phase)
* "Data preparation comprises data acquisition, pre-processing, and feature engineering.
Exploratory data analysis (EDA) may be performed alongside these activities".
* "The data used to train, tune, and test the model must be representative of the operational data that will be used by the model".
Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Since the model selection is complete, thenext step in the ML workflow is to prepare and pre-process the datato ensure it is ready for training and testing. Thus, thecorrect answer is B.
NEW QUESTION # 47
Which ONE of the following activities is MOST relevant when addressing the scenario where you have more than the required amount of data available for the training?
Answer: D
Explanation:
Data sampling involves selecting a representative subset of the data for training. When there is more data than needed, sampling can be used to create a manageable dataset that maintains the statistical properties of the full dataset.
NEW QUESTION # 48
Which of the following is an example of an input change where it would be expected that the AI system should be able to adapt?
Answer: A
Explanation:
The syllabus explains that input changes that arein the same domainas what was used for training are expected to be handled with adaptability:
"Adaptability refers to the ability of a system to adjust its behavior in response to changes in its environment or inputs. This includes changes to the inputs which are still within the expected operational range of the system, such as resolution changes in images or sensor data."
NEW QUESTION # 49
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