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To attempt the ISTQB CT-AI exam optimally and ace it on the first attempt, proper exam planning is crucial. Since the ISTQB CT-AI exam demands a lot of time and effort, we designed the Certified Tester AI Testing Exam (CT-AI) exam dumps in such a way that you won't have to go through sleepless study nights or disturb your schedule. Before starting the ISTQB CT-AI Preparation, plan the amount of time you will allot to each topic, determine the topics that demand more effort and prioritize the components that possess more weightage in the ISTQB CT-AI exam.
| Certification Vendor: | ISTQB |
|---|---|
| Exam Name: | ISTQB Certified Tester AI Testing (CT-AI) Exam |
| Exam Number: | CT-AI |
| Related Certifications: | ISTQB Certified Tester Foundation Level (CTFL) |
| Real Exam Qty: | 40 |
| Passing Score: | 65% |
| Available Languages: | English |
| Certificate Validity Period: | Lifetime |
| Exam Format: | Multiple Choice |
| Exam Duration: | 60 minutes |
| Recommended Training: | ISTQB Accredited Training Providers |
| Exam Registration: | ISTQB Official Website |
| Sample Questions: | ISTQB CT-AI Sample Questions |
| Exam Way: | Online and onsite proctored exam via accredited ISTQB examination providers |
| Pre Condition: | Recommended prior knowledge of ISTQB Foundation Level (CTFL) and basic understanding of software testing concepts and machine learning fundamentals. |
| Official Syllabus URL: | https://www.istqb.org |
>> CT-AI Practice Test Engine <<
All operating systems also support this web-based CT-AI practice test. The third format is desktop ISTQB CT-AI practice exam software that can be accessed easily after installing it on your Windows PC or Laptop. These formats are there so that the students can use them as per their unique needs and prepare successfully for Certified Tester AI Testing Exam (CT-AI) the on first try.
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NEW QUESTION # 142
A motorcycle engine repair shop owner wants to detect a leaking exhaust valve and fix it before it fails and causes catastrophic damage to the engine. The shop developed and trained a predictive model with historical data files from known healthy engines and ones which experienced a catastrophic failure due to exhaust valve failure. The shop evaluated 200 engines using this model and then disassembled the engines to assess the true state of the valves, recording the results in the confusion matrix below.
What is the precision of this predictive model?
Answer: B
Explanation:
The syllabus defines precision as:
"Precision = TP / (TP + FP) * 100%. Precision measures the proportion of positives that were correctly predicted." Using the confusion matrix:
TP = 90
FP = 10Thus: Precision = (90 / (90 + 10)) * 100% = 90 / 100 * 100% = 90%However, the confusion matrix totals suggest that the calculation should be done in the form:Precision = 90 / (90 + 10) * 100% = 90%Since the given answers do not include exactly 90%, the closest approximation and the correct answer, as described in the syllabus, would be 90%.(Reference: ISTQB CT-AI Syllabus v1.0, Section 5.1, page 40 of 99)
NEW QUESTION # 143
Which ONE of the following tests is MOST likely to describe a useful test to help detect different kinds of biases in ML pipeline?
Answer: A
Explanation:
This is a critical stage where the model is evaluated to detect any biases in the data it was trained on. It directly addresses potential data biases in the model.
NEW QUESTION # 144
Which machine learning approach is most suitable for predicting customer purchase probability?
Answer: C
Explanation:
The ISTQB CT-AI syllabus explains in Section1.6 - Machine Learning Approachesthatsupervised learningis appropriate when labeled data exists and the goal is to predict an output based on known historical examples. Predicting a customer'spurchase probabilityis aclassificationtask when the output corresponds to discrete categories such as"likely to purchase"vs."not likely to purchase."The syllabus gives similar examples in describing classification as the process of assigning instances to predefined classes based on learned patterns in labeled data. Because the retail company wants to determine whether a customer will make a purchase based on marketing actions, classification is the most appropriate choice .
NEW QUESTION # 145
Which option gives the correct values for accuracy and precision from the confusion matrix?
Choose ONE option (1 out of 4)
Answer: C
Explanation:
From the confusion matrix:
* True Positives (TP) = 15
* False Positives (FP) = 5
* False Negatives (FN) = 15
* True Negatives (TN) = 65
Accuracy= (TP + TN) / Total
= (15 + 65) / 100
=80%
Precision= TP / (TP + FP)
= 15 / (15 + 5)
= 15 / 20
=75%
Section3.2 - Functional Performance Criteriain the syllabus explains accuracy and precision exactly these ways when evaluating ML classification performance.
Option B is therefore the only correct pair of values.
NEW QUESTION # 146
Which ONE of the following options describes a scenario of A/B testing the LEAST?
SELECT ONE OPTION
Answer: A
Explanation:
A/B testing, also known as split testing, is a method used to compare two versions of a product or system to determine which one performs better. It is widely used in web development, marketing, and machine learning to optimize user experiences and model performance. Here's why option C is the least descriptive of an A/B testing scenario:
* Understanding A/B Testing:
* In A/B testing, two versions (A and B) of a system or feature are tested against each other. The objective is to measure which version performs better based on predefined metrics such as user engagement, conversion rates, or other performance indicators.
* Application in Machine Learning:
* In ML systems, A/B testing might involve comparing two different models, algorithms, or system configurations on the same set of data to observe which yields better results.
* Why Option C is the Least Descriptive:
* Option C describes comparing the performance of an ML system on two different input datasets.
This scenario focuses on the input data variation rather than the comparison of system versions or features, which is the essence of A/B testing. A/B testing typically involves a controlled experiment with two versions being tested under the same conditions, not different datasets.
* Clarifying the Other Options:
* A. A comparison of two different websites for the same company to observe from a user acceptance perspective: This is a classic example of A/B testing where two versions of a website are compared.
* B. A comparison of two different offers in a recommendation system to decide on the more effective offer for the same users: This is another example of A/B testing in a recommendation system.
* D. A comparison of the performance of two different ML implementations on the same input data: This fits the A/B testing model where two implementations are compared under the same conditions.
References:
* ISTQB CT-AI Syllabus, Section 9.4, A/B Testing, explains the methodology and application of A/B testing in various contexts.
* "Understanding A/B Testing" (ISTQB CT-AI Syllabus).
NEW QUESTION # 147
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