Which ONE of the following approaches to labelling requires the least time and effort?SELECT ONE OPTION
Answer(s): B
Labelling Approaches: Among the options provided, pre-labeled datasets require the least time and effort because the data has already been labeled, eliminating the need for further manual or automated labeling efforts.
ISTQB_CT-AI_Syllabus_v1.0, Section 4.5 Data Labelling for Supervised Learning, which discusses various approaches to data labeling, including pre-labeled datasets, and their associated time and effort requirements.
In a certain coffee producing region of Colombia, there have been some severe weather storms, resulting in massive losses in production. This caused a massive drop in stock price of coffee.Which ONE of the following types of testing SHOULD be performed for a machine learning model for stock-price prediction to detect influence of such phenomenon as above on price of coffee stock.SELECT ONE OPTION
Answer(s): C
Type of Testing for Stock-Price Prediction Models: Concept drift refers to the change in the statistical properties of the target variable over time. Severe weather storms causing massive losses in coffee production and affecting stock prices would require testing for concept drift to ensure that the model adapts to new patterns in data over time.
ISTQB_CT-AI_Syllabus_v1.0, Section 7.6 Testing for Concept Drift, which explains the need to test for concept drift in models that might be affected by changing external factors.
Which ONE of the following types of coverage SHOULD be used if test cases need to cause each neuron to achieve both positive and negative activation values?SELECT ONE OPTION
Coverage for Neuron Activation Values: Sign change coverage is used to ensure that test cases cause each neuron to achieve both positive and negative activation values. This type of coverage ensures that the neurons are thoroughly tested under different activation states.
ISTQB_CT-AI_Syllabus_v1.0, Section 6.2 Coverage Measures for Neural Networks, which details different types of coverage measures, including sign change coverage.
Which ONE of the following describes a situation of back-to-back testing the LEAST?SELECT ONE OPTION
Back-to-back testing is a method where the same set of tests are run on multiple implementations of the system to compare their outputs. This type of testing is typically used to ensure consistency and correctness by comparing the outputs of different implementations under identical conditions. Let's analyze the options given:A . Comparison of the results of a current neural network model ML model implemented in platform A (for example Pytorch) with a similar neural network model ML model implemented in platform B (for example Tensorflow), for the same data.This option describes a scenario where two different implementations of the same type of model are being compared using the same dataset. This is a typical back-to-back testing situation.B . Comparison of the results of a home-grown neural network model ML model with results in a neural network model implemented in a standard implementation (for example Pytorch) for the same data.This option involves comparing a custom implementation with a standard implementation, which is also a typical back-to-back testing scenario to validate the custom model against a known benchmark.C . Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.This option involves comparing two different types of models (a neural network and a decision tree). This is not a typical scenario for back-to-back testing because the models are inherently different and would not be expected to produce identical results even on the same data.D . Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.This option involves comparing the outputs of the same model on slightly different datasets. This could be seen as a form of robustness testing or sensitivity analysis, but not typical back-to-back testing as it doesn't involve comparing multiple implementations.Based on this analysis, option C is the one that describes a situation of back-to-back testing the least because it compares two fundamentally different models, which is not the intent of back-to-back testing.
Which ONE of the following options does NOT describe an Al technology related characteristic which differentiates Al test environments from other test environments?SELECT ONE OPTION
Answer(s): D
AI test environments have several unique characteristics that differentiate them from traditional test environments. Let's evaluate each option:A . Challenges resulting from low accuracy of the models.Low accuracy is a common challenge in AI systems, especially during initial development and training phases. Ensuring the model performs accurately in varied and unpredictable scenarios is a critical aspect of AI testing.B . The challenge of mimicking undefined scenarios generated due to self-learning.AI systems, particularly those that involve machine learning, can generate undefined or unexpected scenarios due to their self-learning capabilities. Mimicking and testing these scenarios is a unique challenge in AI environments.C . The challenge of providing explainability to the decisions made by the system.Explainability, or the ability to understand and articulate how an AI system arrives at its decisions, is a significant and unique challenge in AI testing. This is crucial for trust and transparency in AI systems.D . Challenges in the creation of scenarios of human handover for autonomous systems.While important, the creation of scenarios for human handover in autonomous systems is not a characteristic unique to AI test environments. It is more related to the operational and deployment challenges of autonomous systems rather than the intrinsic technology-related characteristics of AI .Given the above points, option D is the correct answer because it describes a challenge related to operational deployment rather than a technology-related characteristic unique to AI test environments.
Share your comments for ISTQB CT-AI exam with other users:
very helpful
good source
my 3rd test and passed on first try. hats off to this brain dumps site.
please upload it
does anybody know if are these real exam questions?
are these questions similar to actual questions in the exam? because they seem to be too easy
i have a lot of experience but what comes in the exam is totally different from the practical day to day tasks. so i thought i would rather rely on these brain dumps rather failing the exam.
good questions
valied exam dumps. they were very helpful and i got a pretty good score. i am very grateful for this service and exam questions
will it help?
very useful to verify knowledge before exam
good stuffs
question 17 : responses arent b and c ?
just passed the exam on my first try using these dumps.
these questions look good.
this is very helpful content
please provide the dumps
it is amazing
quesion 178 about "a banking system that predicts whether a loan will be repaid is an example of the" the answer is classification. not regresion, you should fix it.
please upload apache spark dumps
q14 is b&c to reduce you will switch off mail for every single alert and you will switch on daily digest to get a mail once per day, you might even skip the empty digest mail but i see this as a part of the daily digest adjustment
i think it is good question
good for students who wish to give certification.
is there a google drive link to the images? the links in questions are not working.
very promising, looks great, so much wow!
i scored 87% on the az-204 exam. thanks! i always trust
good need more
sample questions seems good
huawei is ok
good one nice
please continue
this exam dumps just did the job. i donot want to ruffle your feathers but your exam dumps and mock test engine is amazing.
nice questions