CT-AI Study Group & Latest CT-AI Dumps Ebook

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ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • ML: Data: This section of the exam covers explaining the activities and challenges related to data preparation. It also covers how to test datasets create an ML model and recognize how poor data quality can cause problems with the resultant ML model.
Topic 2
  • Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.
Topic 3
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 4
  • systems from those required for conventional systems.
Topic 5
  • Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.
Topic 6
  • Introduction to AI: This exam section covers topics such as the AI effect and how it influences the definition of AI. It covers how to distinguish between narrow AI, general AI, and super AI; moreover, the topics covered include describing how standards apply to AI-based systems.
Topic 7
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 8
  • Testing AI-Specific Quality Characteristics: In this section, the topics covered are about the challenges in testing created by the self-learning of AI-based systems.
Topic 9
  • Machine Learning ML: This section includes the classification and regression as part of supervised learning, explaining the factors involved in the selection of ML algorithms, and demonstrating underfitting and overfitting.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q17-Q22):

NEW QUESTION # 17
You are a test manager planning testing for an invoice financing company. The company buys unpaid invoices from companies and provides them with immediate cash.
The company is replacing their existing conventional system, which takes company accounting records as inputs, with an ML system that classifies each invoice for sales as something that should, or should not be bought. Significant historical production data is available. It is important that invoices are not bought incorrectly.
Which ONE of the following test techniques would be MOST appropriate for you to plan for system testing?

Answer: A

Explanation:
Back-to-back testing is the most appropriate technique in this scenario. It involves comparing the outputs of the new machine learning system with the outputs of the existing conventional system, using the same input data. This approach allows to verify that the ML system performs at least as well as the existing system, particularly in avoiding incorrect purchases of invoices, which is crucial for the company's operations.


NEW QUESTION # 18
Which ONE of the following options describes a scenario of A/B testing the LEAST?

Answer: C

Explanation:
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.


NEW QUESTION # 19
Which ONE of the below statements BEST describes why test environments for autonomous systems might need to be different to other test environments?

Answer: A

Explanation:
Tools may be required to simulate extreme scenarios because autonomous systems often need to be tested under challenging or rare conditions that are difficult to replicate in a real-world environment. These scenarios, such as extreme weather, system failures, or unexpected behaviors, need to be simulated in a controlled environment to ensure the system can handle a wide range of situations safely and effectively. This makes the test environment for autonomous systems unique compared to other types of testing.


NEW QUESTION # 20
ln the near future, technology will have evolved, and Al will be able to learn multiple tasks by itself without needing to be retrained, allowing it to operate even in new environments. The cognitive abilities of Al are similar to a child of 1-2 years.
In the above quote, which ONE of the following options is the correct name of this type of Al?

Answer: C

Explanation:
General AI, or strong AI, has the ability to understand, learn, and apply knowledge across a wide range of tasks, similar to human cognitive abilities. It aligns with the description of AI that can learn multiple tasks and operate in new environments without needing retraining.


NEW QUESTION # 21
Which of the following is a dataset issue that can be resolved using pre-processing?

Answer: D

Explanation:
Pre-processing is an essential step in data preparation that ensures data is clean, formatted correctly, and structured for effective machine learning (ML) model training. One common issue that can be resolved during pre-processing isnumbers stored as strings.
Explanation of Answer Choices:
* Option A: Insufficient data
* Incorrect. Pre-processing cannot resolve insufficient data. If data is lacking, techniques like data augmentation or external data collection are needed.
* Option B: Invalid data
* Incorrect. While pre-processing can identify and handle some forms of invalid data (e.g., missing values, duplicate entries), it does not resolve all invalid data issues. Some cases may require domain expertise to determine validity.
* Option C: Wanted outliers
* Incorrect. Pre-processing usually focuses on handling unwanted outliers. Wanted outliers may need to be preserved, which is more of a data selection decision rather than pre-processing.
* Option D: Numbers stored as strings
* Correct. One of the key functions of data pre-processing isdata transformation, which includes converting incorrectly formatted data types, such as numbers stored as strings, into their correct numerical format.
ISTQB CT-AI Syllabus References:
* Data Pre-Processing Steps:"Transformation: The format of the given data is changed (e.g., breaking an address held as a string into its constituent parts, dropping a field holding a random identifier, converting categorical data into numerical data, changing image formats)".


NEW QUESTION # 22
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