CT-AI Exam Dumps : Certified Tester AI Testing Exam

 A. This statement is incorrect. Current AI is true AI and there is no reason to believe that this fact will change over time.

AI is an evolving field, and the definition of what constitutes AI can change as technology advances.

 B. This statement is correct. In general, what is considered AI today may change over time.

The term AI is dynamic and has evolved over the years. What is considered AI today might be viewed as standard computing in the future. Historically, as technologies become mainstream, they often cease to be considered "AI".

 C. This statement is incorrect. What is considered AI today will continue to be AI even as technology evolves and changes.

This perspective does not account for the historical evolution of the definition of AI. As new technologies emerge, the boundaries of AI shift.

 D. This statement is correct. In general, today the term AI is utilized incorrectly.

While some may argue this, it is not a universal truth. The term AI encompasses a broad range of technologies and applications, and its usage is generally consistent with current technological capabilities.

Adeep neural network (DNN)is a type of artificial neural network that consists of multiple layers between the input and output layers. TheISTQB Certified Tester AI Testing (CT-AI) Syllabusoutlines the following characteristics of a DNN:

Structure of a Deep Neural Network:

ADNN comprises at least three types of layers:

Input layer: Receives the input data.

Hidden layers: Perform complex feature extraction and transformations.

Output layer: Produces the final prediction or classification​.

Analysis of Answer Choices:

A (Only input and output layers)→ Incorrect, as a DNN must haveat least one hidden layer.

B (At least one internal hidden layer)→ Correct, as a neural network must havehidden layersto be considered deep.

C (Minimum of five layers required)→ Incorrect, asthere is no strict definitionthat requires at least five layers.

D (Output layer is not connected to other layers)→ Incorrect, asthe output layer must be connectedto the hidden layers​.

Thus,Option B is the correct answer, asa deep neural network must have at least one hidden layer.

Certified Tester AI Testing Study Guide References:

ISTQB CT-AI Syllabus v1.0, Section 6.1 (Neural Networks and Deep Neural Networks)

ISTQB CT-AI Syllabus v1.0, Section 6.2 (Structure of Deep Neural Networks)​.

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)"​.