Get CT-AI Dumps : Verified Certified Tester AI Testing Exam

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)"?.

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Facial recognition applications involvecomplex computational tasks, including:

Feature Extraction- Identifying unique facial landmarks.

Model Training and Updates- Continuous learning and adaptation of user data.

Image Processing- Handling real-time image recognition under various lighting and angles.

In this scenario, themobile device is experiencing continuous restarts, which suggestsa resource overloadcaused by excessive processing demands.

Mobile devices have limited computational power.

Unlike servers, mobile devices lack powerful GPUs/TPUs required for deep learning models.

On-device learning is computationally expensive.

The model is likely performingreal-time learning, which can overwhelm the CPU and RAM.

Continuous feedback transmission may cause overheating.

If the system is running multiple processes—training, inference, and network communication—it can overload system resources and cause crashes.

(A) The feedback requires a physical connection and cannot be sent over the Internet.?(Incorrect)

Feedback transmission over the internet is common for cloud-based AI services.This is not the cause of the issue.

(B) Mobile operating systems cannot process machine learning algorithms.?(Incorrect)

Many mobile applications use ML models efficiently. The problem here is thehigh computational intensity, not the OS’s ability to run ML algorithms.

(C) The size of the application is consuming too much of the phone’s storage capacity.?(Incorrect)

Storage issues typically result in installation failures or lag,not device restarts.The issue here isprocessing overload, not storage space.

AI-based applications require significant computational power."The computational intensity of AI-based applications can pose a challenge when deployed on resource-limited devices."?

Edge devices may struggle with processing complex ML workloads."Deploying AI models on mobile or edge devices requires optimization, as these devices have limited processing capabilities compared to cloud environments."?

Why is Option D Correct?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option D is the correct answer, as thecomputational demands of the facial recognition system are too high for the mobile hardware to handle, causing continuous restarts.

The problem describes aclassification modelthat predicts whether a patient is at risk for a stroke. The confusion matrix is provided, and the testers have calculated a performance metric as2/3 or 0.6667.

From theISTQB Certified Tester AI Testing (CT-AI) Syllabus, the definitions of functional performance metrics from a confusion matrix include:

Accuracy:

Accuracy=TP+TNTP+TN+FP+FNAccuracy = \frac{TP + TN}{TP + TN + FP + FN}Accuracy=TP+TN+FP+FNTP+TN?

Measures the proportion of correctly classified instances(both true positives and true negatives)over the total dataset.

If the value is0.6667, it suggests that the metric includesboth correct positive and negative classifications, aligning with accuracy?.

Precision:

Precision=TPTP+FPPrecision = \frac{TP}{TP + FP}Precision=TP+FPTP?

Measures how manypredicted positive caseswere actually positive.

Doesnotmatch the given calculation.

Recall (Sensitivity):

Recall=TPTP+FNRecall = \frac{TP}{TP + FN}Recall=TP+FNTP?

Measures how manyactual positiveswere correctly identified.

Doesnotmatch the 0.6667 value.

F1-Score:

F1=2×Precision×RecallPrecision+RecallF1 = 2 \times \frac{Precision \times Recall}{Precision + Recall}F1=2×Precision+RecallPrecision×Recall?

A balance between precision and recall.

The formula isdifferent from the provided calculation.

Since the formula foraccuracymatches the calculated value of0.6667, the best answer isD. Accuracy?.

Certified Tester AI Testing Study Guide References:

ISTQB CT-AI Syllabus v1.0, Section 5.1 (Confusion Matrix and Functional Performance Metrics)

ISTQB CT-AI Syllabus v1.0, Section 5.4 (Selecting ML Functional Performance Metrics)?

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 isCbecause clustering is the most suitable method for grouping people with similar tastes for targeted product marketing??.

Data mislabeling occurs for several reasons, which can significantly impact the performance of machine learning (ML) models, especially in supervised learning. According to the ISTQB Certified Tester AI Testing (CT-AI) syllabus, mislabeling of data can be caused by the following factors:

Random errors by annotators– Mistakes made due to accidental misclassification.

Systemic errors– Errors introduced by incorrect labeling instructions or poor training of annotators.

Deliberate errors– Errors introduced intentionally by malicious data annotators.

Translation errors– Occur when correctly labeled data in one language is incorrectly translated into another language.

Subjectivity in labeling– Some labeling tasks require subjective judgment, leading to inconsistencies between different annotators.

Lack of domain knowledge– If annotators do not have sufficient expertise in the domain, they may label data incorrectly due to misunderstanding the context.

Complex classification tasks– The more complex the task, the higher the probability of labeling mistakes?.

Among the answer choices provided, "Lack of domain knowledge" (Option A) is the best answer because expertise is essential to accurately labeling data in complex domains such as medical, legal, or engineering fields.

Certified Tester AI Testing Study Guide References:

ISTQB CT-AI Syllabus v1.0, Section 4.5.2 (Mislabeled Data in Datasets)

ISTQB CT-AI Syllabus v1.0, Section 4.3 (Dataset Quality Issues)