ISTQB CT-AI Exam With Confidence Using Practice Dumps

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)"​.

Setting model and algorithm hyperparameters is an essential step in the machine learning workflow, primarily occurring during the tuning phase.

Evaluating the model (A): This stage involves assessing the model's performance using metrics and does not typically include the setting of hyperparameters.

Deploying the model (B): Deployment is the stage where the model is put into production and used in real-world applications. Hyperparameters should already be set before this stage.

Tuning the model (C): This is the correct stage where hyperparameters are set. Tuning involves adjusting the hyperparameters to optimize the model's performance.

Data testing (D): Data testing involves ensuring the quality and integrity of the data used for training and testing the model. It does not include setting hyperparameters.

Hence, the most appropriate stage of the ML workflow to set model and algorithm hyperparameters isC. Tuning the model.

References:

ISTQB CT-AI Syllabus Section 3.2 on the ML Workflow outlines the different stages of the ML process, including the tuning phase where hyperparameters are set.

Sample Exam Questions document, Question #31 specifically addresses the stage in the ML workflow where hyperparameters are configured.

Back-to-back testing isused to compare two different versions of an ML model, which is precisely what is needed in this scenario.

The model initiallymisclassified dogs as wolvesdue to feature similarities.

Thetest team retrains the modelwith additional images of dogs and wolves.

The best way to verify whether this additional trainingimproved classification accuracyis to compare theoriginal model's output with the newly trained model's output using the same test dataset​.

A (Metamorphic Testing):Metamorphic testing is useful forgenerating new test casesbased on existing ones but does not directly compare different model versions​.

B (Adversarial Testing):Adversarial testing is used to check how robust a model is againstmaliciously perturbed inputs, not to verify training effectiveness​.

C (Pairwise Testing):Pairwise testing is a combinatorial technique for reducing the number of test casesby focusing on key variable interactions, not for validating model improvements​.

ISTQB CT-AI Syllabus (Section 9.3: Back-to-Back Testing)

"Back-to-back testing is used when an updated ML model needs to be compared against a previous version to confirm that it performs better or as expected"​.

"The results of the newly trained model are compared with those of the prior version to ensure that changes did not negatively impact performance"​.

Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:To verify that the model's performance improved after retraining,back-to-back testing is the most appropriate methodas it compares both model versions. Hence, thecorrect answer is D.