CSA TAISE Dumps in PDF and Online - Page: 4

QUESTION: 9

What is a key challenge that affects synthetic data generation techniques?

Needs a lot of computing power and time to create compared to just using real data sets
Often makes training data that is lower quality and hurts how well the model can perform
May miss rare events or complex patterns that do not show up often in the original data
Can leak private details if the system remembers too much from the real data it learned from

Answer(s): C

Explanation:

A key challenge of synthetic data is fidelity to the "long tail" of data. Because generators prioritize the most frequent statistical patterns, they often fail to replicate the rare edge cases and complex nuances found in real- world data, which can result in AI models that perform poorly when they encounter unusual but critical real- world situations.

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QUESTION: 10

What is the primary purpose of quality filtering in training data preparation?

Organizing content by topic or domain
Distinguishing high-quality content from low-quality sources
Converting all content to a standard format
Reducing the total size of training datasets

Answer(s): B

Explanation:

The primary goal of quality filtering is to ensure that the AI model learns from accurate, coherent, and
representative data. By removing spam, gibberish, and low-value content, developers improve the model's reasoning capabilities and reduce the likelihood of the AI generating nonsensical or harmful outputs.

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QUESTION: 11

Sampling bias in AI occurs when:

Historical patterns in data reflect past discrimination
The AI system is used outside its intended purpose
Training data is not representative of the real-world population the AI system will serve
Evaluation methods use inappropriate benchmarks for testing

Answer(s): C

Explanation:

Sampling bias occurs when the training dataset is a flawed "subset" of reality. If the data used to teach the AI lacks the diversity or range of the actual population it will encounter, the system will produce skewed or inaccurate results for the underrepresented groups.

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QUESTION: 12

A credit risk model was trained using financial data from five years ago. What data quality problem might cause it to make poor predictions about customers today?

The historical data contains too many missing values
The data was collected using inconsistent methodologies
The data lacks timeliness for the current economic context
The financial features are not relevant for credit decisions

Answer(s): C

Explanation:

The primary problem is a lack of timeliness. Because economic conditions and consumer behaviors change over time, a model trained on five-year-old data suffers from data drift. To remain accurate, credit models require fresh data that reflects the current financial landscape.

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QUESTION: 13

What is the relationship between model explainability and AI trustworthiness?

They are unrelated concepts
Explainability only matters for generative AI
Trustworthiness is more important than explainability
Explainability is essential for evaluating trustworthiness

Answer(s): D

Explanation:

Explainability is a prerequisite for trustworthiness. For a system to be considered "trustworthy," it must be transparent enough for experts to audit its logic, for users to understand its results, and for regulators to ensure it complies with safety and fairness standards. Without explainability, "trust" is just blind faith, which is unacceptable in high-stakes AI applications.

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QUESTION: 14

When would you choose LIME over SHAP for model explanation?

When computational resources are limited and you need quick local explanations
When explaining models to non-technical audiences who need visual simplicity
When stakeholders need easily interpretable approximations rather than exact feature contributions
When you need to explain highly complex ensemble models with many features

Answer(s): A

Explanation:

The primary reason to choose LIME over SHAP is computational efficiency. While SHAP is more mathematically consistent and "fair," it is often too slow for large datasets or real-time applications. LIME offers a much faster, "local" approximation that is perfect when you need quick insights without the heavy processing cost.

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QUESTION: 15

The 2021 Anthony Bourdain documentary deepfake controversy highlighted:

Legal issues with documentary filmmaking standards
The ethical implications of using AI to create voiceovers without someone's consent
Technical limitations in voice synthesis technology
Copyright violations in using someone's voice without permission

Answer(s): B

Explanation:

The Bourdain controversy is a landmark case in AI Ethics, illustrating the risks of using synthetic media without informed consent and the lack of transparency regarding the use of "deepfake" audio in creative works.

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QUESTION: 16

What does automated bias detection provide?

Manual review processes conducted by human auditors
Quarterly reporting to regulatory authorities
One-time assessment during initial model development
Continuous monitoring for ethical compliance

Answer(s): D

Explanation:

Automated bias detection provides continuous monitoring. Unlike manual or one-time checks, automated tools scan model performance in real-time to ensure the system remains ethically compliant and fair as real-world data evolves.

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CSA Trusted AI Safety Expert v1 TAISE Dumps in PDF

Free CSA TAISE Real Questions (page: 4)

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