AIP-210 Exam Torrent & AIP-210 Study Questions & AIP-210 Valid Pdf
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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q16-Q21):
NEW QUESTION # 16
Which of the following statements are true regarding highly interpretable models? (Select two.)
- A. They are usually easier to explain to business stakeholders.
- B. They are usually very good at solving non-linear problems.
- C. They are usually binary classifiers.
- D. They are usually referred to as "black box" models.
- E. They usually compromise on model accuracy for the sake of interpretability.
Answer: A,E
Explanation:
Highly interpretable models are models that can provide clear and intuitive explanations for their predictions, such as decision trees, linear regression, or logistic regression. Some of the statements that are true regarding highly interpretable models are:
* They are usually easier to explain to business stakeholders: Highly interpretable models can help communicate the logic and reasoning behind their predictions, which can increase trust and confidence among business stakeholders. For example, a decision tree can show how each feature contributes to a decision outcome, or a linear regression can show how each coefficient affects the dependent variable.
* They usually compromise on model accuracy for the sake of interpretability: Highly interpretable models may not be able to capture complex or non-linear patterns in the data, which can reduce their accuracy and generalization. For example, a decision tree may overfit or underfit the data if it is too deep or too shallow, or a linear regression may not be able to model curved relationships between variables.
NEW QUESTION # 17
A big data architect needs to be cautious about personally identifiable information (PII) that may be captured with their new IoT system. What is the final stage of the Data Management Life Cycle, which the architect must complete in order to implement data privacy and security appropriately?
- A. Duplicate
- B. De-Duplicate
- C. Destroy
- D. Detain
Answer: C
Explanation:
The final stage of the data management life cycle is data destruction, which is the process of securely deleting or erasing data that is no longer needed or relevant for the organization. Data destruction ensures that data is disposed of in compliance with any legal or regulatory requirements, as well as any internal policies or standards. Data destruction also protects the organization from potential data breaches, leaks, or thefts that could compromise its privacy and security. Data destruction can be performed using various methods, such as overwriting, degaussing, shredding, or incinerating
NEW QUESTION # 18
Which of the following unsupervised learning models can a bank use for fraud detection?
- A. DB5CAN
- B. Hierarchical clustering
- C. k-means
- D. Anomaly detection
Answer: D
Explanation:
Anomaly detection is an unsupervised learning technique that identifies outliers or abnormal patterns in data, which can be useful for fraud detection. Anomaly detection algorithms can learn the normal behavior of transactions and flag the ones that deviate significantly from the norm, indicating possible fraud.
NEW QUESTION # 19
What is the open framework designed to help detect, respond to, and remediate threats in ML systems?
- A. Threat Susceptibility Matrix
- B. OWASP Threat and Safeguard Matrix
- C. Adversarial ML Threat Matrix
- D. MITRE ATTandCK Matrix
Answer: C
Explanation:
The Adversarial ML Threat Matrix is an open framework designed to help detect, respond to, and remediate threats in ML systems. The Adversarial ML Threat Matrix is inspired by the MITRE ATTandCK Matrix1, which is a framework for describing cyberattacks across various stages of an attack lifecycle. The Adversarial ML Threat Matrix adapts this framework to address specific threats and vulnerabilities in ML systems, such as data poisoning, model stealing, model evasion, or model inversion2. The Adversarial ML Threat Matrix provides a structured way to organize and classify adversarial techniques, tactics, procedures, examples, and mitigations for ML systems2.
NEW QUESTION # 20
In general, models that perform their tasks:
- A. More accurately are neither more nor less robust against adversarial attacks.
- B. Less accurately are neither more nor less robust against adversarial attacks.
- C. Less accurately are less robust against adversarial attacks.
- D. More accurately are less robust against adversarial attacks.
Answer: D
Explanation:
Adversarial attacks are malicious attempts to fool or manipulate machine learning models by adding small perturbations to the input data that are imperceptible to humans but can cause significant changes in the model output. In general, models that perform their tasks more accurately are less robust against adversarial attacks, because they tend to have higher confidence in their predictions and are more sensitive to small changes in the input data. References: [Adversarial machine learning - Wikipedia], [Why Are Machine Learning Models Susceptible to Adversarial Attacks? | by Anirudh Jain | Towards Data Science]
NEW QUESTION # 21
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