AIP-210 Demotesten & AIP-210 Prüfungsfragen

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AIP-210 Ressourcen Prüfung - AIP-210 Prüfungsguide & AIP-210 Beste Fragen

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CertNexus AIP-210 Prüfungsplan:

Thema	Einzelheiten
Thema 1	Train, validate, and test data subsets Training and Tuning ML Systems and Models
Thema 2	Understanding the Artificial Intelligence Problem Analyze the use cases of ML algorithms to rank them by their success probability
Thema 3	Identify potential ethical concerns Analyze machine learning system use cases
Thema 4	Design machine and deep learning models Explain data collection transformation process in ML workflow
Thema 5	Recognize relative impact of data quality and size to algorithms Engineering Features for Machine Learning
Thema 6	Transform numerical and categorical data Address business risks, ethical concerns, and related concepts in operationalizing the model

CertNexus Certified Artificial Intelligence Practitioner (CAIP) AIP-210 Prüfungsfragen mit Lösungen (Q92-Q97):

92. Frage
A company is developing a merchandise sales application The product team uses training data to teach the AI model predicting sales, and discovers emergent bias. What caused the biased results?

A. The AI model was trained in winter and applied in summer.
B. The team set flawed expectations when training the model.
C. The application was migrated from on-premise to a public cloud.
D. The training data used was inaccurate.

Antwort: A

Begründung:
Explanation
Emergent bias is a type of bias that arises when an AI model encounters new or different data or scenarios that were not present or accounted for during its training or development. Emergent bias can cause the model to make inaccurate or unfair predictions or decisions, as it may not be able to generalize well to new situations or adapt to changing conditions. One possible cause of emergent bias is seasonality, which means that some variables or patterns in the data may vary depending on the time of year. For example, if an AI model for merchandise sales prediction was trained in winter and applied in summer, it may produce biased results due to differences in customer behavior, demand, or preferences.

93. Frage
Which three security measures could be applied in different ML workflow stages to defend them against malicious activities? (Select three.)

A. Monitor model degradation.
B. Use max privilege to control access to ML artifacts.
C. Use data encryption.
D. Use Secrets Manager to protect credentials.
E. Launch ML Instances In a virtual private cloud (VPC).
F. Disable logging for model access.

Antwort: C,D,E

Begründung:
Explanation
Security measures can be applied in different ML workflow stages to defend them against malicious activities, such as data theft, model tampering, or adversarial attacks. Some of the security measures are:
Launch ML Instances In a virtual private cloud (VPC): A VPC is a logically isolated section of a cloud provider's network that allows users to launch and control their own resources. By launching ML instances in a VPC, users can enhance the security and privacy of their data and models, as well as restrict the access and traffic to and from the instances.
Use data encryption: Data encryption is the process of transforming data into an unreadable format using a secret key or algorithm. Data encryption can protect the confidentiality, integrity, and availability of data at rest (stored in databases or files) or in transit (transferred over networks). Data encryption can prevent unauthorized access, modification, or leakage of sensitive data.
Use Secrets Manager to protect credentials: Secrets Manager is a service that helps users securely store, manage, and retrieve secrets, such as passwords, API keys, tokens, or certificates. Secrets Manager can help users protect their credentials from unauthorized access or exposure, as well as rotate them automatically to comply with security policies.

94. Frage
Given a feature set with rows that contain missing continuous values, and assuming the data is normally distributed, what is the best way to fill in these missing features?

A. Fill in missing features with the average of observed values for that feature in the entire dataset.
B. Delete entire columns that contain any missing features.
C. Fill in missing features with random values for that feature in the training set.
D. Delete entire rows that contain any missing features.

Antwort: A

Begründung:
Explanation
Missing values are a common problem in data analysis and machine learning, as they can affect the quality and reliability of the data and the model. There are various methods to deal with missing values, such as deleting, imputing, or ignoring them. One of the most common methods is imputing, which means replacing the missing values with some estimated values based on some criteria. For continuous variables, one of the simplest and most widely used imputation methods is to fill in the missing values with the mean (average) of the observed values for that variable in the entire dataset. This method can preserve the overall distribution and variance of the data, as well as avoid introducing bias or noise.

95. Frage
An AI system recommends New Year's resolutions. It has an ML pipeline without monitoring components.
What retraining strategy would be BEST for this pipeline?

A. When data drift is detected
B. Periodically before New Year's Day and after New Year's Day
C. Periodically every year
D. When concept drift is detected

Antwort: C

96. Frage
R-squared is a statistical measure that:

A. Combines precision and recall of a classifier into a single metric by taking their harmonic mean.
B. Is the proportion of the variance for a dependent variable thaf' s explained by independent variables.
C. Represents the extent to which two random variables vary together.
D. Expresses the extent to which two variables are linearly related.

Antwort: B

Begründung:
R-squared is a statistical measure that indicates how well a regression model fits the data. R-squared is calculated by dividing the explained variance by the total variance. The explained variance is the amount of variation in the dependent variable that can be attributed to the independent variables. The total variance is the amount of variation in the dependent variable that can be observed in the data. R-squared ranges from 0 to 1, where 0 means no fit and 1 means perfect fit.

97. Frage
......

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AIP-210 Demotesten & AIP-210 Prüfungsfragen

AIP-210 Ressourcen Prüfung - AIP-210 Prüfungsguide & AIP-210 Beste Fragen

CertNexus AIP-210 Prüfungsplan:

CertNexus Certified Artificial Intelligence Practitioner (CAIP) AIP-210 Prüfungsfragen mit Lösungen (Q92-Q97):

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