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CertNexus AIP-210 Fragen und Antworten, CertNexus Certified Artificial Intelligence Practitioner (CAIP) Prüfungsfragen

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CertNexus AIP-210 Prüfungsplan:

Thema
Einzelheiten

Thema 1

Address business risks, ethical concerns, and related concepts in training and tuning
Work with textual, numerical, audio, or video data formats

Thema 2

Identify potential ethical concerns
Analyze machine learning system use cases

Thema 3

Design machine and deep learning models
Explain data collection
transformation process in ML workflow

Thema 4

Understanding the Artificial Intelligence Problem
Analyze the use cases of ML algorithms to rank them by their success probability

>> AIP-210 Quizfragen Und Antworten <<

AIP-210 Fragen Und Antworten - AIP-210 Testking

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) AIP-210 Prüfungsfragen mit Lösungen (Q60-Q65):

60. Frage
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. More accurately are less robust against adversarial attacks.
D. Less accurately are less robust against adversarial attacks.

Antwort: C

Begründung:
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]

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

Antwort: A

62. Frage
Your dependent variable data is a proportion. The observed range of your data is 0.01 to 0.99. The instrument used to generate the dependent variable data is known to generate low quality data for values close to 0 and close to 1. A colleague suggests performing a logit-transformation on the data prior to performing a linear regression. Which of the following is a concern with this approach?
Definition of logit-transformation
If p is the proportion: logit(p)=log(p/(l-p))

A. After logit-transformation, the data may violate the assumption of independence.
B. Values near 0.5 before logit-transformation will be near 0 after.
C. The model will be more likely to violate the assumption of normality.
D. Noisy data could become more influential in your model.

Antwort: D

Begründung:
Explanation
Logit-transformation is a common way to transform proportion data into a continuous variable that can be used for linear regression. However, one concern with this approach is that noisy data could become more influential in your model. This is because logit-transformation tends to amplify the values close to 0 and 1, which are also the values that are likely to be affected by measurement errors or outliers. This could distort the relationship between the dependent and independent variables and bias the regression coefficients. References:
[Logit Transformation | Real Statistics Using Excel], [Logit transformation for proportions - Cross Validated]

63. Frage
Which two of the following criteria are essential for machine learning models to achieve before deployment?
(Select two.)

A. Data size
B. Portability
C. Scalability
D. Complexity
E. Explainability

Antwort: C,E

Begründung:
Explanation
Scalability and explainability are two criteria that are essential for ML models to achieve before deployment.
Scalability is the ability of an ML model to handle increasing amounts of data or requests without compromising its performance or quality. Scalability can help ensure that the model can meet the demand and expectations of users or customers, as well as adapt to changing conditions or environments. Explainability is the ability of an ML model to provide clear and intuitive explanations for its predictions or decisions.
Explainability can help increase trust and confidence among users or stakeholders, as well as enable accountability and responsibility for the model's actions and outcomes.

64. Frage
Word Embedding describes a task in natural language processing (NLP) where:

A. Words are converted into numerical vectors.
B. Words are grouped together into clusters and then represented by word cluster membership.
C. Words are featurized by taking a histogram of letter counts.
D. Words are featurized by taking a matrix of bigram counts.

Antwort: A

Begründung:
Explanation
Word embedding is a task in natural language processing (NLP) where words are converted into numerical vectors that represent their meaning, usage, or context. Word embedding can help reduce the dimensionality and sparsity of text data, as well as enable various operations and comparisons among words based on their vector representations. Some of the common methods for word embedding are:
One-hot encoding: One-hot encoding is a method that assigns a unique binary vector to each word in a vocabulary. The vector has only one element with a value of 1 (the hot bit) and the rest with a value of
0. One-hot encoding can create distinct and orthogonal vectors for each word, but it does not capture any semantic or syntactic information about words.
Word2vec: Word2vec is a method that learns a dense and continuous vector representation for each word based on its context in a large corpus of text. Word2vec can capture the semantic and syntactic similarity and relationships among words, such as synonyms, antonyms, analogies, or associations.
GloVe: GloVe (Global Vectors for Word Representation) is a method that combines the advantages of count-based methods (such as TF-IDF) and predictive methods (such as Word2vec) to create word vectors. GloVe can leverage both global and local information from a large corpus of text to capture the co-occurrence patterns and probabilities of words.

65. Frage
......

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AIP-210 Fragen Und Antworten: https://www.zertpruefung.ch/AIP-210_exam.html

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Lee King Lee King

Biography

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