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CertNexus AIP-210 Prüfungsinformationen, AIP-210 Vorbereitung

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CertNexus AIP-210 Prüfungsplan:

Thema
Einzelheiten

Thema 1

Identify potential ethical concerns
Analyze machine learning system use cases

Thema 2

Transform numerical and categorical data
Address business risks, ethical concerns, and related concepts in operationalizing the model

Thema 3

Train, validate, and test data subsets
Training and Tuning ML Systems and Models

>> CertNexus AIP-210 Prüfungsinformationen <<

CertNexus AIP-210 Vorbereitung & AIP-210 Quizfragen Und Antworten

Dass man das Zertifikat für CertNexus AIP-210 erhalten kann, wird die Voruassetzung dafür, dass man in der immer schärf konkurrierten IT-Branche weiter entwickeln kann. Es ist durchaus machbar, dass man anhand der Fragenkataloge zur CertNexus AIP-210 Zertifizierungsprüfung von ITZert diese Prüfung so schnell wie möglich besteht. Wir versprechen Ihnen, dass wir Ihnen alle Ihre bezahlten Summe zurückgeben werden, wenn Sie die CertNexus AIP-210 Zertifizierungsprüfung nicht bestehen, nachdem Sie unsere Fragenpool gekauft haben.

CertNexus Certified Artificial Intelligence Practitioner (CAIP) AIP-210 Prüfungsfragen mit Lösungen (Q75-Q80):

75. Frage
Normalization is the transformation of features:

A. By subtracting from the mean and dividing by the standard deviation.
B. Into the normal distribution.
C. So that they are on a similar scale.
D. To different scales from each other.

Antwort: C

Begründung:
Normalization is the transformation of features so that they are on a similar scale, usually between 0 and 1 or
-1 and 1. This can help reduce the influence of outliers and improve the performance of some machine learning algorithms that are sensitive to the scale of the features, such as gradient descent, k-means, or k- nearest neighbors. References: [Feature scaling - Wikipedia], [Normalization vs Standardization - Quantitative analysis]

76. Frage
A market research team has ratings from patients who have a chronic disease, on several functional, physical, emotional, and professional needs that stay unmet with the current therapy. The dataset also captures ratings on how the disease affects their day-to-day activities.
A pharmaceutical company is introducing a new therapy to cure the disease and would like to design their marketing campaign such that different groups of patients are targeted with different ads. These groups should ideally consist of patients with similar unmet needs.
Which of the following algorithms should the market research team use to obtain these groups of patients?

A. Logistic regression
B. Naive-Bayes
C. k-nearest neighbors
D. k-means clustering

Antwort: D

Begründung:
k-means clustering is an algorithm that should be used by the market research team to obtain groups of patients with similar unmet needs. k-means clustering is an unsupervised learning technique that partitions the data into k clusters based on the similarity of the features. The algorithm iteratively assigns each data point to the cluster with the nearest centroid and updates the centroid until convergence. k-means clustering can help identify patterns and segments in the data that may not be obvious or intuitive. References: [K-means clustering - Wikipedia], [How to Run K-Means Clustering in Python]

77. Frage
Which of the following text vectorization methods is appropriate and correctly defined for an English-to- Spanish translation machine?

A. Using Word2vec because in translation machines, we do not care about the order of the words.
B. Using Word2vec because in translation machines, we need to consider the order of the words.
C. Using TF-IDF because in translation machines, we do not care about the order of the words.
D. Using TF-IDF because in translation machines, we need to consider the order of the words.

Antwort: B

Begründung:
Text vectorization is a technique that converts text into numerical vectors that can be used by machine learning models. Text vectorization can use different methods to represent text features, such as word frequency, word order, word meaning, or word context. Some of the common text vectorization methods are:
* TF-IDF: TF-IDF (term frequency-inverse document frequency) is a method that assigns a weight to each word based on its frequency in a document and its rarity across a collection of documents. TF-IDF can capture the importance and relevance of words for a given topic or domain, but it does not consider the order or meaning of words.
* Word2vec: Word2vec is a method that learns a 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, as well as preserve the order of words.
For an English-to-Spanish translation machine, using Word2vec would be appropriate and correctly defined, because in translation machines, we need to consider the order of the words, as well as their meaning and context.

78. Frage
Which of the following principles supports building an ML system with a Privacy by Design methodology?

A. Understanding, documenting, and displaying data lineage.
B. Avoiding mechanisms to explain and justify automated decisions.
C. Utilizing quasi-identifiers and non-unique identifiers, alone or in combination.
D. Collecting and processing the largest amount of data possible.

Antwort: A

Begründung:
Data lineage is the process of tracking the origin, transformation, and usage of data throughout its lifecycle. It helps to ensure data quality, integrity, and provenance. Data lineage also supports the Privacy by Design methodology, which is a framework that aims to embed privacy principles into the design and operation of systems, processes, and products that involve personal data. By understanding, documenting, and displaying data lineage, an ML system can demonstrate how it collects, processes, stores, and deletes personal data in a transparent and accountable manner3 .

79. Frage
A change in the relationship between the target variable and input features is

A. data drift.
B. covariate shift.
C. model decay.
D. concept drift.

Antwort: D

Begründung:
Explanation
Concept drift, also known as model drift, occurs when the task that the model was designed to perform changes over time. For example, imagine that a machine learning model was trained to detect spam emails based on the content of the email. If the types of spam emails that people receive change significantly, the model may no longer be able to accurately detect spam. References: Understanding Data Drift and Model Drift: Drift Detection in Python | DataCamp, Machine Learning Monitoring, Part 5: Why You Should Care About Data and Concept Drift

80. Frage
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Die Schulungsunterlagen zur AIP-210 Zertifizierungsprüfung von ITZert sind in der Form von PDF und Software angeboten. Sie umfassen die Fragen und Antworten zur AIP-210 Zertifizierungsprüfung. Sie können vielleicht auch den realen Prüfungsaufgaben hier begegnen. Alle diesen Fragen sind perfekt und wirksam. Sie können alle CertNexus AIP-210 Zertifizierungsprüfungen bestehen. Die CertNexus AIP-210 Zertifizierungsprüfungen von ITZert umfassen alle Planprogramme und sowie komplizierte Fragen. Die Fragen und Antworten zur CertNexus AIP-210 Zertifizierungsprüfung von ITZert sind die realen Herausforderungen. Sie müssen Ihre Fähigkeiten und Denkweisen entfalten.

AIP-210 Vorbereitung: https://www.itzert.com/AIP-210_valid-braindumps.html

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