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CertNexus AIP-210 Exam Syllabus Topics:

Topic
Details

Topic 1

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

Topic 2

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

Topic 3

Recognize relative impact of data quality and size to algorithms
Engineering Features for Machine Learning

Topic 4

Identify potential ethical concerns
Analyze machine learning system use cases

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q58-Q63):

NEW QUESTION # 58
Which of the following is the correct definition of the quality criteria that describes completeness?

A. The degree to which a set of measures are specified using the same units of measure in all systems.
B. The degree to which a set of measures are equivalent across systems.
C. The degree to which the measures conform to defined business rules or constraints.
D. The degree to which all required measures are known.

Answer: D

Explanation:
Completeness is a quality criterion that describes the degree to which all required measures are known.
Completeness can help assess the coverage and availability of data for a given purpose or analysis.
Completeness can be measured by comparing the actual number of measures with the expected number of measures, or by identifying and counting any missing, null, or unknown values in the data.

NEW QUESTION # 59

The graph is an elbow plot showing the inertia or within-cluster sum of squares on the y-axis and number of clusters (also called K) on the x-axis, denoting the change in inertia as the clusters change using k-means algorithm.
What would be an optimal value of K to ensure a good number of clusters?

A. 0
B. 1
C. 2
D. 3

Answer: C

Explanation:
The optimal value of K is the one that minimizes the inertia or within-cluster sum of squares, while avoiding too many clusters that may overfit the data. The elbow plot shows a sharp decrease in inertia from K = 1 to K
= 2, and then a more gradual decrease from K = 2 to K = 3. After K = 3, the inertia does not change much as K increases. Therefore, the elbow point is at K = 3, which is the optimal value of K for this data. References:
How to Run K-Means Clustering in Python, K-means clustering - Wikipedia

NEW QUESTION # 60
Which of the following occurs when a data segment is collected in such a way that some members of the intended statistical population are less likely to be included than others?

A. Systematic value distortion
B. Stereotype bias
C. Algorithmic bias
D. Sampling bias

Answer: D

Explanation:
Explanation
Sampling bias occurs when a data segment is collected in such a way that some members of the intended statistical population are less likely to be included than others. This can result in a sample that is not representative of the population and may lead to inaccurate or misleading conclusions. Sampling bias can be caused by various factors, such as non-random sampling methods, non-response, self-selection, or convenience sampling. References: [Sampling bias - Wikipedia], [What is Sampling Bias? Definition, Types and Examples]

NEW QUESTION # 61
Word Embedding describes a task in natural language processing (NLP) where:

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

Answer: D

Explanation:
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.

NEW QUESTION # 62
You are building a prediction model to develop a tool that can diagnose a particular disease so that individuals with the disease can receive treatment. The treatment is cheap and has no side effects. Patients with the disease who don't receive treatment have a high risk of mortality.
It is of primary importance that your diagnostic tool has which of the following?

A. Low false positive rate
B. High positive predictive value
C. High negative predictive value
D. Low false negative rate

Answer: D

Explanation:
A false negative is an error where a positive case (belonging to the target class) is incorrectly predicted as negative (not belonging to the target class). A false negative rate is the ratio of false negatives to all actual positive cases. A low false negative rate means that most of the positive cases are correctly identified by the classifier.
For a diagnostic tool that can diagnose a particular disease so that individuals with the disease can receive treatment, it is of primary importance that it has a low false negative rate. This is because false negatives can have serious consequences for patients who have the disease but do not receive treatment, such as increased risk of mortality or complications. A low false negative rate can ensure that most patients who have the disease are diagnosed correctly and receive timely treatment.

NEW QUESTION # 63
......

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Arthur Reed Arthur Reed

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