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[General] To Get Brilliant Success CertNexus AIP-210 Questions

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【General】 To Get Brilliant Success CertNexus AIP-210 Questions

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CertNexus AIP-210 Exam Syllabus Topics:
TopicDetails
Topic 1
  • Address business risks, ethical concerns, and related concepts in training and tuning
  • Work with textual, numerical, audio, or video data formats
Topic 2
  • Understanding the Artificial Intelligence Problem
  • Analyze the use cases of ML algorithms to rank them by their success probability
Topic 3
  • Train, validate, and test data subsets
  • Training and Tuning ML Systems and Models
Topic 4
  • Recognize relative impact of data quality and size to algorithms
  • Engineering Features for Machine Learning

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q63-Q68):NEW QUESTION # 63
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 random values for that feature in the training set.
  • B. Fill in missing features with the average of observed values for that feature in the entire dataset.
  • C. Delete entire rows that contain any missing features.
  • D. Delete entire columns that contain any missing features.
Answer: B
Explanation:
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.

NEW QUESTION # 64
Which of the following algorithms is an example of unsupervised learning?
  • A. Principal components analysis
  • B. Neural networks
  • C. Ridge regression
  • D. Random forest
Answer: A
Explanation:
Explanation
Unsupervised learning is a type of machine learning that involves finding patterns or structures in unlabeled data without any predefined outcome or feedback. Unsupervised learning can be used for various tasks, such as clustering, dimensionality reduction, anomaly detection, or association rule mining. Some of the common algorithms for unsupervised learning are:
Principal components analysis: Principal components analysis (PCA) is a method that reduces the dimensionality of data by transforming it into a new set of orthogonal variables (principal components) that capture the maximum amount of variance in the data. PCA can help simplify and visualize high-dimensional data, as well as remove noise or redundancy from the data.
K-means clustering: K-means clustering is a method that partitions data into k groups (clusters) based on their similarity or distance. K-means clustering can help discover natural or hidden groups in the data, as well as identify outliers or anomalies in the data.
Apriori algorithm: Apriori algorithm is a method that finds frequent itemsets (sets of items that occur together frequently) and association rules (rules that describe how items are related or correlated) in transactional data. Apriori algorithm can help discover patterns or insights in the data, such as customer behavior, preferences, or recommendations.

NEW QUESTION # 65
Which of the following text vectorization methods is appropriate and correctly defined for an English-to-Spanish translation machine?
  • A. Using TF-IDF because in translation machines, we need to consider the order of the words.
  • B. Using Word2vec because in translation machines, we need to consider the order of the words.
  • C. Using Word2vec because in translation machines, we do not care about the order of the words.
  • D. Using TF-IDF because in translation machines, we do not care about the order of the words.
Answer: B
Explanation:
Explanation
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.

NEW QUESTION # 66
Which of the following tools would you use to create a natural language processing application?
  • A. AWS DeepRacer
  • B. NLTK
  • C. Azure Search
  • D. DeepDream
Answer: B
Explanation:
NLTK (Natural Language Toolkit) is a Python library that provides a set of tools and resources for natural language processing (NLP). NLP is a branch of AI that deals with analyzing, understanding, and generating natural language texts or speech. NLTK offers modules for various NLP tasks, such as tokenization, stemming, lemmatization, parsing, tagging, chunking, sentiment analysis, named entity recognition, machine translation, text summarization, and more .

NEW QUESTION # 67
Which of the following is the definition of accuracy?
  • A. (True Positives + False Positives) / Total Predictions
  • B. True Positives / (True Positives + False Negatives)
  • C. True Positives / (True Positives + False Positives)
  • D. (True Positives + True Negatives) / Total Predictions
Answer: D
Explanation:
Explanation
Accuracy is a measure of how well a classifier can correctly predict the class of an instance. Accuracy is calculated by dividing the number of correct predictions (true positives and true negatives) by the total number of predictions. True positives are instances that are correctly predicted as positive (belonging to the target class). True negatives are instances that are correctly predicted as negative (not belonging to the target class).

NEW QUESTION # 68
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