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CertNexus AIP-210 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Design machine and deep learning models
  • Explain data collection
  • transformation process in ML workflow
Topic 2
  • Address business risks, ethical concerns, and related concepts in training and tuning
  • Work with textual, numerical, audio, or video data formats
Topic 3
  • Transform numerical and categorical data
  • Address business risks, ethical concerns, and related concepts in operationalizing the model
Topic 4
  • Understanding the Artificial Intelligence Problem
  • Analyze the use cases of ML algorithms to rank them by their success probability

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q94-Q99):

NEW QUESTION # 94
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?

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 # 95
Which of the following models are text vectorization methods? (Select two.)

Answer: B,E

Explanation:
Skip-gram and TF-IDF are both text vectorization methods that convert text into numerical feature vectors.
Skip-gram is a prediction-based word embedding method that learns vector representations of words from their contexts in a large corpus of text. TF-IDF is a frequency-based word weighting method that assigns scores to words based on their importance in a document and in a corpus of documents. References: Text Vectorization and Word Embedding | Guide to Master NLP (Part 5), What Is Text Vectorization? Everything You Need to Know - deepset


NEW QUESTION # 96
Which of the following principles supports building an ML system with a Privacy by Design methodology?

Answer: C

Explanation:
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 .


NEW QUESTION # 97
An organization sells house security cameras and has asked their data scientists to implement a model to detect human feces, as distinguished from animals, so they can alert th customers only when a human gets close to their house.
Which of the following algorithms is an appropriate option with a correct reason?

Answer: C

Explanation:
Explanation
Neural network models are suitable for classification problems with a large number of features, because they can learn complex and non-linear patterns from high-dimensional data. They can also handle image data, which is likely to be the input for the human face detection problem. Neural networks can also be trained using transfer learning, which can leverage pre-trained models on similar tasks and improve the accuracy and efficiency of the model. References: [Neural network - Wikipedia], [Transfer Learning - Machine Learning's Next Frontier]


NEW QUESTION # 98
Which of the following algorithms is an example of unsupervised learning?

Answer: C

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 # 99
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