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Title: Excellect AIP-210 Pass Rate & AIP-210 Discount [Print This Page]
Author: joeward337    Time: before yesterday 20:41
Title: Excellect AIP-210 Pass Rate & AIP-210 Discount
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CertNexus AIP-210 Exam Syllabus Topics:
TopicDetails
Topic 1
  • Train, validate, and test data subsets
  • Training and Tuning ML Systems and Models
Topic 2
  • Transform numerical and categorical data
  • Address business risks, ethical concerns, and related concepts in operationalizing the model
Topic 3
  • Address business risks, ethical concerns, and related concepts in training and tuning
  • Work with textual, numerical, audio, or video data formats

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q67-Q72):NEW QUESTION # 67
A data scientist is tasked to extract business intelligence from primary data captured from the public. Which of the following is the most important aspect that the scientist cannot forget to include?
Answer: B
Explanation:
Explanation
Data privacy is the right of individuals to control how their personal data is collected, used, shared, and protected. It also involves complying with relevant laws and regulations that govern the handling of personal data. Data privacy is especially important when extracting business intelligence from primary data captured from the public, as it may contain sensitive or confidential information that could harm the individuals if misused or breached .

NEW QUESTION # 68
In general, models that perform their tasks:
Answer: C
Explanation:
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]

NEW QUESTION # 69
For a particular classification problem, you are tasked with determining the best algorithm among SVM, random forest, K-nearest neighbors, and a deep neural network. Each of the algorithms has similar accuracy on your data. The stakeholders indicate that they need a model that can convey each feature's relative contribution to the model's accuracy. Which is the best algorithm for this use case?
Answer: D
Explanation:
Random forest is an ensemble learning method that combines multiple decision trees to create a more accurate and robust classifier or regressor. Random forest can convey each feature's relative contribution to the model' s accuracy by measuring how much the prediction error increases when a feature is randomly permuted. This metric is called feature importance or Gini importance. Random forest can also provide insights into the interactions and dependencies among features by visualizing the decision trees .

NEW QUESTION # 70
Which of the following models are text vectorization methods? (Select two.)
Answer: C,D
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 # 71
Which of the following is the correct definition of the quality criteria that describes completeness?
Answer: A
Explanation:
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 # 72
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