AIP-210 New Real Test, Certification AIP-210 Test Answers

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CertNexus AIP-210 Exam Syllabus Topics:

Topic	Details
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	Transform numerical and categorical data Address business risks, ethical concerns, and related concepts in operationalizing the model
Topic 3	Design machine and deep learning models Explain data collection transformation process in ML workflow
Topic 4	Understanding the Artificial Intelligence Problem Analyze the use cases of ML algorithms to rank them by their success probability
Topic 5	Recognize relative impact of data quality and size to algorithms Engineering Features for Machine Learning
Topic 6	Train, validate, and test data subsets Training and Tuning ML Systems and Models

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q24-Q29):

NEW QUESTION # 24
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 the average of observed values for that feature in the entire dataset.
B. Fill in missing features with random values for that feature in the training set.
C. Delete entire columns that contain any missing features.
D. Delete entire rows that contain any missing features.

Answer: A

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 # 25
In general, models that perform their tasks:

A. More accurately are less robust against adversarial attacks.
B. Less accurately are less robust against adversarial attacks.
C. More accurately are neither more nor less robust against adversarial attacks.
D. Less accurately are neither more nor less robust against adversarial attacks.

Answer: A

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 # 26
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. High negative predictive value
B. High positive predictive value
C. Low false negative rate
D. Low false positive rate

Answer: C

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 # 27
In which of the following scenarios is lasso regression preferable over ridge regression?

A. The number of features is much larger than the sample size.
B. There are many features with no association with the dependent variable.
C. There is high collinearity among some of the features associated with the dependent variable.
D. The sample size is much larger than the number of features.

Answer: B

Explanation:
Explanation
Lasso regression is a type of linear regression that adds a regularization term to the loss function to reduce overfitting and improve generalization. Lasso regression uses an L1 norm as the regularization term, which is the sum of the absolute values of the coefficients. Lasso regression can shrink some of the coefficients to zero, which effectively eliminates some of the features from the model. Lasso regression is preferable over ridge regression when there are many features with no association with the dependent variable, as it can perform feature selection and reduce the complexity and noise of the model.

NEW QUESTION # 28
What is Word2vec?

A. A matrix of how frequently words appear in a group of documents.
B. A word embedding method that finds characteristics of words in a very large number of documents.
C. A word embedding method that builds a one-hot encoded matrix from samples and the terms that appear in them.
D. A bag of words.

Answer: B

Explanation:
Word2vec is a word embedding method that finds characteristics of words in a very large number of documents. Word embedding is a technique that converts words into numerical vectors that represent their meaning, usage, or context. Word2vec 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 associations1.

NEW QUESTION # 29
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