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Latest Released CertNexus AIP-210 Test Answers - AIP-210 Training CertNexus Certified Artificial Intelligence Practitioner (CAIP) Pdf

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CertNexus AIP-210 Exam Syllabus Topics:

Topic
Details

Topic 1

Transform numerical and categorical data
Address business risks, ethical concerns, and related concepts in operationalizing the model

Topic 2

Design machine and deep learning models
Explain data collection
transformation process in ML workflow

Topic 3

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

Topic 4

Address business risks, ethical concerns, and related concepts in training and tuning
Work with textual, numerical, audio, or video data formats

Topic 5

Identify potential ethical concerns
Analyze machine learning system use cases

Topic 6

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

CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q28-Q33):

NEW QUESTION # 28
Which of the following are true about the transform-design pattern for a machine learning pipeline? (Select three.) It aims to separate inputs from features.

A. It represents steps in the pipeline with a directed acyclic graph (DAG).
B. It ensures reproducibility.
C. It transforms the output data after production.
D. It encapsulates the processing steps of ML pipelines.
E. It seeks to isolate individual steps of ML pipelines.

Answer: B,D,E

Explanation:
Explanation
The transform-design pattern for ML pipelines aims to separate inputs from features, encapsulate the processing steps of ML pipelines, and represent steps in the pipeline with a DAG. These goals help to make the pipeline modular, reusable, and easy to understand. The transform-design pattern does not seek to isolate individual steps of ML pipelines, as this would create entanglement and dependency issues. It also does not transform the output data after production, as this would violate the principle of separation of concerns.

NEW QUESTION # 29
Your dependent variable Y is a count, ranging from 0 to infinity. Because Y is approximately log-normally distributed, you decide to log-transform the data prior to performing a linear regression.
What should you do before log-transforming Y?

A. Subtract the mean of Y from all the Y values.
B. Add 1 to all of the Y values.
C. Explore the data for outliers.
D. Divide all the Y values by the standard deviation of Y.

Answer: B

Explanation:
Explanation
Before log-transforming Y, we should add 1 to all of the Y values. This is because log transformation is undefined for zero or negative values, and some of the Y values may be zero. Adding 1 to all of the Y values can avoid this problem and ensure that the log transformation is valid and meaningful. Adding 1 to all of the Y values is also known as a log-plus-one transformation.

NEW QUESTION # 30
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 # 31
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. Delete entire columns that contain any missing features.
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. Fill in missing features with random values for that feature in the training set.

Answer: B

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 # 32
Which of the following options is a correct approach for scheduling model retraining in a weather prediction application?

A. When the input format changes
B. As new resources become available
C. When the input volume changes
D. Once a month

Answer: A

Explanation:
The input format is the way that the data is structured, organized, and presented to the model. For example, the input format could be a CSV file, an image file, or a JSON object. The input format can affect how the model interprets and processes the data, and therefore how it makes predictions. When the input format changes, it may require retraining the model to adapt to the new format and ensure its accuracy and reliability.
For example, if the weather prediction application switches from using numerical values to categorical values for some features, such as wind direction or cloud cover, it may need to retrain the model to handle these changes .

NEW QUESTION # 33
......

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