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CertNexus AIP-210 Prüfungsplan:

Thema
Einzelheiten

Thema 1

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

Thema 2

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

Thema 3

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

Thema 4

Understanding the Artificial Intelligence Problem
Analyze the use cases of ML algorithms to rank them by their success probability

CertNexus Certified Artificial Intelligence Practitioner (CAIP) AIP-210 Prüfungsfragen mit Lösungen (Q43-Q48):

43. Frage
A dataset can contain a range of values that depict a certain characteristic, such as grades on tests in a class during the semester. A specific student has so far received the following grades: 76,81, 78, 87, 75, and 72.
There is one final test in the semester. What minimum grade would the student need to achieve on the last test to get an 80% average?

A. 0
B. 1
C. 2
D. 3

Antwort: A

Begründung:
Explanation
To calculate the minimum grade needed to achieve an 80% average, we can use the following formula:
minimum grade = (target average * number of tests - sum of grades) / (number of tests - 1) Plugging in the given values, we get:
minimum grade = (80 * 7 - (76 + 81 + 78 + 87 + 75 + 72)) / (7 - 6)
minimum grade = (560 - 469) / 1
minimum grade = 91
Therefore, the student needs to score at least 91 on the last test to get an 80% average.

44. Frage
Which of the following options is a correct approach for scheduling model retraining in a weather prediction application?

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

Antwort: D

Begründung:
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
.

45. Frage
Which two of the following criteria are essential for machine learning models to achieve before deployment?
(Select two.)

A. Complexity
B. Portability
C. Explainability
D. Scalability
E. Data size

Antwort: C,D

Begründung:
Explanation
Scalability and explainability are two criteria that are essential for ML models to achieve before deployment.
Scalability is the ability of an ML model to handle increasing amounts of data or requests without compromising its performance or quality. Scalability can help ensure that the model can meet the demand and expectations of users or customers, as well as adapt to changing conditions or environments. Explainability is the ability of an ML model to provide clear and intuitive explanations for its predictions or decisions.
Explainability can help increase trust and confidence among users or stakeholders, as well as enable accountability and responsibility for the model's actions and outcomes.

46. Frage
Which two techniques are used to build personas in the ML development lifecycle? (Select two.)

A. Population variance
B. Population triage
C. Population regression
D. Population estimates
E. Population resampling

Antwort: B,D

Begründung:
Personas are fictional characters that represent the potential users or customers of an ML system. Personas can help understand the needs, goals, preferences, and behaviors of the target audience, as well as design and evaluate the system from their perspective. Some of the techniques that are used to build personas in the ML development lifecycle are:
* Population estimates: Population estimates are statistical methods that estimate the size, characteristics, and distribution of a population based on a sample or a census. Population estimates can help identify and quantify the potential market segments and user groups for an ML system, as well as their demographics, locations, and behaviors.
* Population triage: Population triage is a process of prioritizing and selecting the most relevant and representative personas for an ML system based on some criteria or metrics. Population triage can help focus on the key user needs and scenarios, as well as avoid creating too many or too few personas.

47. Frage
You train a neural network model with two layers, each layer having four nodes, and realize that the model is underfit. Which of the actions below will NOT work to fix this underfitting?

A. Add features to training data
B. Increase the complexity of the model
C. Train the model for more epochs
D. Get more training data

Antwort: D

Begründung:
Underfitting is a problem that occurs when a model learns too little from the training data and fails to capture the underlying complexity or structure of the data. Underfitting can result from using insufficient or irrelevant features, a low complexity of the model, or a lack of training data. Underfitting can reduce the accuracy and generalization of the model, as it may produce oversimplified or inaccurate predictions. Some of the ways to fix underfitting are:
* Add features to training data: Adding more features or variables to the training data can help increase the information and diversity of the data, which can help the model learn more complex patterns and relationships.
* Increase the complexity of the model: Increasing the complexity of the model can help increase its expressive power and flexibility, which can help it fit better to the data. For example, adding more layers or nodes to a neural network can increase its complexity.
* Train the model for more epochs: Training the model for more epochs can help increase its learning ability and convergence, which can help it optimize its parameters and reduce its error.
Getting more training data will not work to fix underfitting, as it will not change the complexity or structure of the data or the model. Getting more training data may help with overfitting, which is when a model learns too much from the training data and fails to generalize well to new or unseen data.

48. Frage
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