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AIP-210 Certification Exam Infor & Real AIP-210 Braindumps

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CertNexus AIP-210 Exam Syllabus Topics:

Topic
Details

Topic 1

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

Topic 2

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

Topic 3

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

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

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

Topic 6

Identify potential ethical concerns
Analyze machine learning system use cases

CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q32-Q37):

NEW QUESTION # 32
You and your team need to process large datasets of images as fast as possible for a machine learning task.
The project will also use a modular framework with extensible code and an active developer community.
Which of the following would BEST meet your needs?

A. TensorBoard
B. Keras
C. Caffe
D. Microsoft Cognitive Services

Answer: C

Explanation:
Explanation
Caffe is a deep learning framework that is designed for speed and modularity. It can process large datasets of images efficiently and supports various types of neural networks. It also has a large and active developer community that contributes to its code base and documentation. Caffe is suitable for image processing tasks such as classification, segmentation, detection, and recognition

NEW QUESTION # 33
The following confusion matrix is produced when a classifier is used to predict labels on a test dataset. How precise is the classifier?

A. 37/(37+7)
B. 48/(48+37)
C. 37/(37+8)
D. (48+37)/100

Answer: C

Explanation:
Explanation
Precision is a measure of how well a classifier can avoid false positives (incorrectly predicted positive cases).
Precision is calculated by dividing the number of true positives (correctly predicted positive cases) by the number of predicted positive cases (true positives and false positives). In this confusion matrix, the true positives are 37 and the false positives are 8, so the precision is 37/(37+8) = 0.822.

NEW QUESTION # 34
A company is developing a merchandise sales application The product team uses training data to teach the AI model predicting sales, and discovers emergent bias. What caused the biased results?

A. The application was migrated from on-premise to a public cloud.
B. The AI model was trained in winter and applied in summer.
C. The team set flawed expectations when training the model.
D. The training data used was inaccurate.

Answer: B

Explanation:
Explanation
Emergent bias is a type of bias that arises when an AI model encounters new or different data or scenarios that were not present or accounted for during its training or development. Emergent bias can cause the model to make inaccurate or unfair predictions or decisions, as it may not be able to generalize well to new situations or adapt to changing conditions. One possible cause of emergent bias is seasonality, which means that some variables or patterns in the data may vary depending on the time of year. For example, if an AI model for merchandise sales prediction was trained in winter and applied in summer, it may produce biased results due to differences in customer behavior, demand, or preferences.

NEW QUESTION # 35
An AI system recommends New Year's resolutions. It has an ML pipeline without monitoring components.
What retraining strategy would be BEST for this pipeline?

A. When data drift is detected
B. Periodically before New Year's Day and after New Year's Day
C. When concept drift is detected
D. Periodically every year

Answer: D

Explanation:
Explanation
Retraining is the process of updating an existing ML model with new or updated data to maintain or improve its performance and relevance. Retraining can help address various issues or challenges in ML systems, such as data drift, concept drift, model degradation, or changing requirements. Retraining can be done using different strategies, such as periodically, continuously, or on-demand.
For an AI system that recommends New Year's resolutions, retraining periodically every year would be the best strategy for this pipeline. This is because New Year's resolutions are seasonal and time-sensitive, meaning that they may vary depending on the year or the current situation. Retraining periodically every year can help ensure that the system's recommendations are up-to-date and relevant for each new year.

NEW QUESTION # 36
An HR solutions firm is developing software for staffing agencies that uses machine learning.
The team uses training data to teach the algorithm and discovers that it generates lower employability scores for women. Also, it predicts that women, especially with children, are less likely to get a high-paying job.
Which type of bias has been discovered?

A. Emergent
B. Technical
C. Preexisting
D. Automation

Answer: C

Explanation:
Explanation
Preexisting bias is a type of bias that originates from historical or social contexts, such as stereotypes, prejudices, or discriminations. Preexisting bias can affect the data or the algorithm used for machine learning, as well as the outcomes or decisions made by machine learning. Preexisting bias can cause unfair or harmful impacts on certain groups or individuals based on their attributes, such as gender, race, age, or disability3. In this case, the software that uses machine learning generates lower employability scores for women and predicts that women, especially with children, are less likely to get a high-paying job. This indicates that the software has preexisting bias against women, which may reflect the historical or social inequalities or expectations in the labor market.

NEW QUESTION # 37
......

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