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CertNexus AIP-210 Exam

Exam Name: Certified Artificial Intelligence Practitioner Exam
Exam Code: AIP-210 CAIP
Related Certification(s): CertNexus Certified AI Practitioner Certification
Certification Provider: CertNexus
Actual Exam Duration: 120 Minutes
Number of AIP-210 practice questions in our database: 90 (updated: Jun. 11, 2024)
Expected AIP-210 Exam Topics, as suggested by CertNexus :
  • Topic 1: Identify potential ethical concerns/ Analyze machine learning system use cases
  • Topic 2: Train, validate, and test data subsets/ Training and Tuning ML Systems and Models
  • Topic 3: Recognize relative impact of data quality and size to algorithms/ Engineering Features for Machine Learning
  • Topic 4: Transform numerical and categorical data/ Address business risks, ethical concerns, and related concepts in operationalizing the model
  • Topic 5: Understanding the Artificial Intelligence Problem/ Analyze the use cases of ML algorithms to rank them by their success probability
  • Topic 6: Address business risks, ethical concerns, and related concepts in training and tuning/ Work with textual, numerical, audio, or video data formats
  • Topic 7: Design machine and deep learning models/ Explain data collection/transformation process in ML workflow
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Free CertNexus AIP-210 Exam Actual Questions

Note: Premium Questions for AIP-210 were last updated On Jun. 11, 2024 (see below)

Question #1

Which of the following tests should be performed at the production level before deploying a newly retrained model?

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Correct Answer: B

Performance testing is a type of testing that should be performed at the production level before deploying a newly retrained model. Performance testing measures how well the model meets the non-functional requirements, such as speed, scalability, reliability, availability, and resource consumption. Performance testing can help identify any bottlenecks or issues that may affect the user experience or satisfaction with the model. Reference: [Performance Testing Tutorial: What is, Types, Metrics & Example], [Performance Testing for Machine Learning Systems | by David Talby | Towards Data Science]


Question #2

Workflow design patterns for the machine learning pipelines:

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Correct Answer: B

Workflow design patterns for machine learning pipelines are common solutions to recurring problems in building and managing machine learning workflows. One of these patterns is to represent a pipeline with a directed acyclic graph (DAG), which is a graph that consists of nodes and edges, where each node represents a step or task in the pipeline, and each edge represents a dependency or order between the tasks. A DAG has no cycles, meaning there is no way to start at one node and return to it by following the edges. A DAG can help visualize and organize the pipeline, as well as facilitate parallel execution, fault tolerance, and reproducibility.


Question #3

Which of the following tests should be performed at the production level before deploying a newly retrained model?

Reveal Solution Hide Solution
Correct Answer: B

Performance testing is a type of testing that should be performed at the production level before deploying a newly retrained model. Performance testing measures how well the model meets the non-functional requirements, such as speed, scalability, reliability, availability, and resource consumption. Performance testing can help identify any bottlenecks or issues that may affect the user experience or satisfaction with the model. Reference: [Performance Testing Tutorial: What is, Types, Metrics & Example], [Performance Testing for Machine Learning Systems | by David Talby | Towards Data Science]


Question #4

Workflow design patterns for the machine learning pipelines:

Reveal Solution Hide Solution
Correct Answer: B

Workflow design patterns for machine learning pipelines are common solutions to recurring problems in building and managing machine learning workflows. One of these patterns is to represent a pipeline with a directed acyclic graph (DAG), which is a graph that consists of nodes and edges, where each node represents a step or task in the pipeline, and each edge represents a dependency or order between the tasks. A DAG has no cycles, meaning there is no way to start at one node and return to it by following the edges. A DAG can help visualize and organize the pipeline, as well as facilitate parallel execution, fault tolerance, and reproducibility.


Question #5

Which of the following tests should be performed at the production level before deploying a newly retrained model?

Reveal Solution Hide Solution
Correct Answer: B

Performance testing is a type of testing that should be performed at the production level before deploying a newly retrained model. Performance testing measures how well the model meets the non-functional requirements, such as speed, scalability, reliability, availability, and resource consumption. Performance testing can help identify any bottlenecks or issues that may affect the user experience or satisfaction with the model. Reference: [Performance Testing Tutorial: What is, Types, Metrics & Example], [Performance Testing for Machine Learning Systems | by David Talby | Towards Data Science]



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