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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Exam AIP-210

A Certified Artificial Intelligence Practitioner (CAIP) is a data professional that can implement the power of AI and machine learning to solve business challenges using various modeling techniques. CAIPs can utilize AI to automate processes, reduce costs, drive down completion times, and perform operational tasks that allow humans to perform higher level work. They have advanced knowledge of the engineering features of a dataset to prepare it for use in a machine learning model, the ability to select algorithms and perform model training and model handoff, and an understanding of the ethics and oversight required to create ethical outcomes with AI. Certified AI Practitioners enable organizations to enhance customer experiences and propel innovation to achieve their AI goals.

This certification exam is designed for practitioners who are seeking to demonstrate a vendorneutral, cross-industry skill set within AI and with a focus on ML that will enable them to design, implement, and hand off an AI solution or environment. Exposure in a professional environment: 1 to 3 years. 

Exam Details

Number of Items: The exam should comprise 80 scored and 10 trial items with adequate time.
Passing Score: TBA
Duration: 120 minutes (Note: exam time includes 5 minutes for reading and signing the Candidate
Agreement and 5 minutes for the Pearson VUE testing system tutorial.)
Exam Options: In person at Pearson VUE test centers or online with Pearson OnVUE online proctoring.
Item Formats: Multiple Choice / Multiple Response

Exam Objectives

Domain 1.0 Understanding the Artificial Intelligence Problem (26%)

Objective 1.1 Describe how artificial intelligence and machine learning are used to solve business (including commercial, government, public interest, and research) problems
Objective 1.2 Analyze the use cases of ML algorithms to rank them by their success probability
Objective 1.3 Research Learning Systems [Identify business case for image recognition; NLP; Speech recognition; Predictive & recommendation systems; Discovery & diagnostic systems; Robotics and autonomous systems]
Objective 1.4 Analyze machine learning system use cases
Objective 1.5 Communicate with stakeholders
Objective 1.6 Identify potential ethical concerns

Domain 2.0 Engineering Features for Machine Learning (20%)

Objective 2.1 Recognize relative impact of data quality and size to algorithms
Objective 2.2 Explain data collection/transformation process in ML workflow (transformations include standardization; normalization; log, square-root, and logit transformations)
Objective 2.3 Work with textual, numerical, audio, or video data formats
Objective 2.4 Transform numerical and categorical data
Objective 2.5 Address business risks, ethical concerns, and related concepts in data exploration/ feature engineering

Domain 3.0 Training and Tuning ML Systems and Models (24%)

Objective 3.1 Design machine and deep learning models [Differentiate types of ML algorithms; differentiate types of DL algorithms; design for pattern recognition in predictive models]
Objective 3.2 Optimize the algorithm (e.g., structure, run time, in your PDF - delete regularizing parameters, tuning hyperparameters)
Objective 3.3 Train, validate, and test data subsets 
Objective 3.4 Evaluate the model
Objective 3.5 Address business risks, ethical concerns, and related concepts in training and tuning

Domain 4.0 Operationalizing ML Models (30%)

Objective 4.1 Deploy a model
Objective 4.2 Secure a pipeline (includes maintenance)
Objective 4.3 Maintain the model postproduction
Objective 4.4 Address business risks, ethical concerns, and related concepts in operationalizing the model 

View Online CertNexus Certified Artificial Intelligence Practitioner (CAIP) AIP-210 Free Questions

1. 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. Caffe
B. Keras
C. Microsoft Cognitive Services
D. TensorBoard
Answer: A

2. Which of the following principles supports building an ML system with a Privacy by Design methodology?
A. Avoiding mechanisms to explain and justify automated decisions.
B. Collecting and processing the largest amount of data possible.
C. Understanding, documenting, and displaying data lineage.
D. Utilizing quasi-identifiers and non-unique identifiers, alone or in combination.
Answer: C

3. A data scientist is tasked to extract business intelligence from primary data captured from the public.
Which of the following is the most important aspect that the scientist cannot forget to include?
A. Cyberprotection
B. Cybersecurity
C. Data privacy
D. Data security
Answer: C

4. For a particular classification problem, you are tasked with determining the best algorithm among SVM, random forest, K-nearest neighbors, and a deep neural network. Each of the algorithms has similar accuracy on your data. The stakeholders indicate that they need a model that can convey each feature's relative contribution to the model's accuracy.
Which is the best algorithm for this use case?
A. Deep neural network
B. K-nearest neighbors
C. Random forest
D. SVM
Answer: C

5. Which three security measures could be applied in different ML workflow stages to defend them against malicious activities? (Select three.)
A. Disable logging for model access.
B. Launch ML Instances In a virtual private cloud (VPC).
C. Monitor model degradation.
D. Use data encryption.
E. Use max privilege to control access to ML artifacts.
F. Use Secrets Manager to protect credentials.
Answer: BDF

6. A healthcare company experiences a cyberattack, where the hackers were able to reverse-engineer a dataset to break confidentiality.
Which of the following is TRUE regarding the dataset parameters?
A. The model is overfitted and trained on a high quantity of patient records.
B. The model is overfitted and trained on a low quantity of patient records.
C. The model is underfitted and trained on a high quantity of patient records.
D. The model is underfitted and trained on a low quantity of patient records.
Answer: B