AIP-210 RELIABLE MOCK TEST | AIP-210 LATEST EXAM PDF

AIP-210 Reliable Mock Test | AIP-210 Latest Exam Pdf

AIP-210 Reliable Mock Test | AIP-210 Latest Exam Pdf

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Tags: AIP-210 Reliable Mock Test, AIP-210 Latest Exam Pdf, AIP-210 Interactive Questions, AIP-210 Exam Registration, Customizable AIP-210 Exam Mode

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CertNexus AIP-210 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Recognize relative impact of data quality and size to algorithms
  • Engineering Features for Machine Learning
Topic 2
  • Identify potential ethical concerns
  • Analyze machine learning system use cases
Topic 3
  • Transform numerical and categorical data
  • Address business risks, ethical concerns, and related concepts in operationalizing the model
Topic 4
  • Design machine and deep learning models
  • Explain data collection
  • transformation process in ML workflow

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AIP-210 Latest Exam Pdf & AIP-210 Interactive Questions

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q76-Q81):

NEW QUESTION # 76
An AI practitioner incorporates risk considerations into a deployment plan and decides to log and store historical predictions for potential, future access requests.
Which ethical principle is this an example of?

  • A. Safety
  • B. Transparency
  • C. Fairness
  • D. Privacy

Answer: B

Explanation:
Explanation
Transparency is an ethical principle that describes the degree to which an AI system can provide clear and understandable information about its inputs, outputs, processes, and decisions. Transparency can help increase trust and confidence among users and stakeholders, as well as enable accountability and responsibility for the system's actions and outcomes. Logging and storing historical predictions for potential, future access requests is an example of transparency, as it can help provide evidence and explanation for the system's recommendations, as well as facilitate auditing and feedback.


NEW QUESTION # 77
Which of the following sentences is TRUE about the definition of cloud models for machine learning pipelines?

  • A. Infrastructure as a Service (IaaS) can provide CPU, memory, disk, network and GPU.
  • B. Software as a Service (SaaS) can provide AI practitioner data science services such as Jupyter notebooks.
  • C. Platform as a Service (PaaS) can provide some services within an application such as payment applications to create efficient results.
  • D. Data as a Service (DaaS) can host the databases providing backups, clustering, and high availability.

Answer: B

Explanation:
Explanation
Cloud models are service models that provide different levels of abstraction and control over computing resources in a cloud environment. Some of the common cloud models for machine learning pipelines are:
Software as a Service (SaaS): SaaS provides ready-to-use applications that run on the cloud provider's infrastructure and are accessible through a web browser or an API. SaaS can provide AI practitioner data science services such as Jupyter notebooks, which are web-based interactive environments that allow users to create and share documents that contain code, text, visualizations, and more.
Platform as a Service (PaaS): PaaS provides a platform that allows users to develop, run, and manage applications without worrying about the underlying infrastructure. PaaS can provide some services within an application such as payment applications to create efficient results.
Infrastructure as a Service (IaaS): IaaS provides access to fundamental computing resources such as servers, storage, networks, and operating systems. IaaS can provide CPU, memory, disk, network and GPU resources that can be used to run machine learning models and applications.
Data as a Service (DaaS): DaaS provides access to data sources that can be consumed by applications or users on demand. DaaS can host the databases providing backups, clustering, and high availability.


NEW QUESTION # 78
In a self-driving car company, ML engineers want to develop a model for dynamic pathing. Which of following approaches would be optimal for this task?

  • A. Reinforcement learning
  • B. Dijkstra Algorithm
  • C. Unsupervised Learning
  • D. Supervised Learning.

Answer: A

Explanation:
Explanation
Reinforcement learning is a type of machine learning that involves learning from trial and error based on rewards and penalties. Reinforcement learning can be used to develop models for dynamic pathing, which is the problem of finding an optimal path from one point to another in an uncertain and changing environment.
Reinforcement learning can enable the model to adapt to new situations and learn from its own actions and feedback. For example, a self-driving car company can use reinforcement learning to train its model to navigate complex traffic scenarios and avoid collisions .


NEW QUESTION # 79
Which database is designed to better anticipate and avoid risks of AI systems causing safety, fairness, or other ethical problems?

  • A. Asset
  • B. Code Repository
  • C. Incident
  • D. Configuration Management

Answer: C

Explanation:
Explanation
An incident database is a database that is designed to better anticipate and avoid risks of AI systems causing safety, fairness, or other ethical problems. An incident database collects and stores information about incidents or events where AI systems have caused or contributed to negative outcomes or harms, such as accidents, errors, biases, discriminations, or violations. An incident database can help identify patterns, trends, causes, impacts, and solutions for AI-related incidents, as well as provide guidance and best practices for preventing or mitigating future incidents.


NEW QUESTION # 80
What is the open framework designed to help detect, respond to, and remediate threats in ML systems?

  • A. MITRE ATT&CK Matrix
  • B. Threat Susceptibility Matrix
  • C. OWASP Threat and Safeguard Matrix
  • D. Adversarial ML Threat Matrix

Answer: D

Explanation:
Explanation
The Adversarial ML Threat Matrix is an open framework designed to help detect, respond to, and remediate threats in ML systems. The Adversarial ML Threat Matrix is inspired by the MITRE ATT&CK Matrix1, which is a framework for describing cyberattacks across various stages of an attack lifecycle. The Adversarial ML Threat Matrix adapts this framework to address specific threats and vulnerabilities in ML systems, such as data poisoning, model stealing, model evasion, or model inversion2. The Adversarial ML Threat Matrix provides a structured way to organize and classify adversarial techniques, tactics, procedures, examples, and mitigations for ML systems2.


NEW QUESTION # 81
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