What Is AI+ Data?

Overview

AI+ Data is a AI CERTs credential. Apply AI techniques to data analysis, preparation, and intelligent data solutions. This PathKey™ guide summarizes the domains, format, cost signals, eligibility, and renewal pattern so you can decide before you book. Accumentum is an Army CA and Air Force COOL Approved Vendor. We teach preparation for the AI CERTs exam. AI CERTs sets exam content, fees, and policies.

Accumentum is an Army CA and Air Force COOL Approved Vendor. We teach preparation for the AI CERTs exam. AI CERTs sets exam content, fees, and policies.

Who it is for

• Professionals aiming to leverage AI and data analytics to uncover insights and drive strategic decision-making in data-centric environments.
• Leaders responsible for overseeing data-driven AI implementations who need to understand AI+ Data tools and their organizational impact.
• Individuals in tech or data solution sales seeking to effectively communicate the value of AI+ Data applications to clients or stakeholders.

Exam domains

Weights follow the official outline.

1. 01

   Introduction to AI and Data-Driven Technologies

   Overview of artificial intelligence, machine learning, and generative AI in data contexts, Understanding the role of AI in enhancing data analytics and processing, and Key differences between traditional data analysis and AI-driven data solutions.

2. 02

   AI+ Data Ecosystem Overview

   Exploration of the AI+ Data infrastructure and service offerings for data-driven AI, Overview of tools for building, managing, and deploying AI-enhanced data solutions, and Understanding integration of AI with data pipelines and analytics platforms.

3. 03

   Practical AI Use Cases in Data-Driven Business

   Identifying business challenges addressable through AI-enhanced data solutions, Examples of AI applications in sectors like finance, marketing, and healthcare, and Techniques for aligning business objectives with AI+ Data capabilities.

4. 04

   Foundation Models for Data Applications

   Introduction to foundation models tailored for data analytics and processing, Design considerations for selecting and deploying models in data-centric environments, and Understanding model performance and scalability for large-scale data tasks.

5. 05

   Responsible AI in Data Environments

   Principles of ethical AI, focusing on fairness, transparency, and accountability in data use, Strategies for identifying and mitigating bias in AI-driven data models, and Implementing responsible AI practices in data workflows and analytics.

6. 06

   Security for AI+ Data Solutions

   Best practices for securing data used in AI model training and inference, Techniques for protecting sensitive data in AI+ Data environments, and Understanding encryption, access controls, and data privacy in AI applications.

7. 07

   Governance and Compliance in AI+ Data

   Key governance frameworks for managing AI projects in data-centric contexts, Understanding regulatory standards impacting AI and data (e.g., GDPR, CCPA), and Tools for monitoring and auditing AI+ Data solutions for compliance.

8. 08

   Data Management for AI Applications

   Best practices for collecting, cleaning, and preparing data for AI model training, Building and managing data pipelines within the AI+ Data ecosystem, and Techniques for ensuring data quality, consistency, and relevance for AI workflows.

9. 09

   Deploying AI Solutions in AI+ Data

   Step-by-step process for deploying AI models in the AI+ Data environment, Configuring resources for optimal performance and scalability in data applications, and Monitoring and managing deployed AI models for accuracy and efficiency.

10. 10

    Preparing for the AI+ Data (AIDATA) Certification

    Overview of the AI CERTs AI+ Data (AIDATA) certification exam structure and objectives, Key topics and competencies tested in the certification exam, and Practice scenarios and questions to reinforce course concepts.

Exam format and cost

Questions

Confirm on the vendor site

Duration

Confirm on the vendor site

Format

Confirm question format on the vendor outline

Passing score

Confirm the passing score on the vendor site

Cost

Confirm the current exam fee on the vendor site before you register

Delivery

Confirm delivery options on the vendor site

Languages

Confirm available languages on the vendor site

Experience and eligibility

Familiarity with fundamental data concepts, such as databases, data processing, or analytics, is recommended to contextualize AI applications. A basic understanding of IT environments or data management systems to support learning about AI-driven data solutions.

Renewal

Renewal rules for AI+ Data are set by AI CERTs. Many credentials use a multi-year cycle with continuing education, a renewal fee, or a newer exam. Confirm the current cycle on the vendor site.

Roles this credential fits

• This foundational course is designed for individuals who work with AI technologies but do not necessarily develop AI/ML solutions, such as business analysts, IT managers, and sales professionals. It covers essential topics like fundamental AI concepts, practical use cases, and the ethical implementation of AI within data-centric environments. Participants will explore the AI+ Data ecosystem, learning how to identify opportunities for AI applications, understand design considerations for foundation models, and recognize the importance of responsible AI practices. The course also includes modules on security, compliance, and governance for AI-driven data solutions, ensuring learners are well-prepared for the AI CERTs AI+ Data certification exam. By the end of the training, participants will possess the knowledge to leverage AI for business innovation and understand the impact of AI on data-driven organizational strategies.
• AI & Data
• Teams standardizing on this credential for hiring or compliance

Official sources

• AI CERTs

Always confirm fees, dates, and eligibility on the vendor site before you register. Vendors change outlines and prices without notice.