AI Readiness (software developer) Assessment helps find skilled candidates with AI skills
The AI assessment assesses candidates' understanding of fundamental AI principles and their capacity to solve practical AI tasks. This screening test helps in identifying the existing employees who are ready to start working in AI, so that the companies can have a clear roadmap for AI adoption.
Available on request
Coding,Domain
Intermediate
30 minutes
30 questions
Software Tester, Quality Analysts, Test engineer, Data analyst, Software developers, Game developers
English India
About Mercer | Mettl AI Readiness (software developer) Assessment
By understanding the level of AI readiness, businesses can identify areas that require improvement and explore potential AI applications to enhance their operations. The AI Readiness (software developer) Assessment assists in skill gaps identification, training needs assessment, talent identification, skill development, talent pipeline building, resource optimization and embracing AI while remaining competitive in the business landscape.
What is inside this AI Readiness (software developer) Assessment?
The test consists of thirty questions with an intermediate difficulty level. The total test duration is thirty minutes.
What skills does this AI Readiness (software developer) Test cover?
The test consists of the following skills:
- Programming languages: This section includes Python, Tensorflow and PyTorch.
- Data handling and manipulation: This section includes Pandas, Numpy, Scikit-learn, data preprocessing techniques and data visualization tools (Matplotlib, Tableau).
- Machine learning (ML) and deep learning (DL): This section includes ML concepts - supervised learning, unsupervised learning, ML algorithms - decision trees, linear regression, clustering and DL concepts - neural networks, CNNs, RNNs, and transformers.
- Model evaluation and validation: This section includes model evaluation methods, cross-validation, hyperparameter tuning, and performance metrics.
- SDLC: This section includes version control (Git), code analysis (SonarQube), code review, and debugging.
- Gen AI: This section includes Large Language Models – fundamentals, and knowledge of API integration of Generative AI tools.
- Prompting: This section includes biases in AI algorithms and ethical considerations while prompting.
- Cloud computing: This section includes cloud services for AI (e.g., Amazon SageMaker, Google Cloud AI Platform) and security considerations in the cloud.
What roles can you assess using the AI Readiness (software developer) Test?
- Data analysts
- Software developers
- Test engineers
- Software testers
- Quality analysts
- Game developers
AI Readiness (software developer) Assessment competency framework
Get a detailed look inside the test
AI Readiness (software developer) Assessment competencies under scanner
AI Readiness (software developer) skills
Competencies:
This section includes Python, Tensorflow and PyTorch.
This section includes Pandas, Numpy, Scikit-learn, data preprocessing techniques and data visualization tools (Matplotlib, Tableau)
This section includes ML concepts - supervised learning, unsupervised learning, ML algorithms - decision trees, linear regression, clustering and DL concepts - neural networks, CNNs, RNNs, and transformers.
This section includes model evaluation methods, cross-validation, hyperparameter tuning, and performance metrics.
This section includes version control (Git), code analysis (SonarQube), code review, and debugging.
This section includes Large Language Models – fundamentals, and knowledge of API integration of Generative AI tools.
This section includes biases in AI algorithms and ethical considerations while prompting.
This section includes cloud services for AI (e.g., Amazon SageMaker, Google Cloud AI Platform) and security considerations in the cloud.
Customize this AI Readiness (software developer) Assessment
Flexible customization options to suit your needs
Choose easy, medium or hard questions from our skill libraries to assess candidates of different experience levels.
Add multiple skills in a single test to create an effective assessment. Assess multiple skills together.
Add, edit or bulk upload your own coding questions, MCQ, whiteboarding questions & more.
Get a tailored assessment created with the help of our subject matter experts to ensure effective screening.
The Mercer | Mettl AI Readiness (software developer) Assessment advantage
- Industry Leading 24/7 Support
- State of the art examination platform
- Inbuilt Cutting Edge AI-Driven Proctoring
- Simulators designed by developers
- Tests Tailored to Your business needs
- Support for 20+ Languages in 80+ Countries Globally
Frequently Asked Questions (FAQs)
1. Why is measuring AI readiness important?
The assessment can help companies identify existing talent with strong AI potential, contributing to the development of an AI talent pipeline. This information can be instrumental in creating a strategic roadmap for AI adoption within the organization.
2. How will this assessment help in prioritizing AI skill development?
This assessment establishes a baseline for measuring AI skill growth over time. By reassessing developers periodically, organizations can track progress and adjust training efforts accordingly.
3. Does the assessment measure the impact of AI adoption?
No, the assessment does not measure the specific impact of AI adoption within the organization. The impact of AI implementation varies widely based on factors such as industry, business model, organizational structure, and existing technological infrastructure. The assessment focuses on evaluating developer readiness to work with AI technologies, which is a foundational step towards successful AI adoption.
4. Does the assessment measure the cultural fit for AI adoption?
This assessment focusses specifically on the technical aspects of AI readiness. The behavioural aspects such as identifying high risk profiles, etc. can also be added to this assessment or can be offered as a separate assessment.
