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Official specifications published by BCS
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Skills this practice test covers
- Recall the key AI terms and the milestones from the Dartmouth conference of 1956 to large language models.
- Separate ethics from law and name the UK AI Principles for responsible AI.
- Tell machine learning from deep learning, and classification from clustering.
- Judge data against the five quality characteristics and name the risks of handling data in AI.
- Choose an Agile, Waterfall or Hybrid approach and the governance an AI project needs.
Explore related topics
EC-AI exam content
6 domains · 23 topics · 135 questions
An Introduction to AI and Historical Development15%21 questions
This domain sets out the key terms, from human intelligence to machine learning, and the milestones of AI history. Candidates also tell narrow AI, which exists today, from general AI, which remains theoretical.
Skills tested
- Define human intelligence, artificial intelligence, machine learning and the scientific method.
- Place AI milestones in order, from the Dartmouth conference of 1956 and the AI winters to large language models.
- Give examples of narrow AI, such as speech recognition, and explain why general AI is still hypothetical.
Questions by topic
- Definitions of key AI terms8
- Types of AI5
- Key milestones in AI development8
Ethical and Legal Considerations15%21 questions
Candidates explain what ethics is and how it differs from law. They name the main concerns, such as bias, privacy and the effect on jobs, and the principles that guide responsible AI, including the UK AI Principles and ISO 42001.
Skills tested
- Explain how something can be legal but unethical, or ethical but illegal.
- Show how biased training data leads to biased AI output.
- List the UK AI Principles, from safety, security and robustness to contestability and redress.
- Recognise ISO 42001 as a management system standard for AI.
Questions by topic
- Role of ethics in AI5
- Guiding principles for ethical AI9
- Key ethical concerns in AI7
Enablers of Artificial Intelligence15%20 questions
This domain covers what makes AI work in practice: robotics, machine learning and deep learning, and everyday examples such as generative AI tools and the Internet of Things.
Skills tested
- Tell intelligent robots from non-intelligent ones, and name types such as industrial robots, nanobots and humanoids.
- Explain robotic process automation (RPA) as software robots for repetitive business tasks.
- Place deep learning inside machine learning, and machine learning inside AI.
- Match machine learning concepts such as prediction, classification and clustering to examples.
Questions by topic
- Robotics in AI5
- Machine learning description4
- Common ML concepts5
- Common examples of AI6
Finding and Using Data in Artificial Intelligence20%27 questions
One of the two largest domains, it defines key data terms, sets out what good-quality data looks like and lists the risks in handling it. Candidates also follow the stages of the machine learning process and name ways to present data.
Skills tested
- Sort data into structured, semi-structured and unstructured types, with an example of each.
- Test data against the five quality characteristics: accuracy, completeness, uniqueness, consistency and timeliness.
- Name the risks of handling data in AI, such as bias, misinformation and legal restrictions.
- Put the machine learning process in order, from analysing the problem to the final review.
Questions by topic
- Data quality characteristics6
- Key data terms6
- Data in the ML process5
- Data visualisation techniques and tools3
- Key generative AI terms3
- Risks of handling data in AI4
Using AI in Your Organisation20%27 questions
The other large domain looks at AI from the business side. Candidates find tasks that suit automation, compare project management approaches and plan the governance that an AI project needs.
Skills tested
- Spot repetitive, rule-based tasks that AI could take over.
- Choose an Agile, Waterfall or Hybrid approach for an AI project.
- Plan compliance, risk management and lifecycle governance for an AI system.
Questions by topic
- Project management approaches9
- Governance activities for AI implementation9
- Opportunities for AI in organisations9
Future Planning and Impact - Human Plus Machine15%19 questions
This domain looks ahead to new roles, AI in sectors such as healthcare, finance and education, and the effects of AI on society, the economy and the environment. BCS does not test the names or duties of specific AI job roles.
Skills tested
- Describe how AI creates new roles and changes existing jobs.
- Give examples of AI in sectors such as marketing, healthcare, finance and transportation.
- Weigh the benefits of AI against challenges such as job losses, bias and environmental cost.
Questions by topic
- Future of AI6
- AI's impact on society6
- AI uses in the real world4
- AI roles and career opportunities3
Requirements
- No formal prerequisites: anyone can book the exam.
- Recommended: extra independent study for candidates with no prior AI knowledge.
- Recommended: a course with a BCS accredited training provider.
About the EC-AI exam
With the Essentials Certificate in Artificial Intelligence (EC-AI), BCS checks a candidate's grasp of the key terms, history, ethics and everyday uses of AI. It is an entry-level exam for professionals in any industry, and no prior AI knowledge is required. The syllabus moves from definitions and milestones to data, machine learning, the adoption of AI in an organisation and the effect of AI on jobs and society. Passing earns a certificate that does not expire.
The PlanetCert practice test spreads its questions across all six domains, with the most on data and on the organisational use of AI, the two largest. BCS questions at this level ask a candidate to recall or explain, so the explanations focus on exact meanings, such as how classification differs from clustering. Flashcard mode suits the definitions and milestones, from the Dartmouth conference to the AI winters. Brainy AI Assistant can explain how ethics differs from law, or why generative AI is not the same thing as general AI.
How to earn the certification
- Earns
- BCS Essentials Certificate in Artificial IntelligenceEssentials
The certificate does not expire.
- After
- FC-AI BCS Foundation Certificate in Artificial Intelligence
The next BCS step in AI, which treats the same subjects in more depth.
- FC-AI BCS Foundation Certificate in Artificial Intelligence
How to prepare for EC-AI
- 1
Read the BCS syllabus line by line
The syllabus lists every term and example that a question can use. It also says what is not tested, such as the duties of specific AI job roles.
- 2
Draw a timeline of AI history
Place the Dartmouth conference, the two AI winters, the rise of big data and the arrival of large language models on one line. Then note what changed at each point.
- 3
Learn data terms in contrasting pairs
Contrast structured with unstructured data, and classification with clustering. The data domain is one of the two largest, so give it extra time.
- 4
Test a generative AI tool critically
Ask a generative AI tool about a familiar subject and check the answer for errors and bias. This makes the ethics and data risk topics concrete.
- 5
Finish with the full practice test
Take the EC-AI practice test in Timed Mode once every domain has been covered. Ask Brainy AI Assistant to explain any pair of terms that still gets mixed up.
Start a timed EC-AIrun
Who EC-AI is for
- Managers who need to judge where AI could help a team.
- Staff in non-technical roles who use AI tools at work and want to understand them.
- Students and career changers who want a first qualification in AI.
- Candidates who want a base before the BCS Foundation Certificate in Artificial Intelligence.
Typical job titles: Business analyst · Project manager · Operations manager · IT support analyst · Digital transformation lead
EC-AI glossary
16 terms from the exam objectives and the vendor's official material, each defined in a sentence.
- AI winters
- Periods when funding for AI research and interest in it fell sharply.
- Asilomar principles
- A set of guidelines for the responsible development of AI.
- Classification
- Sorting data into categories that have been defined in advance.
- Clustering
- Grouping similar data points when no categories have been defined in advance.
- Dartmouth conference of 1956
- The 1956 meeting that is seen as the start of AI as a field of study.
- Deep learning
- A type of machine learning that uses neural networks with many layers.
- General AI
- Hypothetical AI that could understand or learn any intellectual task that a human can, which has not yet been achieved.
- Generative AI
- AI models that create new text, images or other content based on the data they were trained on.
- ISO 42001
- The international standard for an AI management system, which helps an organisation govern its use of AI.
- Large language models (LLMs)
- Deep learning models trained on very large datasets that can recognise, summarise, translate and generate text.
- Lifecycle governance
- Managing, monitoring and reviewing an AI model for as long as it is in use.
- Narrow AI
- AI that does one task within a well-defined area, such as speech recognition or translation.
- Robotic Process Automation (RPA)
- Software robots that carry out repetitive, rule-based business tasks, such as copying data between systems.
- Semi-structured data
- Data that is not held in tables but still has some organisation, such as XML, JSON or emails with headers.
- UK AI Principles
- Five principles for AI in the UK: safety, security and robustness; transparency and explainability; fairness; accountability and governance; and contestability and redress.
- Unstructured data
- Data with no set order or structure, such as images, videos, social media posts and free text.
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