IBM Artificial Intelligence Fundamental Associate (C1000-206) Certification Sample Questions

The purpose of this Sample Question Set is to provide you with information about the IBM Artificial Intelligence Fundamentals v1 - Associate exam. These sample questions will make you very familiar with both the type and the difficulty level of the questions on the C1000-206 certification test. To get familiar with real exam environment, we suggest you try our Sample IBM Artificial Intelligence Fundamental Associate Certification Practice Exam. This sample practice exam gives you the feeling of reality and is a clue to the questions asked in the actual IBM Certified Artificial Intelligence v1 - Associate certification exam.

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IBM C1000-206 Sample Questions:

01. A robotics research lab wants one system that learns to recognize objects from image data with neural networks, then applies explicit, inspectable logical rules about those objects to answer questions. It needs both learned pattern recognition and rule-based reasoning.

Which AI paradigm describes this design?

a) Symbolic AI

b) Neuro-symbolic artificial intelligence

c) Deep learning

d) Statistical NLP

02. An airline runs a traditional ML model that forecasts seat demand and is adding an LLM assistant that revenue analysts question in plain language.

How do the two systems differ in their inputs and outputs?

a) The forecaster takes prompts and gives variable replies; the assistant takes structured inputs and gives fixed, structured outputs

b) Both give identical, fixed outputs for identical inputs, because both are machine learning models trained on historical data

c) The assistant needs its prompts supplied as labeled, structured records, in the same format the forecaster uses for its inputs

d) The assistant takes prompts and gives variable replies; the forecaster takes structured inputs and gives structured outputs

03. A university IT team is building semantic search over its help-desk articles.

Which sequence of steps is correct?

a) 1) Embed each article with an embedding model 2) Store and index the vectors in a vector database 3) Embed the user's query with the same model 4) Compare the query vector with the stored vectors by similarity

b) 1) Store and index the vectors in a vector database 2) Embed each article with an embedding model 3) Embed the user's query with the same model 4) Compare the query vector with the stored vectors by similarity

c) 1) Embed each article with an embedding model 2) Store and index the vectors in a vector database 3) Compare the query vector with the stored vectors by similarity 4) Embed the user's query with the same model

d) 1) Embed each article with an embedding model 2) Embed the user's query with the same model 3) Compare the query vector with the stored vectors by similarity 4) Store and index the vectors in a vector database

04. A retailer's AI shopping assistant will draw on each customer's purchase history. The product owner commits that every customer receives a clear statement of how their personal information will be used and protected.

Which of IBM's five pillars of AI ethics does this commitment put into practice?

a) Transparency, because the pillar calls for users to see how the shopping service works

b) Explainability, because the pillar calls for clarity on what went into each recommendation

c) Privacy, because the pillar calls for explicit assurances to users about how their data is handled

d) Fairness, because the pillar calls for equitable treatment of individuals and groups

05. A home appliance manufacturer hires three contract annotators to label 20,000 warranty complaints as minor, serious or critical. Each annotator works from personal opinion with no shared guideline, and a quality check finds identical complaint text labeled differently. Every record and category was included.

Which type of AI bias is the training data exposed to?

a) Exclusion bias from important records being left out of the data

b) Recall bias introduced when labels are applied inconsistently

c) Prejudice bias from stereotypes in the data

d) Confirmation bias from reliance on existing trends

06. A telecom provider runs two initiatives. Initiative 1 forecasts which subscribers will cancel next quarter from billing and usage tables. Initiative 2 is an internal assistant that answers staff questions from thousands of policy manuals and free-text chat transcripts.

Which pairing of primary data type to initiative is correct?

a) Both rely mainly on semi-structured data, because each source holds metadata that tags its records

b) Both rely on structured data, as generative models need tables

c) Initiative 1 relies mainly on structured data; Initiative 2 mainly on unstructured data

d) Initiative 1 relies on unstructured data; Initiative 2 on structured data

07. A furniture retailer's marketing team asks an LLM for product descriptions. The output keeps missing the company's fixed three-part layout and tone, even though the instructions alone have been reworded several times. The team has three approved descriptions.

Which technique is the most appropriate?

a) Few-shot prompting with the approved descriptions

b) Full fine-tuning of the model on the approved descriptions

c) Chain-of-thought prompting that requests reasoning steps first

d) Retrieval-augmented generation over the full product catalog

08. A retail chain with an online shop is reviewing five proposed AI projects.

Which two (2) projects are supervised learning problems? (Choose two.)

a) Grouping customers into segments when no segment categories exist in advance

b) Predicting whether a new transaction is fraudulent from past transactions each already marked fraudulent or legitimate

c) Finding products often bought together in one basket

d) Training a pricing agent that is rewarded whenever profit rises after a price change

e) Forecasting each store's monthly revenue from past recorded revenue

09. A telecom operator wants one large pretrained model to handle spam detection, sentiment analysis and ticket routing. It has a labeled dataset for each task, cannot afford to store a full model copy per task, and has found hand-written prompts brittle: small wording changes swing accuracy.

Which approach fits these constraints?

a) Fully fine-tuning a separate copy of the model for each task

b) Few-shot prompting with more hand-written examples added to each prompt

c) Retrieval-augmented generation over the datasets

d) Prompt tuning a separate soft prompt for each task

10. The CFO of a sportswear brand is comparing RAG and fine-tuning for a planned product assistant and asks how the costs of each behave over time.

Which comparison is accurate?

a) RAG spreads cost over the system's life through data pipeline and retrieval store upkeep, while fine-tuning front-loads training compute and repeats it at each retrain

b) RAG needs a large upfront GPU training run, while fine-tuning carries only ongoing data pipeline costs after deployment

c) Both become one-time costs once deployed, because neither needs further spending after launch

d) Fine-tuning removes infrastructure cost entirely, because parameter-efficient methods need no compute

Answers:

Question: 01 Answer: b	Question: 02 Answer: d	Question: 03 Answer: a	Question: 04 Answer: c	Question: 05 Answer: b
Question: 06 Answer: c	Question: 07 Answer: a	Question: 08 Answer: b, e	Question: 09 Answer: d	Question: 10 Answer: a

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