A short baseline you’ll revisit at the end of Day 2. Sign in with the code you created in Exercise 1. ~5–7 min.
Section AAbout You
A1 — Primary Role
What best describes your primary professional role?
Cost Engineer / Estimator
Project Controls Manager / Analyst
Scheduler / Planning Engineer
Risk Manager / Analyst
Project Manager
Data Analyst / Business Intelligence
Other
A2 — Experience
How many years of experience do you have in project controls or cost engineering?
Less than 3 years
3–7 years
8–15 years
More than 15 years
A3 — Industry Sector
What is your primary industry sector?
Oil & Gas / Energy
Infrastructure / Transportation
Defence / Government
Construction / EPC
Mining / Resources
Other
Your Participant Code
Enter the code you created in Exercise 1. This links your survey answers together across the bootcamp.
A1, A2 & A3 required to continue
Section BSelf-Assessment
The five stages below come from the bootcamp’s AI adoption maturity framework. Read them carefully before answering.
1
Exploratory
No data analytics expertise or limited skills — just beginning to explore what’s possible
2
Investment
Aware of the need; beginning to commit to adoption through tools, training, or hiring
3
Experience
Usable data exists; some hands-on experience with analytics tools and techniques
4
Experimental
AI/ML systems in place; actively driving data-led decisions and learning from outcomes
5
Leading
AI/ML is central to how your business operates; you drive revenue-based learning technology
B1 — Personal Level
Where would you place yourself personally on this scale right now?
1Exploratory
2Investment
3Experience
4Experimental
5Leading
B2 — Organisation Level
Where would you place your organisation on this scale right now?
1Exploratory
2Investment
3Experience
4Experimental
5Leading
B3 — Confidence in Those Ratings
How confident are you in those self-assessments?
Very confident — I know exactly where I stand
Fairly confident — good estimate, could be off by one level
Not very confident — I am guessing
I don’t have enough information to answer
Section CKnowledge Check
For each question, choose the best answer and rate how confident you are. Both parts matter. There is no penalty for guessing.
Question 1 of 5
Artificial intelligence, machine learning, and deep learning are the same thing.
True
False
Not sure
Confidence in your answer
Very confident
Somewhat confident
Just guessing
Question 2 of 5
A regression model is used to predict:
A category (e.g., will this project overrun? Yes or No)
A continuous value (e.g., what will the final cost be?)
Either, depending on the data
I’m not sure
Confidence in your answer
Very confident
Somewhat confident
Just guessing
Question 3 of 5
You are building a model to predict a project’s Estimate at Completion (EAC) at 30% complete. Which of the following would be the target variable — the thing you want the model to predict?
The project’s original budget
The CPI at 30% complete
The actual final cost at project completion
The control budget at the time of prediction
I’m not sure
Confidence in your answer
Very confident
Somewhat confident
Just guessing
Question 4 of 5
Unsupervised learning algorithms require labelled training data — examples where the correct answer is already known.
True
False
Not sure
Confidence in your answer
Very confident
Somewhat confident
Just guessing
Question 5 of 5
In the context of AI and machine learning, “overfitting” means:
The model is too accurate on training data and performs poorly on new, unseen data
The model has been trained on too many features
The training dataset is too large for the algorithm to process efficiently
The model’s predictions are consistently too high
I’m not sure
Confidence in your answer
Very confident
Somewhat confident
Just guessing
Answer all 5 questions to continue
Section DExperience Inventory
D1 — AI Tool Usage
In the past 12 months, have you used AI or machine learning tools in your professional work?
Yes — regularly (weekly or more)
Yes — occasionally (a few times)
No, but I have experimented personally
No
D2 — Tool or Technique (optional)
Briefly describe the AI/ML tool or technique you used. Leave blank if not applicable.
D3 — Predictive Modelling
Have you ever built or contributed to a predictive model of any kind — in Excel, Python, a BI tool, or purpose-built software?
Yes — I built one myself
Yes — I contributed to one built by others
No, but I have used outputs from one
No
Section EExpectation Setting
E1 — Your Goal
In one sentence, what is the single most important thing you want to leave this bootcamp able to do?
E2 — Biggest Concern (optional)
What is your biggest concern or uncertainty about AI/ML in your work context?
E1 required to submit
Survey Complete
Your responses have been saved. At the end of Day 2 you will be shown your answers and asked to re-evaluate them with what you know then.
Keep a note of your participant code below — you will need it on Day 2.
