AM2 Mock Interview
Instruction and application
In ProgressIn our last session, we had a mock interview to practice answering questions you may encounter in your work-based project. Now, let's look at questions for your portfolio.
As a reminder, there are a few key elements to consider when being interviewed.
Listen- If you’re unsure what the question is asking, invite the assessor to repeat/rephrase the question. Or ask clarifying questions!
- Listen for invitations to address certain standards in the assessment plan.
- Take notes on the key words during questions. These can be made on a document on screen
Answer- Quality beats quantity
- Apprenticeships are all about in-work impact - make sure to talk about the impact/value you’ve added to the business!
- Link back to the grading criteria - after all, these are the main thing you’re trying to demonstrate in your assessment!Remember, there are different grading criteria for the portfolio and WBP. Make sure you reference the portfolio criteria!
- Don’t assume the assessor to be a technical expert in your business/field
Evidence- Remember, being able to explain the theory/technical side of your portfolio will help the assessor validate your evidence.
- For Portfolio KSB’s ONLY: You can introduce any additional evidence you wish the assessor to consider (if you didn’t cover everything in your portfolio - here’s a good chance). This extra evidence must be signed off by your manager.
Action Item: Have a Mock Interview
Instructions
- Review the questions below
- Pick the questions you would like to practice with a peer. Try to complete as many as you can!
Timing
3 minutes
to review the questions, then start asking them.
- Ask one question at a time, specify if it's from the WBP or Portfolio, then allow about 3 minutes for the answer.
- Set aside 1 minute to take note of the feedback.
- Once 20 minutesare complete, you will return to the main room for questionsFeedback Guidelines
As a reminder, while you review your peers' work, think of the four pillars of good feedback:
- Constructive
- Specific
- Justified
- Kind
Detailed AIMLF KSB mapping to be finalised — reference the Machine Learning Engineer standard.
Questions - Monitoring and Performance: "How do you typically monitor deployed ML models in your role to ensure optimal performance, reliability, and drift detection? Can you give an example of a specific monitoring technique you use?"
Problem Solving:
"Imagine a scenario where a critical ML inference pipeline goes down. How would you identify and escalate the incident, and what steps would you take to communicate the issue and mitigate its operational impact?"
Regulatory Compliance:
"Describe how you ensure that your use of data and models complies with information security standards, responsible AI, and ethical practices within your organisation. What are some key pieces of legislation that influence your ML governance policies?"
Continuous Improvement:
"How do you approach identifying and addressing technical debt within ML systems you work on? What techniques do you use to ensure continuous improvement and capture good practices?"
Model Design and Experimentation:
"Beyond just monitoring, how have you actively compared and contrasted different modelling approaches to truly optimise performance for specific use cases? Can you explain how you've leveraged different training or ingestion strategies (streaming, batch, on-demand) and justify your choices?"
Problem Solving (Deep Dive):
"Walk me through a complex ML incident you've encountered. How did your root cause investigation lead to a resolution, and how did you effectively troubleshoot and communicate with stakeholders? How did your approach demonstrate a justification for maintaining business continuity?"
Continuous Improvement (Sustainability):
"Describe a time you evaluated an opportunity to extract more value from an existing ML product. How did you balance costs, environmental impact (considering net-zero goals), and potential operating benefits? How have you identified and assessed new technologies that offer increased performance, and how would you evaluate the impact of implementing such changes?"
Continuous Professional Development:
"Outline your personal strategy for keeping up to date with emerging and contemporary technologies in machine learning, data engineering, and AI. How have you specifically evaluated the impact that staying current has had on your own professional growth and your ability to deliver sustainable products and services?"