Practice Question Patterns and Scenario Interpretation
How to deconstruct the exam questions. A guide to the most common question patterns and how to interpret the scenarios.
Deconstructing the Exam Questions
The Google Cloud Professional Machine Learning Engineer exam is not a test of your ability to memorize facts. It's a test of your ability to apply your knowledge to solve real-world problems. The questions are all scenario-based, so it's important to be able to quickly interpret the scenarios and identify the key information.
1. The "Right Tool for the Job" Pattern
This is the most common question pattern on the exam. You will be given a scenario and asked to choose the best tool for the job. To answer these questions correctly, you need to have a deep understanding of the different ML tools on Google Cloud and when to use each of them.
Example:
You need to build a model to classify images of custom car parts. You have a large dataset of labeled images. Your team has no ML expertise. Which tool should you use?
- (A) Vision API
- (B) AutoML Vision
- (C) A custom TensorFlow model
- (D) BigQuery ML
Answer: (B) AutoML Vision is the best choice for this scenario because it allows you to train a custom image classification model without any ML expertise.
2. The "Troubleshooting" Pattern
In this question pattern, you will be given a scenario where something has gone wrong and asked to identify the cause of the problem. To answer these questions correctly, you need to have a good understanding of the common errors that can occur in ML systems and how to troubleshoot them.
Example:
You are training a model on Vertex AI. Your training job fails with a ResourceExhaustedError. What is the most likely cause of this error?
- (A) Your training job does not have permission to access a resource.
- (B) You have provided an invalid argument to a TensorFlow operation.
- (C) Your training job has run out of memory.
- (D) Your model is not able to handle the volume of requests that it is receiving.
Answer: (C) A ResourceExhaustedError almost always means that your training job has run out of memory.
3. The "Best Practices" Pattern
In this question pattern, you will be given a scenario and asked to choose the best way to do something. To answer these questions correctly, you need to have a good understanding of the best practices for building and maintaining ML systems on Google Cloud.
Example:
You want to safely and automatically deploy your model to production. You want to be able to quickly roll back to the previous version if the new model has any issues. Which deployment strategy is the best choice?
- (A) A manual deployment process
- (B) A blue/green deployment
- (C) A canary deployment
- (D) A shadow deployment
Answer: (B) A blue/green deployment is the safest way to deploy a new model to production because it allows you to test the new model in a separate environment before switching the traffic over to it.
Knowledge Check
?Knowledge Check
You are training a model to predict the price of a house. You train the model on a dataset that includes a feature for the size of the house in square feet. You then serve the model to users who provide the size of their house in square meters. What type of skew is this?