8 of the 77 questions in this set, with the correct answer marked and every option explained.
1. You work on a team where the process for deploying a model into production starts with data scientists training different versions of models in a Kubeflow pipeline. The workflow then stores the new model artifact into the corresponding Cloud Storage bucket. You need to build the next steps of the pipeline after the submitted model is ready to be tested and deployed in production on AI Platform. How should you configure the architecture before deploying the model to production?
✗Create a new AI Platform model version - > Deploy model in test environment -> Validate model
Creating the production model version first means an unvalidated model already exists in the registry, which defeats the point of a gate.
✗Validate model -> Deploy model in test environment -> Create a new AI Platform model version
You cannot meaningfully validate a model before it is deployed somewhere to be exercised.
✓Deploy model in test environment -> Validate model -> Create a new AI Platform model version
the model can be validated after it is deployed to the test environment, and the release version is established before the model is deployed in production
✗Create a new AI Platform model version -> Validate model -> Deploy model in test environment
Deploying to test after the production version already exists inverts the promotion path.
2. You work for a manufacturing company that owns a high-value machine which has several machine settings and multiple sensors. A history of the machine’s hourly sensor readings and known failure event data are stored in BigQuery. You need to predict if the machine will fail within the next 3 days in order to schedule maintenance before the machine fails. Which data preparation and model training steps should you take?
✗Data preparation: Daily max value feature engineering with DataPrep; Model training: AutoML classification with BQML
A single daily maximum discards the trend information that predicts gradual machine degradation. A rolling average captures it.
✗Data preparation: Daily min value feature engineering with DataPrep; Model training: Logistic regression with BQML and AUTO_CLASS_WEIGHTS set to True
Same problem as the daily maximum — one extreme value per day loses the shape of the signal.
✓Data preparation: Rolling average feature engineering with DataPrep; Model training: Logistic regression with BQML and AUTO_CLASS_WEIGHTS set to True
D is correct because it uses the rolling average of the sensor data and balances the weights using the BQML auto class weight balance parameter.
✗Data preparation: Rolling average feature engineering with DataPrep; Model training: Logistic regression with BQML and AUTO_CLASS_WEIGHTS set to False
The feature engineering is right, but failure data is heavily imbalanced. Disabling automatic class weights lets the model predict 'no failure' every time and still look accurate.
3. You are an ML engineer at a media company. You need to build an ML model to analyze video content frame-by-frame, identify objects, and alert users if there is inappropriate content. Which Google Cloud products should you use to build this project?
✓Pub/Sub, Cloud Function, Video Intelligence API, Cloud Logging
C is correct as Video Intelligence API can find inappropriate components and other components satisfy the requirements of real-time processing and notification
✗Pub/Sub, Cloud Function, AutoML Video Intelligence, Cloud Logging
AutoML Video Intelligence is for custom labels you train yourself. If the pre-built API already recognises what you need, training a custom model is unnecessary work.
✗Pub/Sub, Cloud Function, Cloud Vision API
The Vision API analyses still images. Frame-by-frame analysis of video is what Video Intelligence is built for.
Cloud IoT is for device telemetry, not video ingestion, and Vision API is again the wrong service for video.
4. You need to create a model that predicts stock prices given a variety of factors. What type of problem are we modeling for?
✗Clustering
Clustering is unsupervised — it groups similar records without a target value. Here you have a known price to predict.
✗Linear
Describes the form a model might take, not the category of problem. Linear regression is one way to solve a regression problem.
✓Regression
Regression is the supervised learning task for modeling and predicting continuous, numeric variables. Examples include predicting real-estate prices, stock price movements, or student test scores.
✗Classification
Classification predicts a discrete label. A stock price is a continuous number, which makes it regression.
5. What are wide neural networks good for, compared deep neural networks?
✗Wide neural networks are better for generalization.
Generalisation is the strength of depth. Wide models tend to memorise the training examples they were given.
✗Wide networks are better at more complex problems.
Complex, hierarchical problems favour depth, because each layer composes features learned by the one below.
✗Wide networks are an older style neural network.
Width and depth are architectural choices, not eras. Wide-and-deep models deliberately combine both.
✓Wide neural networks are better for memorization.
Deep networks are better at generalization.
6. You are developing an application that will process thousands of images and scan for explicit content. You need to develop your learning model quickly, and are not familiar with working in Tensorflow. How can you complete this task as quickly as possible while saving on costs?
✗Hire a consultant to assist with training your machine learning model to process your images.
Adds cost and delay, and still ends with a custom model to build and maintain. Explicit-content detection is a solved problem already exposed as an API.
✗Develop your training model in Cloud ML Engine, then have your application call on the trained model.
Training a custom vision model requires labelled data, compute and expertise you have just said you lack. Far slower and more expensive than calling a pre-trained API. (Cloud ML Engine is now part of Vertex AI.)
✓Have your application call on the Cloud Vision API and pass your images to it via a Cloud Storage URI.
The Cloud Vision API is able to scan for explicit images, making this the quickest and most efficient answer.
✗Use Cloud Datalab to develop your learning model in an interactive format, to make the process easier.
A notebook environment makes development more pleasant but does not remove it. You would still be building and training a model from scratch. (Datalab is deprecated; Vertex AI Workbench replaced it.)
7. You need to give a team member the ability to use a training model for predictions, but not have the ability to create or delete models. What IAM role should you assign to achieve this task with the minimum necessary permissions?
✗Model Owner
Grants full control including deletion, which is exactly what the requirement rules out.
✓Model User
The Model User role allows a user to read models and use them for prediction, but nothing else.
✗Project Editor
A broad project-wide role covering nearly every resource. A serious violation of least privilege when all that is needed is the ability to request predictions.
✗Cloud ML Engine Developer
Permits creating and training models. More than the task requires, and the question asks for the minimum.
8. What is a difference between example (training) data and test data?
✗Test data have labels attached and examples do not.
Backwards, and both are labelled in supervised learning. Training labels teach the model; test labels are held back to score it.
✗Examples are used to validate training models, and test data is used to train the learning model.
The two roles are swapped. Training on your test set destroys the only unbiased estimate of performance you have.
✓Example (training) data uses its labels to train the machine learning model, while test data uses its labels to validate the model’s accuracy.
Examples have a correct label attached to them, which an ML model uses to learn how to associate features with the label. While test data also has labels, it is used to validate accuracy of the model, but not to train it.
✗Examples are used for feature engineering, and test data is used for hyperparameter tuning.
Both halves are off. Feature engineering is a preprocessing step applied across the data, and hyperparameter tuning should use a separate validation set — reusing the test set for tuning contaminates it.
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