Google Professional Machine Learning Engineer Exam Practice Questions (P. 2)

GPT-4o - Answer

Regarding the choice between using answer D and conducting a hyperparameter tuning job focused on learning rate and increasing neurons, it seems counter-intuitive in terms of minimizing overfitting. Increasing neurons indeed adds complexity, which can exacerbate an overfitting scenario if not properly managed. A more effective approach might indeed involve tuning L2 regularization and dropout, as these methods directly tackle model complexity to enhance generalizability. Opting for a hyperparameter tuning job to ensure that the right balance between model complexity and training dynamics is found generally gives you an advantage in combating overfitting.

GPT-4o - Answer

It seems that the notion of "Incorrect data split ratio during model training, evaluation, validation, and test" as the cause of declining model accuracy post-deployment doesn't completely hold up. The essence here lies in the evolving nature of market conditions, which necessitates recurring model updates to maintain accuracy. A model that isn't periodically recalibrated with fresh data will likely fall short as it continues to learn from the initial, now outdated dataset. This suggests that the recurring issue is indeed the lack of retraining, as updating the model to reflect new data trends is crucial in dynamic market environments.

GPT-4o - Answer

The correct response involves scanning the data for PII using Google Cloud's Data Loss Prevention API before allowing it into your operational data environment. This is efficiently executed by initially writing all streamed files to a single destination like BigQuery, and then performing systematic scans of this destination. An alternative perspective might consider a more continuous or real-time approach as indicated in some user discussions, but such setups could require substantially increased complexity and potential custom development. For standardized implementation on Google Cloud, periodic scanning as described remains a robust choice.

GPT-4o - Answer

When dealing with a dataset that has time signals spread across multiple columns, it's imperative to manually handle the data splitting for training, validation, and testing. This manual splitting ensures that your model accurately recognizes and utilizes the temporal distribution of the dataset rather than treating each entry with equal historical weight. By designating set periods for training, validation, and testing, the temporal relationships and dependencies within the data are better maintained and reflected in model performance. This approach is crucial for predictive tasks where time plays a significant role in the target variable.

GPT-4o - Answer

Absolutely, choosing option B is spot on. Cloud Build is designed to handle continuous integration and deployment, which is exactly what you need for automating your unit tests each time there’s a new push in Cloud Source Repositories. It’s straightforward and integrates smoothly with the workflow, unlike other options that add unnecessary complexity. This setup simplifies your CI/CD pipeline by leveraging Cloud Build’s native capabilities, ensuring that your unit tests run automatically and reliably.

GPT-4o - Answer

Adjusting the 'batch size' is an effective approach to balancing training time and model accuracy. By increasing the batch size, you can expedite the processing of data through parallelism on capable hardware, potentially reducing training time. However, it's crucial to monitor the model's performance as too large a batch size could adversely affect the accuracy, due to less noise in the gradient estimates. Contrary to some suggestions, changing the scale-tier primarily affects resource allocation rather than directly speeding up training or improving accuracy.