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| Section | Objectives |
|---|---|
| Serving and scaling models | - Online prediction (Vertex AI Prediction) - Hardware accelerators (GPU/TPU) in serving - Model optimization (Quantization, Distillation) - Batch prediction |
| Scaling prototypes into ML models | - Training at scale (Distributed training, TPUs) - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) - Hyperparameter tuning |
| Collaborating within and across teams to manage data and models | - Version control and reproducibility (e.g., DVC, MLOps) - Data management and governance - Collaboration between Data Scientists, Data Engineers, and ML Engineers |
| Architecting low-code ML solutions | - AutoML capabilities and implementation - Implementing BigQuery ML for basic models - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) |
| Automating and orchestrating ML pipelines | - Triggering and scheduling pipelines - Vertex AI Pipelines (Kubeflow Pipelines) - CI/CD for ML systems |
| Monitoring ML solutions | - Performance monitoring and drift detection - Model retraining strategies - Logging and alerting (Cloud Monitoring) |
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NEW QUESTION # 222
You are an ML engineer at a global shoe store. You manage the ML models for the company's website. You are asked to build a model that will recommend new products to the user based on their purchase behavior and similarity with other users. What should you do?
Answer: B
Explanation:
Collaborative filtering is a technique that recommends items to users based on the ratings of other users. It works by finding users who have similar ratings to the current user and then recommending items that those users have liked.
https://cloud.google.com/architecture/recommendations-using-machine-learning-on-compute-engine#filtering_the_data
NEW QUESTION # 223
You work for a company that is developing a new video streaming platform. You have been asked to create a recommendation system that will suggest the next video for a user to watch. After a review by an AI Ethics team, you are approved to start development. Each video asset in your company's catalog has useful metadata (e.g., content type, release date, country), but you do not have any historical user event dat a. How should you build the recommendation system for the first version of the product?
Answer: B
NEW QUESTION # 224
You work as an analyst at a large banking firm. You are developing a robust, scalable ML pipeline to train several regression and classification models. Your primary focus for the pipeline is model interpretability. You want to productionize the pipeline as quickly as possible What should you do?
Answer: A
Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to "automate and orchestrate ML pipelines using Cloud Composer". Cloud Composer2 is a fully managed workflow orchestration service that uses Apache Airflow to create, schedule, monitor, and manage workflows.Cloud Composer allows you to build custom training pipelines for deep learning-based models and integrate them with other Google Cloud services. You can also use Cloud Composer to implement model interpretability techniques, such as feature attributions, explainable AI, or model debugging3. The other options are not relevant or optimal for this scenario. References:
* Professional ML Engineer Exam Guide
* Cloud Composer
* Model interpretability with Cloud Composer
* Google Professional Machine Learning Certification Exam 2023
* Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
NEW QUESTION # 225
You are an ML engineer at a bank. You have developed a binary classification model using AutoML Tables to predict whether a customer will make loan payments on time. The output is used to approve or reject loan requests. One customer's loan request has been rejected by your model, and the bank's risks department is asking you to provide the reasons that contributed to the model's decision. What should you do?
Answer: D
Explanation:
* Option A is correct because using local feature importance from the predictions is the best way to provide the reasons that contributed to the model's decision for a specific customer's loan request. Local feature importance is a measure of how much each feature affects the prediction for a given instance, relative to the average prediction for the dataset1. AutoML Tables provides local feature importance values for each prediction, which can be accessed using the Vertex AI SDK for Python or the Cloud Console2. By using local feature importance, you can explain why the model rejected the loan request based on the customer's data.
* Option B is incorrect because using the correlation with target values in the data summary page is not a good way to provide the reasons that contributed to the model's decision for a specific customer's loan request. The correlation with target values is a measure of how much each feature is linearly related to the target variable for the entire dataset, not for a single instance3. The data summary page in AutoML Tables shows the correlation with target values for each feature, as well as other statistics such as mean, standard deviation, and histogram4. However, these statistics are not useful for explaining the model's decision for a specific customer, as they do not account for the interactions between features or the non-linearity of the model.
* Option C is incorrect because using the feature importance percentages in the model evaluation page is not a good way to provide the reasons that contributed to the model's decision for a specific customer's
* loan request. The feature importance percentages are a measure of how much each feature affects the overall accuracy of the model for the entire dataset, not for a single instance5. The model evaluation page in AutoML Tables shows the feature importance percentages for each feature, as well as other metrics such as precision, recall, and confusion matrix. However, these metrics are not useful for explaining the model's decision for a specific customer, as they do not reflect the individual contribution of each feature for a given prediction.
* Option D is incorrect because varying features independently to identify the threshold per feature that changes the classification is not a feasible way to provide the reasons that contributed to the model's decision for a specific customer's loan request. This method involves changing the value of one feature at a time, while keeping the other features constant, and observing how the prediction changes.
However, this method is not practical, as it requires making multiple prediction requests, and may not capture the interactions between features or the non-linearity of the model.
References:
* Local feature importance
* Getting local feature importance values
* Correlation with target values
* Data summary page
* Feature importance percentages
* [Model evaluation page]
* [Varying features independently]
NEW QUESTION # 226
You have deployed a scikit-learn model to a Vertex Al endpoint using a custom model server. You enabled auto scaling; however, the deployed model fails to scale beyond one replica, which led to dropped requests. You notice that CPU utilization remains low even during periods of high load. What should you do?
Answer: D
Explanation:
Auto scaling is a feature that allows you to automatically adjust the number of prediction nodes based on the traffic and load of your deployed model1. However, auto scaling depends on the CPU utilization of your prediction nodes, which is the percentage of CPU resources used by your model server1. If your CPU utilization is low, even during periods of high load, it means that your model server is not fully utilizing the available CPU resources, and thus auto scaling will not trigger more replicas2.
One possible reason for low CPU utilization is that your model server is using a single worker process to handle prediction requests3. A worker process is a subprocess that runs your model code and handles prediction requests3. If you have only one worker process, it can only handle one request at a time, which can lead to dropped requests when the traffic is high3. To increase the CPU utilization and the throughput of your model server, you can increase the number of worker processes, which will allow your model server to handle multiple requests in parallel3.
To increase the number of workers in your model server, you need to modify your custom model server code and use the --workers flag to specify the number of worker processes you want to use3. For example, if you are using a Gunicorn server, you can use the following command to start your model server with four worker processes:
gunicorn --bind :$PORT --workers 4 --threads 1 --timeout 60 main:app
By increasing the number of workers in your model server, you can increase the CPU utilization of your prediction nodes, and thus enable auto scaling to scale beyond one replica.
The other options are not suitable for your scenario, because they either do not address the root cause of low CPU utilization, such as attaching a GPU or scheduling scaling, or they do not enable auto scaling, such as increasing the minReplicaCount, which is a fixed number of nodes that will always run regardless of the traffic1.
Reference:
Scaling prediction nodes | Vertex AI | Google Cloud
Troubleshooting | Vertex AI | Google Cloud
Using a custom prediction routine with online prediction | Vertex AI | Google Cloud
NEW QUESTION # 227
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