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| Section | Objectives |
|---|---|
| ML pipeline automation and orchestration | - Pipeline design
|
| Designing ML solutions | - ML architecture design
|
| ML model development | - Evaluation
|
| Deployment and operations | - Model deployment
|
| Data preparation and processing | - Feature engineering
|
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NEW QUESTION # 402
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: C
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 model 1 . However, auto scaling depends on the CPU utilization of your prediction nodes, which is the percentage of CPU resources used by your model server 1 . 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 au to scaling will not trigger more replicas 2 .
On e possible reason for low CPU utilization is that your model server is using a single worker process to handle prediction requests 3 . A worker process is a subprocess that runs your model code and handles prediction requests 3 . 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 high 3 . 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 parallel 3 .
To increase the number of workers i n 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 use 3 . 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 traffi c 1 .
:
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 # 403
You need to build an ML model for a social media application to predict whether a user's submitted profile photo meets the requirements. The application will inform the user if the picture meets the requirements. How should you build a model to ensure that the application does not falsely accept a non-compliant picture?
Answer: B
Explanation:
Recall is the ratio of true positives to the sum of true positives and false negatives. It measures how well the model can identify all the relevant cases. In this scenario, the relevant cases are the pictures that do not meet the profile photo requirements. Therefore, minimizing false negatives means minimizing the cases where the model incorrectly predicts that a non-compliant picture meets the requirements. By using AutoML to optimize the model's recall, the model will be more likely to reject a non-compliant picture and inform the user accordingly. Reference:
[AutoML Vision] is a service that allows you to train custom ML models for image classification and object detection tasks. You can use AutoML to optimize your model for different metrics, such as recall, precision, or F1 score.
[Recall] is one of the evaluation metrics for ML models. It is defined as TP / (TP + FN), where TP is the number of true positives and FN is the number of false negatives. Recall measures how well the model can identify all the relevant cases. A high recall means that the model has a low rate of false negatives.
NEW QUESTION # 404
You have recently created a proof-of-concept (POC) deep learning model. You are satisfied with the overall architecture, but you need to determine the value for a couple of hyperparameters.
You want to perform hyperparameter tuning on Vertex AI to determine both the appropriate embedding dimension for a categorical feature used by your model and the optimal learning rate.
You configure the following settings:
- For the embedding dimension, you set the type to INTEGER with a
minValue of 16 and maxValue of 64.
- For the learning rate, you set the type to DOUBLE with a minValue of
10e-05 and maxValue of 10e-02.
You are using the default Bayesian optimization tuning algorithm, and you want to maximize model accuracy. Training time is not a concern. How should you set the hyperparameter scaling for each hyperparameter and the maxParallelTrials?
Answer: A
NEW QUESTION # 405
You need to build an ML model for a social media application to predict whether a user's submitted profile photo meets the requirements. The application will inform the user if the picture meets the requirements. How should you build a model to ensure that the application does not falsely accept a non-compliant picture?
Answer: D
Explanation:
"Use AutoML to optimize the model's F1 score in order to balance the accuracy of false positives and false negatives", is the best approach to build an ML model that can predict whether a user's submitted profile photo meets the requirements while ensuring that the application does not falsely accept a non-compliant picture.
NEW QUESTION # 406
You are developing an ML model that predicts the cost of used automobiles based on data such as location, condition model type color, and engine-'battery efficiency. The data is updated every night Car dealerships will use the model to determine appropriate car prices. You created a Vertex Al pipeline that reads the data splits the data into training/evaluation/test sets performs feature engineering trains the model by using the training dataset and validates the model by using the evaluation dataset. You need to configure a retraining workflow that minimizes cost What should you do?
Answer: C
Explanation:
Comparing the training and evaluation losses of the current run is a good way to check if the model is overfitting or underfitting. If the losses are similar, it means that the model is generalizing well and can be deployed to a Vertex AI endpoint. Vertex AI endpoint is a service that allows you to serve your ML models online and scale them automatically. By using a training/serving skew threshold model monitoring, you can detect if there is a significant difference between the data used for training and the data used for serving. This can indicate that the model is becoming stale or inaccurate over time. When the model monitoring threshold is triggered, it means that the model needs to be retrained with the latest data. By redeploying the pipeline, you can automate the retraining process and update the model with the new data. This way, you can minimize the cost of retraining and ensure that your model is always up-to-date and accurate. References:
* Vertex AI documentation
* Vertex AI endpoint documentation
* Model monitoring documentation
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
NEW QUESTION # 407
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