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| Section | Objectives |
|---|---|
| Topic 1: 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 |
| Topic 2: Scaling prototypes into ML models | - Training at scale (Distributed training, TPUs) - Hyperparameter tuning - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) |
| Topic 3: Monitoring ML solutions | - Model retraining strategies - Performance monitoring and drift detection - Logging and alerting (Cloud Monitoring) |
| Topic 4: Serving and scaling models | - Online prediction (Vertex AI Prediction) - Batch prediction - Hardware accelerators (GPU/TPU) in serving - Model optimization (Quantization, Distillation) |
| Topic 5: Automating and orchestrating ML pipelines | - Triggering and scheduling pipelines - CI/CD for ML systems - Vertex AI Pipelines (Kubeflow Pipelines) |
| Topic 6: Architecting low-code ML solutions | - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) - AutoML capabilities and implementation - Implementing BigQuery ML for basic models |
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NEW QUESTION # 78
You have deployed multiple versions of an image classification model on Al Platform. You want to monitor the performance of the model versions overtime. How should you perform this comparison?
Answer: C
Explanation:
The performance of an image classification model can be measured by various metrics, such as accuracy, precision, recall, F1-score, and mean average precision (mAP). These metrics can be calculated based on the confusion matrix, which compares the predicted labels and the true labels of the images1 One of the best ways to monitor the performance of multiple versions of an image classification model on AI Platform is to compare the mean average precision across the models using the Continuous Evaluation feature. Mean average precision is a metric that summarizes the precision and recall of a model across different confidence thresholds and classes. Mean average precision is especially useful for multi-class and multi-label image classification problems, where the model has to assign one or more labels to each image from a set of possible labels. Mean average precision can range from 0 to 1, where a higher value indicates a better performance2 Continuous Evaluation is a feature of AI Platform that allows you to automatically evaluate the performance of your deployed models using online prediction requests and responses. Continuous Evaluation can help you monitor the quality and consistency of your models over time, and detect any issues or anomalies that may affect the model performance. Continuous Evaluation can also provide various evaluation metrics and visualizations, such as accuracy, precision, recall, F1-score, ROC curve, and confusion matrix, for different types of models, such as classification, regression, and object detection3 To compare the mean average precision across the models using the Continuous Evaluation feature, you need to do the following steps:
Enable the online prediction logging for each model version that you want to evaluate. This will allow AI Platform to collect the prediction requests and responses from your models and store them in BigQuery4 Create an evaluation job for each model version that you want to evaluate. This will allow AI Platform to compare the predicted labels and the true labels of the images, and calculate the evaluation metrics, such as mean average precision. You need to specify the BigQuery table that contains the prediction logs, the data schema, the label column, and the evaluation interval.
View the evaluation results for each model version on the AI Platform Models page in the Google Cloud console. You can see the mean average precision and other metrics for each model version over time, and compare them using charts and tables. You can also filter the results by different classes and confidence thresholds.
The other options are not as effective or feasible. Comparing the loss performance for each model on a held-out dataset or on the validation data is not a good idea, as the loss function may not reflect the actual performance of the model on the online prediction data, and may vary depending on the choice of the loss function and the optimization algorithm. Comparing the receiver operating characteristic (ROC) curve for each model using the What-If Tool is not possible, as the What-If Tool does not support image data or multi-class classification problems.
NEW QUESTION # 79
You are building a custom image classification model and plan to use Vertex Al Pipelines to implement the end-to-end training. Your dataset consists of images that need to be preprocessed before they can be used to train the model. The preprocessing steps include resizing the images, converting them to grayscale, and extracting features. You have already implemented some Python functions for the preprocessing tasks. Which components should you use in your pipeline ' ?
Answer: B
NEW QUESTION # 80
You developed a Vertex Al pipeline that trains a classification model on data stored in a large BigQuery table.
The pipeline has four steps, where each step is created by a Python function that uses the KubeFlow v2 API The components have the following names:
You launch your Vertex Al pipeline as the following:
You perform many model iterations by adjusting the code and parameters of the training step.
You observe high costs associated with the development, particularly the data export and preprocessing steps.
You need to reduce model development costs.
What should you do?
Answer: D
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". Vertex AI Pipelines2 is a service that allows you to orchestrate your ML workflows using Kubeflow Pipelines SDK v2 or TensorFlow Extended. Vertex AI Pipelines supports execution caching, which means that if you run a pipeline and it reaches a component that has already been run with the same inputs and parameters, the component does not run again. Instead, the component uses the output from the previous run. This can save you time and resources when you are iterating on your pipeline.
Therefore, option A is the best way to reduce model development costs, as it enables execution caching for the data export and preprocessing steps, which are likely to be the same for each model iteration. The other options are not relevant or optimal for this scenario. References:
* Professional ML Engineer Exam Guide
* Vertex AI Pipelines
* Google Professional Machine Learning Certification Exam 2023
* Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
NEW QUESTION # 81
Your team has a model deployed to a Vertex Al endpoint You have created a Vertex Al pipeline that automates the model training process and is triggered by a Cloud Function. You need to prioritize keeping the model up-to-date, but also minimize retraining costs. How should you configure retraining'?
Answer: B
Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to "configure and optimize model monitoring jobs". Vertex AI Model Monitoring documentation states that "model monitoring helps you detect when your model's performance degrades over time due to changes in the data that your model receives or returns" and that "you can configure model monitoring to send notifications to Pub/Sub when it detects anomalies or drift in your model's predictions"2. Therefore, enabling model monitoring on the Vertex AI endpoint and configuring Pub/Sub to call the Cloud Function when feature drift is detected would help you keep the model up-to-date and minimize retraining costs. The other options are not relevant or optimal for this scenario. Reference:
Professional ML Engineer Exam Guide
Vertex AI Model Monitoring
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
NEW QUESTION # 82
You recently created a new Google Cloud Project After testing that you can submit a Vertex Al Pipeline job from the Cloud Shell, you want to use a Vertex Al Workbench user-managed notebook instance to run your code from that instance You created the instance and ran the code but this time the job fails with an insufficient permissions error. What should you do?
Answer: D
NEW QUESTION # 83
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