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| Section | Objectives |
|---|---|
| Topic 1: Automating and orchestrating ML pipelines | - CI/CD for ML systems - Vertex AI Pipelines (Kubeflow Pipelines) - Triggering and scheduling pipelines |
| Topic 2: Serving and scaling models | - Online prediction (Vertex AI Prediction) - Batch prediction - Hardware accelerators (GPU/TPU) in serving - Model optimization (Quantization, Distillation) |
| Topic 3: Scaling prototypes into ML models | - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) - Hyperparameter tuning - Training at scale (Distributed training, TPUs) |
| Topic 4: Collaborating within and across teams to manage data and models | - Data management and governance - Collaboration between Data Scientists, Data Engineers, and ML Engineers - Version control and reproducibility (e.g., DVC, MLOps) |
| Topic 5: Monitoring ML solutions | - Logging and alerting (Cloud Monitoring) - Model retraining strategies - Performance monitoring and drift detection |
| Topic 6: Architecting low-code ML solutions | - AutoML capabilities and implementation - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) - Implementing BigQuery ML for basic models |
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NEW QUESTION # 283
You are developing a model to predict whether a failure will occur in a critical machine part. You have a dataset consisting of a multivariate time series and labels indicating whether the machine part failed You recently started experimenting with a few different preprocessing and modeling approaches in a Vertex Al Workbench notebook. You want to log data and track artifacts from each run. How should you set up your experiments?
Answer: D
Explanation:
The option A is the most suitable solution for logging data and tracking artifacts from each run of a model development experiment in a Vertex AI Workbench notebook. Vertex AI Workbench is a service that allows you to create and run interactive notebooks on Google Cloud. You can use Vertex AI Workbench to experiment with different preprocessing and modeling approaches for your time series prediction problem.
You can also use the Vertex AI TensorBoard instance and the Vertex AI SDK to create an experiment and associate the TensorBoard instance. TensorBoard is a tool that allows you to visualize and monitor the metrics and artifacts of your ML experiments. You can use the Vertex AI SDK to create an experiment object, which is a logical grouping of runs that share a common objective. You can also use the Vertex AI SDK to associate the experiment object with a TensorBoard instance, which is a managed service that hosts a TensorBoard web app. By using the Vertex AI TensorBoard instance and the Vertex AI SDK, you can easily set up and manage your experiments, and access the TensorBoard web app from the Vertex AI console. You can also use the log_time_series_metrics function and the log_metrics function to log data and track artifacts from each run.
The log_time_series_metrics function is a function that allows you to log the time series data, such as the multivariate time series and the labels, to the TensorBoard instance. The log_metrics function is a function that allows you to log the scalar metrics, such as the loss values, to the TensorBoard instance. By using these functions, you can record the data and artifacts from each run of your experiment, and compare them in the TensorBoard web app. You can also use the TensorBoard web app to visualize the data and artifacts, such as the time series plots, the scalar charts, the histograms, and the distributions. By using the Vertex AI TensorBoard instance, the Vertex AI SDK, and the log functions, you can log data and track artifacts from each run of your experiment in a Vertex AI Workbench notebook. References:
* Vertex AI Workbench documentation
* Vertex AI TensorBoard documentation
* Vertex AI SDK documentation
* log_time_series_metrics function documentation
* log_metrics function documentation
* [Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate]
NEW QUESTION # 284
You are training a custom language model for your company using a large dataset. You plan to use the Reduction Server strategy on Vertex Al. You need to configure the worker pools of the distributed training job. What should you do?
Answer: D
NEW QUESTION # 285
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.
References: 1: Confusion matrix 2: Mean average precision 3: Continuous Evaluation overview 4: Configure online prediction logging : [Create an evaluation job] : [View evaluation results] : [What-If Tool overview]
NEW QUESTION # 286
You are an ML engineer at a manufacturing company. You need to build a model that identifies defects in products based on images of the product taken at the end of the assembly line. You want your model to preprocess the images with lower computation to quickly extract features of defects in products. Which approach should you use to build the model?
Answer: C
Explanation:
* Option A is incorrect because reinforcement learning is not a suitable approach to build a model that identifies defects in products based on images of the product taken at the end of the assembly line. Reinforcement learning is a type of machine learning that learns from its own actions and rewards, rather than from labeled data or explicit feedback1. Reinforcement learning is more suitable for problems that involve sequential decision making, such as games, robotics, or control systems1.
However, defect detection is a problem that involves image classification or segmentation, which requires supervised learning, not reinforcement learning.
* Option B is incorrect because a recommender system is not a relevant approach to build a model that identifies defects in products based on images of the product taken at the end of the assembly line. A recommender system is a system that suggests items or actions to users based on their preferences, behavior, or context2. A recommender system is more suitable for problems that involve personalization, such as e-commerce, entertainment, or social media2. However, defect detection is a problem that involves image classification or segmentation, which requires supervised learning, not recommender system.
* Option C is incorrect because recurrent neural networks (RNN) are not the most efficient approach to build a model that identifies defects in products based on images of the product taken at the end of the assembly line. RNNs are a type of neural networks that can process sequential data, such as text, speech, or video, by maintaining a hidden state that captures the temporal dependencies3. RNNs are more suitable for problems that involve natural language processing, speech recognition, or video analysis3.
However, defect detection is a problem that involves image classification or segmentation, which does not require temporal dependencies, but rather spatial dependencies. Moreover, RNNs are computationally expensive and prone to vanishing or exploding gradients4.
* Option D is correct because convolutional neural networks (CNN) are the best approach to build a model that identifies defects in products based on images of the product taken at the end of the assembly line. CNNs are a type of neural networks that can process image data, by applying convolutional filters that extract local features and reduce the dimensionality of the data5. CNNs are more suitable for problems that involve image classification, object detection, or segmentation5. CNNs can preprocess the images with lower computation to quickly extract features of defects in products, by using techniques such as pooling, dropout, or batch normalization6.
References:
* Reinforcement learning
* Recommender system
* Recurrent neural network
* Vanishing and exploding gradients
* Convolutional neural network
* CNN techniques
* [Defect detection]
* [Image classification]
* [Image segmentation]
NEW QUESTION # 287
You are responsible for managing and monitoring a Vertex AI model that is deployed in production. You want to automatically retrain the model when its performance deteriorates. What should you do?
Answer: C
Explanation:
Vertex AI Model Monitoring automatically tracks model performance on production data and can be configured to trigger retraining workflows when key metrics fall below defined thresholds, enabling proactive, automated model maintenance.
NEW QUESTION # 288
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