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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Monitor and optimize AI solutions | 16% | - Optimize cost, latency, and resource usage - Monitor data quality and pipeline health - Troubleshoot and maintain production systems - Monitor model performance, fairness, and drift |
| Topic 2: Automate and orchestrate ML pipelines | 18% | - Automate retraining and model updates - Design end-to-end ML workflows - Implement CI/CD for ML systems - Use Vertex AI Pipelines, TFX, and other orchestration tools |
| Topic 3: Architect low-code AI solutions | 12% | - Identify use cases for low-code/no-code AI tools - Apply responsible AI principles to low-code designs - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder |
| Topic 4: Collaborate to manage data and models | 16% | - Address data privacy, compliance, and governance - Organize and prepare enterprise data
|
| Topic 5: Train and deploy models | 20% | - Configure training jobs and environments - Use Vertex AI deployment features and infrastructure - Deploy models for online, batch, and streaming prediction - Implement generative AI deployment patterns |
| Topic 6: Scale prototypes into AI models | 18% | - Optimize model performance and generalization - Work with foundation models and generative AI techniques - Select appropriate model architectures and frameworks - Design and run experiments |
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NEW QUESTION # 393
You are developing a model to identify traffic signs in images extracted from videos taken from the dashboard of a vehicle. You have a dataset of 100 000 images that were cropped to show one out of ten different traffic signs. The images have been labeled accordingly for model training and are stored in a Cloud Storage bucket You need to be able to tune the model during each training run. How should you train the model?
Answer: B
Explanation:
Image classification is a task where the model assigns a label to an image based on its content, such as "stop sign" or "speed limit"1. Object detection is a task where the model locates and identifies multiple objects in an image, and draws bounding boxes around them2. Since your dataset consists of images that were cropped to show one out of ten different traffic signs, you are dealing with an image classification problem, not an object detection problem. Therefore, you need to train a model for image classification, not object detection.
Vertex AI AutoML is a service that allows you to train and deploy high-quality ML models with minimal effort and machine learning expertise3. You can use Vertex AI AutoML to train a model for image classification by uploading your images and labels to a Vertex AI dataset, and then launching an AutoML training job4. However, Vertex AI AutoML does not allow you to tune the model during each training run, as it automatically selects the best model architecture and hyperparameters for your data4.
Vertex AI custom training is a service that allows you to train and deploy your own custom ML models using your own code and frameworks5. You can use Vertex AI custom training to train a model for image classification by writing your own model training code, such as using TensorFlow or PyTorch, and then creating and running a custom training job. Vertex AI custom training allows you to tune the model during each training run, as you can specify the model architecture and hyperparameters in your code, and use Vertex AI Hyperparameter Tuning to optimize them .
Therefore, the best option for your scenario is to develop the model training code for image classification and train a model by using Vertex AI custom training.
Reference:
Image classification | TensorFlow Core
Object detection | TensorFlow Core
Introduction to Vertex AI AutoML | Google Cloud
AutoML Vision | Google Cloud
Introduction to Vertex AI custom training | Google Cloud
[Custom training with TensorFlow | Vertex AI | Google Cloud]
[Hyperparameter tuning overview | Vertex AI | Google Cloud]
NEW QUESTION # 394
You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly to users in an app in real time. Because different seasons and population increases impact the data relevance, you will retrain the model every month. You want to follow Google-recommended best practices. How should you configure the end-to-end architecture of the predictive model?
Answer: B
Explanation:
The end-to-end architecture of the predictive model for estimating delay times for multiple transportation routes should be configured using Kubeflow Pipelines. Kubeflow Pipelines is a platform for building and deploying scalable, portable, and reusable machine learning pipelines on Kubernetes. Kubeflow Pipelines allows you to orchestrate your multi-step workflow from data preparation, model training, model evaluation, model deployment, and model serving. Kubeflow Pipelines also provides a user interface for managing and tracking your pipeline runs, experiments, an d artifacts 1 Using Kubeflow Pipelines has several advantages for this use case:
* Full automation: You can define your pipeline as a Python script that specifies the steps and dependencies of your workflow, and use the Kubeflow Pipelines SDK to compile and upload your pipeline to the Kubeflow Pipelines service. You can also use the Kubeflow Pipelines UI to create, run, and monitor your pipeline 2
* Scalability: You can leverage the power of Kubernetes to scale your pipeline components horizontally and vertically, and use distributed training frameworks such as TensorFlow or PyTorch to train your model on multiple nodes or GPUs 3
* Portability: You can package your pipeline components as Docker containers that can run on any Kubernetes cluster, and use the Kubeflow Pipelines SDK to export and import your pipeline packages across different environments 4
* Reusability: You can reuse your pipeline components across different pipelines, and share your components with other users through the Kubeflow Pipelines Component Store. You can also use pre- built components from the Kubeflow Pipelines library or other sources 5
* Schedulability: You can use the Kubeflow Pipelines UI or the Kubeflow Pipelines SDK to schedule recurring pipeline runs based on cron expressions or intervals. For example, you can schedule your pipeline to run every month to retrain your model on the latest data.
The other options are not as suitable for this use case. Using a model trained and deployed on BigQuery ML is not recommended, as BigQuery ML is mainly designed for simple and quick machine learning tasks on large-scale data, and does not support complex models or custom code. Writing a Cloud Functions script that launches a training and deploying job on AI Platform is not ideal, as Cloud Functions has limitations on the memory, CPU, and execution time, and does not provide a user interface for managing and tracking your pipeline. Using Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model is not optimal, as Dataflow is mainly designed for data processing and streaming analytics, and does not support model serving or monitoring.
References: 1 : Kubeflow Pipelines overview 2 : Build a pipeline 3 : Scale your machine learning training and prediction workloads 4 : Export and import pipelines 5 : Build components and pipelines :
[Schedule recurring pipeline runs] : [BigQuery ML overview] : [Cloud Functions documentation] : [Dataflow documentation]
NEW QUESTION # 395
A Machine Learning Specialist is preparing data for training on Amazon SageMaker. The Specialist is using one of the SageMaker built-in algorithms for the training. The dataset is stored in .CSV format and is transformed into a numpy.array, which appears to be negatively affecting the speed of the training.
What should the Specialist do to optimize the data for training on SageMaker?
Answer: A
NEW QUESTION # 396
You have been asked to develop an input pipeline for an ML training model that processes images from disparate sources at a low latency. You discover that your input data does not fit in memory. How should you create a dataset following Google-recommended best practices?
Answer: A
NEW QUESTION # 397
While performing exploratory data analysis on a dataset, you find that an important categorical feature has 5% null values. You want to minimize the bias that could result from the missing values. How should you handle the missing values?
Answer: C
NEW QUESTION # 398
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