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| Section | Weight | Objectives |
|---|---|---|
| Architect low-code AI solutions | 12% | - Identify use cases for low-code/no-code AI tools - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder - Apply responsible AI principles to low-code designs |
| Collaborate to manage data and models | 16% | - Organize and prepare enterprise data
- Address data privacy, compliance, and governance |
| Monitor and optimize AI solutions | 16% | - Monitor data quality and pipeline health - Monitor model performance, fairness, and drift - Optimize cost, latency, and resource usage - Troubleshoot and maintain production systems |
| Train and deploy models | 20% | - Deploy models for online, batch, and streaming prediction - Configure training jobs and environments - Implement generative AI deployment patterns - Use Vertex AI deployment features and infrastructure |
| Automate and orchestrate ML pipelines | 18% | - Automate retraining and model updates - Use Vertex AI Pipelines, TFX, and other orchestration tools - Implement CI/CD for ML systems - Design end-to-end ML workflows |
| Scale prototypes into AI models | 18% | - Select appropriate model architectures and frameworks - Work with foundation models and generative AI techniques - Optimize model performance and generalization - Design and run experiments |
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NEW QUESTION # 138
You work for a hospital. You received approval to collect the necessary patient data, and you trained a Vertex AI tabular AutoML model that calculates patients' risk score for hospital admission. You deployed the model. However, you're concerned that patient demographics might change over time and alter the feature interactions and impact prediction accuracy. You want to be alerted if feature interactions change, and you want to understand the importance of the features for the predictions. You want your alerting approach to minimize cost. What should you do?
Answer: B
NEW QUESTION # 139
You are training a Resnet model on Al Platform using TPUs to visually categorize types of defects in automobile engines. You capture the training profile using the Cloud TPU profiler plugin and observe that it is highly input-bound. You want to reduce the bottleneck and speed up your model training process. Which modifications should you make to the tf .data dataset?
Choose 2 answers
Answer: A,E
Explanation:
The tf.data dataset is a TensorFlow API that provides a way to create and manipulate data pipelines for machine learning. The tf.data dataset allows you to apply various transformations to the data, such as reading, shuffling, batching, prefetching, and interleaving. These transformations can affect the performance and efficiency of the model training process1 One of the common performance issues in model training is input-bound, which means that the model is waiting for the input data to be ready and is not fully utilizing the computational resources. Input-bound can be caused by slow data loading, insufficient parallelism, or large data size. Input-bound can be detected by using the Cloud TPU profiler plugin, which is a tool that helps you analyze the performance of your model on Cloud TPUs. The Cloud TPU profiler plugin can show you the percentage of time that the TPU cores are idle, which indicates input-bound2 To reduce the input-bound bottleneck and speed up the model training process, you can make some modifications to the tf.data dataset. Two of the modifications that can help are:
* Use the interleave option for reading data. The interleave option allows you to read data from multiple files in parallel and interleave their records. This can improve the data loading speed and reduce the idle time of the TPU cores. The interleave option can be applied by using the tf.data.Dataset.interleave method, which takes a function that returns a dataset for each input element, and a number of parallel calls3
* Set the prefetch option equal to the training batch size. The prefetch option allows you to prefetch the next batch of data while the current batch is being processed by the model. This can reduce the latency between batches and improve the throughput of the model training.The prefetch option can be applied by using the tf.data.Dataset.prefetch method, which takes a buffer size argument. The buffer size should be equal to the training batch size, which is the number of examples per batch4 The other options are not effective or counterproductive. Reducing the value of the repeat parameter will reduce the number of epochs, which is the number of times the model sees the entire dataset. This can affect the model's accuracy and convergence. Increasing the buffer size for the shuffle option will increase the randomness of the data, but also increase the memory usage and the data loading time. Decreasing the batch size argument in your transformation will reduce the number of examples per batch, which can affect the model's stability and performance.
References: 1: tf.data: Build TensorFlow input pipelines 2: Cloud TPU Tools in TensorBoard 3: tf.data.Dataset.interleave 4: tf.data.Dataset.prefetch : [Better performance with the tf.data API]
NEW QUESTION # 140
You are developing a model to help your company create more targeted online advertising campaigns. You need to create a dataset that you will use to train the model. You want to avoid creating or reinforcing unfair bias in the model. What should you do? (Choose two.)
Answer: A
NEW QUESTION # 141
You work for a toy manufacturer that has been experiencing a large increase in demand. You need to build an ML model to reduce the amount of time spent by quality control inspectors checking for product defects. Faster defect detection is a priority. The factory does not have reliable Wi-Fi. Your company wants to implement the new ML model as soon as possible. Which model should you use?
Answer: B
Explanation:
AutoML Vision Edge is a service that allows you to create custom image classification and object detection models that can run on edge devices, such as mobile phones, tablets, or IoT devices1. AutoML Vision Edge offers four types of models that vary in size, accuracy, and latency: mobile-versatile-1, mobile-low-latency-1, mobile-high-accuracy-1, and mobile-core-ml-low-latency-12. Each model has its own trade-offs and use cases, depending on the device specifications and the application requirements.
For the use case of building an ML model to reduce the amount of time spent by quality control inspectors checking for product defects, the best model to use is the AutoML Vision Edge mobile-low-latency-1 model. This model is optimized for fast inference on mobile devices, with a latency of less than 50 milliseconds on a Pixel 1 phone2. Faster defect detection is a priority for the toy manufacturer, and the factory does not have reliable Wi-Fi, so a low-latency model that can run on the device without internet connection is ideal. The mobile-low-latency-1 model also has a small size of less than 4 MB, which makes it easy to deploy and update2. The mobile-low-latency-1 model has a slightly lower accuracy than the mobile-high-accuracy-1 model, but it is still suitable for most image classification tasks2. Therefore, the AutoML Vision Edge mobile-low-latency-1 model is the best option for this use case.
Reference:
AutoML Vision Edge documentation
AutoML Vision Edge model types
NEW QUESTION # 142
You recently trained an XGBoost model on tabular data You plan to expose the model for internal use as an HTTP microservice After deployment you expect a small number of incoming requests. You want to productionize the model with the least amount of effort and latency. What should you do?
Answer: D
Explanation:
XGBoost is a popular open-source library that provides a scalable and efficient implementation of gradient boosted trees. You can use XGBoost to train a classification or regression model on tabular data. You can also use Vertex AI to productionize the model and expose it for internal use as an HTTP microservice. Vertex AI is a service that allows you to create and train ML models using Google Cloud technologies. You can use a prebuilt XGBoost Vertex container to create a model and deploy it to Vertex AI Endpoints. A prebuilt Vertex container is a container image that contains the dependencies and libraries needed to run a specific ML framework, such as XGBoost. You can use a prebuilt Vertex container to simplify the model creation and deployment process, without having to build your own custom container. Vertex AI Endpoints is a service that allows you to serve your ML models online and scale them automatically. You can use Vertex AI Endpoints to deploy the model from the prebuilt Vertex container and expose it as an HTTP microservice. You can also configure the endpoint to handle a small number of incoming requests, and optimize the latency and cost of serving the model. By using a prebuilt XGBoost Vertex container and Vertex AI Endpoints, you can productionize the model with the least amount of effort and latency. Reference:
XGBoost documentation
Vertex AI documentation
Prebuilt Vertex container documentation
Vertex AI Endpoints documentation
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
NEW QUESTION # 143
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