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| Section | Weight | Objectives |
|---|---|---|
| Train and deploy models | 20% | - Deploy models for online, batch, and streaming prediction - Configure training jobs and environments - Use Vertex AI deployment features and infrastructure - Implement generative AI deployment patterns |
| Scale prototypes into AI models | 18% | - Optimize model performance and generalization - Work with foundation models and generative AI techniques - Design and run experiments - Select appropriate model architectures and frameworks |
| Collaborate to manage data and models | 16% | - Address data privacy, compliance, and governance - Manage datasets and features in Vertex AI - Organize and prepare enterprise data
|
| Architect low-code AI solutions | 12% | - Apply responsible AI principles to low-code designs - Identify use cases for low-code/no-code AI tools - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder |
| Automate and orchestrate ML pipelines | 18% | - Automate retraining and model updates - Use Vertex AI Pipelines, TFX, and other orchestration tools - Design end-to-end ML workflows - Implement CI/CD for ML systems |
| Monitor and optimize AI solutions | 16% | - Optimize cost, latency, and resource usage - Monitor data quality and pipeline health - Monitor model performance, fairness, and drift - Troubleshoot and maintain production systems |
>> Professional-Machine-Learning-Engineer Originale Fragen <<
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395. Frage
You work at a marketing research firm. Your team's current project involves analyzing a large volume of customer surveys. Your team decides to use LLMs in Model Garden and track model training artifacts. You need to determine how to effectively share notebooks within your team and how to orchestrate the ML workflow. You want to use Google-managed services as much as possible. What should you do?
Antwort: D
Begründung:
Agent Platform Colab Enterprise provides a Google-managed, collaborative notebook environment that simplifies sharing work across the team. Agent Platform Pipelines is the managed orchestration service for repeatable ML workflows and integrates with training jobs, Model Garden resources, metadata, and model artifacts.
396. Frage
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
Antwort: A,C
Begründung:
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]
397. Frage
Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which platform components should you choose for this system?
Antwort: C
Begründung:
Vertex AI Pipelines and AI Platform Prediction are the platform components that best suit the requirements of the data science team. Vertex AI Pipelines is a service that allows you to orchestrate and automate your machine learning workflows using pipelines. Pipelines are portable and scalable ML workflows that are based on containers. You can use Vertex AI Pipelines to schedule model retraining, use custom containers, and integrate with other Google Cloud services. AI Platform Prediction is a service that allows you to host your trained models and serve online predictions. You can use AI Platform Prediction to deploy models trained on Vertex AI or elsewhere, and benefit from features such as autoscaling, monitoring, logging, and explainability. Reference:
Vertex AI Pipelines
AI Platform Prediction
398. Frage
You developed an ML model with Al Platform, and you want to move it to production. You serve a few thousand queries per second and are experiencing latency issues. Incoming requests are served by a load balancer that distributes them across multiple Kubeflow CPU-only pods running on Google Kubernetes Engine (GKE). Your goal is to improve the serving latency without changing the underlying infrastructure. What should you do?
Antwort: C
Begründung:
https://www.tensorflow.org/tfx/serving/performance
399. Frage
You are building a TensorFlow text-to-image generative model by using a dataset that contains billions of images with their respective captions. You want to create a low maintenance, automated workflow that reads the data from a Cloud Storage bucket collects statistics, splits the dataset into training/validation/test datasets performs data transformations, trains the model using the training/validation datasets. and validates the model by using the test dataset. What should you do?
Antwort: C
400. Frage
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