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| Section | Weight | Objectives |
|---|---|---|
| Automate and orchestrate ML pipelines | 18% | - Implement CI/CD for ML systems - Automate retraining and model updates - Design end-to-end ML workflows - Use Vertex AI Pipelines, TFX, and other orchestration tools |
| Train and deploy models | 20% | - Configure training jobs and environments - Deploy models for online, batch, and streaming prediction - Implement generative AI deployment patterns - Use Vertex AI deployment features and infrastructure |
| Collaborate to manage data and models | 16% | - Organize and prepare enterprise data
- Manage datasets and features in Vertex AI |
| Monitor and optimize AI solutions | 16% | - Optimize cost, latency, and resource usage - Monitor model performance, fairness, and drift - Monitor data quality and pipeline health - Troubleshoot and maintain production systems |
| Architect low-code AI solutions | 12% | - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder - Identify use cases for low-code/no-code AI tools - Apply responsible AI principles to low-code designs |
| Scale prototypes into AI models | 18% | - Design and run experiments - Select appropriate model architectures and frameworks - Optimize model performance and generalization - Work with foundation models and generative AI techniques |
>> Professional-Machine-Learning-Engineerテスト問題集 <<
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質問 # 16
You work for a biotech startup that is experimenting with deep learning ML models based on properties of biological organisms. Your team frequently works on early-stage experiments with new architectures of ML models, and writes custom TensorFlow ops in C++. You train your models on large datasets and large batch sizes. Your typical batch size has 1024 examples, and each example is about 1 MB in size. The average size of a network with all weights and embeddings is 20 GB. What hardware should you choose for your models?
正解：B
解説：
The best hardware to choose for your models is a cluster with 2 a2-megagpu-16g machines, each with 16 NVIDIA Tesla A100 GPUs (640 GB GPU memory in total), 96 vCPUs, and 1.4 TB RAM. This hardware configuration can provide you with enough compute power, memory, and bandwidth to handle your large and complex deep learning models, as well as your custom TensorFlow ops in C++. The NVIDIA Tesla A100 GPUs are the latest and most advanced GPUs from NVIDIA, which offer high performance, scalability, and efficiency for various ML workloads. They also support multi-instance GPU (MIG) technology, which allows you to partition each GPU into up to seven smaller instances, each with its own memory, cache, and compute cores. This can enable you to run multiple experiments in parallel, or to optimize the resource utilization and cost efficiency of your models. The a2-megagpu-16g machines are part of the Google Cloud Accelerator- Optimized VM (A2) family, which are designed to provide the best performance and flexibility for GPU- intensive applications. They also offer high-speed NVLink interconnects between the GPUs, which can improve the data transfer and communication between the GPUs. Moreover, the a2-megagpu-16g machines have 96 vCPUs and 1.4 TB RAM, which can support the CPU and memory requirements of your models, as well as the data preprocessing and postprocessing tasks.
The other options are not optimal for the following reasons:
* A. A cluster with 2 n1-highcpu-64 machines, each with 8 NVIDIA Tesla V100 GPUs (128 GB GPU memory in total), and a n1-highcpu-64 machine with 64 vCPUs and 58 GB RAM is not a good option, as it has less GPU memory, compute power, and bandwidth than the a2-megagpu-16g machines. The NVIDIA Tesla V100 GPUs are the previous generation of GPUs from NVIDIA, which have lower performance, scalability, and efficiency than the NVIDIA Tesla A100 GPUs. They also do not support the MIG technology, which can limit the flexibility and optimization of your models. Moreover, the n1- highcpu-64 machines are part of the Google Cloud N1 VM family, which are general-purpose VMs that do not offer the best performance and features for GPU-intensive applications. They also have lower vCPUs and RAM than the a2-megagpu-16g machines, which can affect the CPU and memory requirements of your models, as well as the data preprocessing and postprocessing tasks.
* C. A cluster with an n1-highcpu-64 machine with a v2-8 TPU and 64 GB RAM is not a good option, as it has less GPU memory, compute power, and bandwidth than the a2-megagpu-16g machines. The v2-8 TPU is a cloud tensor processing unit (TPU) device, which is a custom ASIC chip designed by Google to accelerate ML workloads. However, the v2-8 TPU is the second generation of TPUs, which have lower performance, scalability, and efficiency than the latest v3-8 TPUs. They also have less memory and bandwidth than the NVIDIA Tesla A100 GPUs, which can limit the size and complexity of your models, as well as the data transfer and communication between the devices. Moreover, the n1-highcpu-
64 machine has lower vCPUs and RAM than the a2-megagpu-16g machines, which can affect the CPU and memory requirements of your models, as well as the data preprocessing and postprocessing tasks.
* D. A cluster with 4 n1-highcpu-96 machines, each with 96 vCPUs and 86 GB RAM is not a good option, as it does not have any GPUs, which are essential for accelerating deep learning models. The n1- highcpu-96 machines are part of the Google Cloud N1 VM family, which are general-purpose VMs that do not offer the best performance and features for GPU-intensive applications. They also have lower RAM than the a2-megagpu-16g machines, which can affect the memory requirements of your models, as well as the data preprocessing and postprocessing tasks.
:
Professional ML Engineer Exam Guide
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate Google Cloud launches machine learning engineer certification NVIDIA Tesla A100 GPU Google Cloud Accelerator-Optimized VM (A2) family Google Cloud N1 VM family Cloud TPU
質問 # 17
You deployed an ML model into production a year ago. Every month, you collect all raw requests that were sent to your model prediction service during the previous month. You send a subset of these requests to a human labeling service to evaluate your model's performance. After a year, you notice that your model's performance sometimes degrades significantly after a month, while other times it takes several months to notice any decrease in performance. The labeling service is costly, but you also need to avoid large performance degradations. You want to determine how often you should retrain your model to maintain a high level of performance while minimizing cost. What should you do?
正解：A
質問 # 18
You have been given a dataset with sales predictions based on your company's marketing activities. The data is structured and stored in BigQuery, and has been carefully managed by a team of data analysts. You need to prepare a report providing insights into the predictive capabilities of the data. You were asked to run several ML models with different levels of sophistication, including simple models and multilayered neural networks.
You only have a few hours to gather the results of your experiments. Which Google Cloud tools should you use to complete this task in the most efficient and self-serviced way?
正解：A
解説：
* Option A is correct because using BigQuery ML to run several regression models, and analyze their performance is the most efficient and self-serviced way to complete the task. BigQuery ML is a service that allows you to create and use ML models within BigQuery using SQL queries1. You can use BigQuery ML to run different types of regression models, such as linear regression, logistic regression, or DNN regression2. You can also use BigQuery ML to analyze the performance of your models, such as the mean squared error, the accuracy, or the ROC curve3. BigQuery ML is fast, scalable, and easy to use, as it does not require any data movement, coding, or additional tools4.
* Option B is incorrect because reading the data from BigQuery using Dataproc, and running several models using SparkML is not the most efficient and self-serviced way to complete the task. Dataproc is a service that allows you to create and manage clusters of virtual machines that run Apache Spark and other open-source tools5. SparkML is a library that provides ML algorithms and utilities for Spark.
However, this option requires more effort and resources than option A, as it involves moving the data from BigQuery to Dataproc, creating and configuring the clusters, writing and running the SparkML code, and analyzing the results.
* Option C is incorrect because using Vertex AI Workbench user-managed notebooks with scikit-learn code for a variety of ML algorithms and performance metrics is not the most efficient and self-serviced way to complete the task. Vertex AI Workbench is a service that allows you to create and use notebooks for ML development and experimentation. Scikit-learn is a library that provides ML algorithms and utilities for Python. However, this option also requires more effort and resources than option A, as it involves creating and managing the notebooks, writing and running the scikit-learn code, and analyzing the results.
* Option D is incorrect because training a custom TensorFlow model with Vertex AI, reading the data
* from BigQuery featuring a variety of ML algorithms is not the most efficient and self-serviced way to complete the task. TensorFlow is a framework that allows you to create and train ML models using Python or other languages. Vertex AI is a service that allows you to train and deploy ML models using built-in algorithms or custom containers. However, this option also requires more effort and resources than option A, as it involves writing and running the TensorFlow code, creating and managing the training jobs, and analyzing the results.
References:
* BigQuery ML overview
* Creating a model in BigQuery ML
* Evaluating a model in BigQuery ML
* BigQuery ML benefits
* Dataproc overview
* [SparkML overview]
* [Vertex AI Workbench overview]
* [Scikit-learn overview]
* [TensorFlow overview]
* [Vertex AI overview]
質問 # 19
You work for a delivery company. You need to design a system that stores and manages features such as parcels delivered and truck locations over time. The system must retrieve the features with low latency and feed those features into a model for online prediction. The data science team will retrieve historical data at a specific point in time for model training. You want to store the features with minimal effort. What should you do?
正解：B
質問 # 20
You are an ML engineer at a mobile gaming company. A data scientist on your team recently trained a TensorFlow model, and you are responsible for deploying this model into a mobile application. You discover that the inference latency of the current model doesn't meet production requirements. You need to reduce the inference time by 50%, and you are willing to accept a small decrease in model accuracy in order to reach the latency requirement. Without training a new model, which model optimization technique for reducing latency should you try first?
正解：A
解説：
Dynamic range quantization is a model optimization technique for reducing latency that reduces the numerical precision of the weights and activations of models. This technique can reduce the model size, memory usage, and inference time by up to 4x with negligible accuracy loss. Dynamic range quantization can be applied to a trained TensorFlow model without retraining, and it is suitable for mobile applications that require low latency and power consumption.
Weight pruning, model distillation, and dimensionality reduction are also model optimization techniques for reducing latency, but they have some limitations or drawbacks compared to dynamic range quantization:
Weight pruning works by removing parameters within a model that have only a minor impact on its predictions. Pruned models are the same size on disk, and have the same runtime latency, but can be compressed more effectively. This makes pruning a useful technique for reducing model download size, but not for reducing inference time.
Model distillation works by training a smaller and simpler model (student) to mimic the behavior of a larger and complex model (teacher). Distilled models can have lower latency and memory usage than the original models, but they require retraining and may not preserve the accuracy of the teacher model.
Dimensionality reduction works by reducing the number of features or dimensions in the input data or the model layers. Dimensionality reduction can improve the computational efficiency and generalization ability of models, but it may also lose some information or introduce noise in the data or the model. Dimensionality reduction also requires retraining or modifying the model architecture.
Reference:
[TensorFlow Model Optimization]
[TensorFlow Model Optimization Toolkit - Post-Training Integer Quantization]
[Model optimization methods to cut latency, adapt to new data]
質問 # 21
......
JpexamのGoogleどのバージョンでも、Google Professional Machine Learning Engineerガイド資料はダウンロード数とProfessional-Machine-Learning-Engineer同時ユーザー数に制限がないため、ユーザーは同じ質問セットで複数の演習を練習し、知識を繰り返し統合できます。 学習の過程で、Google Professional Machine Learning Engineer実際の試験のテストエンジンは、学習プロセスの弱点を強化するのに便利です。 これは、間違ったProfessional-Machine-Learning-Engineer質問を整理するプロセスの代替として使用できます
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