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| Section | Objectives |
|---|---|
| Topic 1: Collaborating within and across teams to manage data and models | - Data management and governance - Version control and reproducibility (e.g., DVC, MLOps) - Collaboration between Data Scientists, Data Engineers, and ML Engineers |
| Topic 2: Monitoring ML solutions | - Model retraining strategies - Performance monitoring and drift detection - Logging and alerting (Cloud Monitoring) |
| Topic 3: Scaling prototypes into ML models | - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) - Training at scale (Distributed training, TPUs) - Hyperparameter tuning |
| Topic 4: Serving and scaling models | - Batch prediction - Hardware accelerators (GPU/TPU) in serving - Model optimization (Quantization, Distillation) - Online prediction (Vertex AI Prediction) |
| Topic 5: Architecting low-code ML solutions | - AutoML capabilities and implementation - Implementing BigQuery ML for basic models - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) |
| Topic 6: Automating and orchestrating ML pipelines | - CI/CD for ML systems - Vertex AI Pipelines (Kubeflow Pipelines) - Triggering and scheduling pipelines |
>> Professional-Machine-Learning-Engineer受験対策解説集 <<
多くの受験者は、Professional-Machine-Learning-Engineer試験に合格するための準備で困難に直面しています。しかし、当社の教材は、受験者が試験に簡単に合格するのに役立ちます。 Professional-Machine-Learning-Engineerガイドの質問は、Google学習者が脆弱なリンクを見つけて対処するのに役立つ統計レポート機能を提供できます。 Professional-Machine-Learning-Engineerテストトレントは、タイミングの機能と試験のシミュレーションを強化します。タイマーを設定して試験をシミュレートし、学習者が速度を調整してアラートを維持できるようにします。 Professional-Machine-Learning-Engineerガイドの質問は、学習者が試験をマスターして合格するのに非常に便利です。
質問 # 210
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：C
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.
Reference:
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 # 211
You are an ML engineer at a global car manufacture. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?
正解：B
解説：
https://developers.google.com/machine-learning/crash-course/feature-crosses/check-your-understanding
質問 # 212
You work for a manufacturing company. You need to train a custom image classification model to detect product defects at the end of an assembly line. Although your model is performing well, some images in your holdout set are consistently mislabeled with high confidence. You want to use Vertex AI to understand your model's results. What should you do?
正解：B
質問 # 213
You work for a startup that has multiple data science workloads. Your compute infrastructure is currently on-premises, and the data science workloads are native to PySpark. Your team plans to migrate their data science workloads to Google Cloud. You need to build a proof of concept to migrate one data science job to Google Cloud. You want to propose a migration process that requires minimal cost and effort. What should you do first?
正解：C
質問 # 214
You have a high-traffic ecommerce application that uses a recommendation model deployed on an Agent Platform endpoint. During flash sales, traffic quickly spikes from 1,000 to 50,000 queries per second (QPS). During these spikes, the application returns 503: Service Unavailable errors because the autoscaler cannot provision new resources quickly enough to meet the sudden demand. Your need to eliminate service disruptions during the start of the sale. How should you configure the endpoint?
正解：D
解説：
Increasing the minimum replica count pre-provisions enough serving capacity to absorb the initial traffic surge before autoscaling can add more replicas. Setting a sufficiently high maximum replica count then allows the endpoint to continue scaling as demand grows, preventing startup- period 503 errors during flash sales.
質問 # 215
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さらに、PassTest Professional-Machine-Learning-Engineerダンプの一部が現在無料で提供されています：https://drive.google.com/open?id=1TTpW9pJhHtz8RKb_vVVoYaHthInb74PX