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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Professional Machine Learning Engineer Certification Exam |
| Exam Number: | Professional-Machine-Learning-Engineer |
| Real Exam Qty: | Approximately 50–60 questions |
| Exam Duration: | 120 minutes |
| Certificate Validity Period: | 2 years |
| Exam Format: | Multiple select, Case study, Multiple choice |
| Available Languages: | Japanese, English |
| Related Certifications: | Google Cloud Professional Cloud Architect Google Cloud Associate Cloud Engineer Google Cloud Professional Data Engineer |
| Exam Price: | $200 USD |
| Recommended Training: | Vertex AI Documentation Google Cloud Skills Boost - Machine Learning Engineer Path |
| Exam Registration: | Kryterion Webassessor Google Cloud Certification Portal |
| Sample Questions: | Google Professional-Machine-Learning-Engineer Sample Questions |
| Exam Way: | Online proctored exam or in-person testing via Kryterion test centers. |
| Pre Condition: | No formal prerequisites required, but 3+ years of industry experience in ML/AI and familiarity with Google Cloud Platform are strongly recommended. |
| Official Syllabus URL: | https://cloud.google.com/certification/machine-learning-engineer |
>> Professional-Machine-Learning-Engineer最新題庫 <<
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要成為認證的 Google 專業機器學習工程師，考生必須展示多個關鍵領域的熟練程度，包括數據準備和處理、機器學習模型開發和實施以及商業問題解決。考試涵蓋了廣泛的主題，包括數據建模、分佈式計算以及 TensorFlow 和 Keras 等機器學習框架。此外，考生必須熟悉機器學習中使用的工具和技術，包括 Google Cloud 平台服務，如 BigQuery、Dataflow 和 Kubernetes。
問題 #128
You want to train an AutoML model to predict house prices by using a small public dataset stored in BigQuery. You need to prepare the data and want to use the simplest most efficient approach. What should you do?
答案：B
問題 #129
You work for a social media company. You want to create a no-code image classification model for an iOS mobile application to identify fashion accessories You have a labeled dataset in Cloud Storage You need to configure a training workflow that minimizes cost and serves predictions with the lowest possible latency What should you do?
答案：A
解題說明：
* AutoML Edge is a service that allows you to train and deploy custom image classification models for mobile devices12. It supports exporting models as Core ML files, which are compatible with iOS applications3.
* Using a Core ML model directly on the device eliminates the need for network requests and reduces prediction latency. It also minimizes the cost of serving predictions, as there is no need to pay for cloud resources or network bandwidth.
* Option A is incorrect because sending batch requests during prediction does not reduce latency, as the requests still need to be processed by the cloud service. It also incurs more cost than using a local model on the device.
* Option C is incorrect because TFLite models are not compatible with iOS applications. TFLite models are designed for Android and other platforms that support TensorFlow Lite4.
* Option D is incorrect because exposing the model as a Vertex AI endpoint requires network requests
* and cloud resources, which increase latency and cost. It also does not leverage the benefits of AutoML Edge, which is optimized for mobile devices.
問題 #130
You are developing an ML model that uses sliced frames from video feed and creates bounding boxes around specific objects. You want to automate the following steps in your training pipeline: ingestion and preprocessing of data in Cloud Storage, followed by training and hyperparameter tuning of the object model using Vertex AI jobs, and finallydeploying the model to an endpoint. You want to orchestrate the entire pipeline with minimal cluster management. What approach should you use?
答案：D
解題說明：
* Option A is incorrect because using Kubeflow Pipelines on Google Kubernetes Engine is not the most convenient way to orchestrate the entire pipeline with minimal cluster management. Kubeflow Pipelines is an open-source platform that allows you to build, run, and manage ML pipelines using containers1. Google Kubernetes Engine is a service that allows you to create and manage clusters of virtual machines that run Kubernetes, an open-source system for orchestrating containerized applications2. However, this option requires more effort and resources than option B, as it involves creating and configuring the clusters, installing and maintaining Kubeflow Pipelines, and writing and running the pipeline code.
* Option B is correct because using Vertex AI Pipelines with TensorFlow Extended (TFX) SDK is the best way to orchestrate the entire pipeline with minimal cluster management. Vertex AI Pipelines is a service that allows you to create and run scalable and portable ML pipelines on Google Cloud3. TensorFlow Extended (TFX) is a framework that provides a set of components and libraries for building production-ready ML pipelines using TensorFlow4. You can use Vertex AI Pipelines with TFX SDK to ingest and preprocess the data in Cloud Storage, train and tune the object model using Vertex AI jobs, and deploy the model to an endpoint, using predefined or custom components. Vertex AI Pipelines handles the underlying infrastructure and orchestration for you, so you don't need to worry about cluster management or scalability.
* Option C is incorrect because using Vertex AI Pipelines with Kubeflow Pipelines SDK is not the most suitable way to orchestrate the entire pipeline with minimal cluster management. Kubeflow Pipelines SDK is a library that allows you to build and run ML pipelines using Kubeflow Pipelines5. You can use Vertex AI Pipelines with Kubeflow Pipelines SDK to create and run ML pipelines on Google Cloud, using containers. However, this option is less convenient and consistent than option B, as it requires you to use different APIs and tools for different steps of the pipeline, such as Vertex AI SDK for training and deployment, and Kubeflow Pipelines SDK for ingestion and preprocessing. Moreover, this option does not leverage the benefits of TFX, such as the standard components, the metadata store, or the ML Metadata library.
* Option D is incorrect because using Cloud Composer for the orchestration is not the most efficient way to orchestrate the entire pipeline with minimal cluster management. Cloud Composer is a service that allows you to create and run workflows using Apache Airflow, an open-source platform for orchestrating complex tasks. You can use Cloud Composer to orchestrate the entire pipeline, by creating and managing DAGs (directed acyclic graphs) that define the dependencies and order of the tasks.
However, this option is more complex andcostly than option B, as it involves creating and configuring the environments, installing and maintaining Airflow, and writing and running the DAGs.
References:
* Kubeflow Pipelines documentation
* Google Kubernetes Engine documentation
* Vertex AI Pipelines documentation
* TensorFlow Extended documentation
* Kubeflow Pipelines SDK documentation
* [Cloud Composer documentation]
* [Vertex AI documentation]
* [Cloud Storage documentation]
* [TensorFlow documentation]
問題 #131
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?
答案：A
解題說明：
Vertex AI Feature Store is a service that allows you to store and manage your ML features on Google Cloud. You can use Vertex AI Feature Store to store features such as parcels delivered and truck locations over time, and retrieve them with low latency for online prediction. Online prediction is a type of prediction that provides low-latency responses to individual or small batches of input data. You can also use Vertex AI Feature Store to retrieve historical data at a specific point in time for model training. Model training is a process of learning the parameters of a ML model from data. By using Vertex AI Feature Store, you can store the features with minimal effort, and avoid the complexity of managing your own data storage and serving system. Reference:
Vertex AI Feature Store documentation
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
問題 #132
You are tuning a gradient-boosted model with eight hyperparameters. Your previous approach used grid search, which consumed the entire monthly compute budget without converging on a strong configuration. You need an approach that finds a good configuration with fewer trials and automatically abandons unpromising runs. What should you do?
答案：A
解題說明：
Bayesian optimization uses results from completed trials to select promising regions of the search space, which converges faster than exhaustive or random search in high-dimensional spaces.
Early stopping terminates underperforming trials before completion, further reducing total compute cost.
問題 #133
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