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該考試包含多項選擇題、表現型任務和案例研究，旨在評估候選人使用Google Cloud的機器學習工具和服務進行設計和實施機器學習解決方案的能力。考試旨在測試候選人對於主要機器學習概念的知識，如監督式和非監督式學習，深度學習，自然語言處理和計算機視覺。該考試還評估候選人對於如何構建可擴展和可靠的機器學習模型以處理大量數據集的理解。

該考試涵蓋與機器學習相關的各種主題，包括數據準備、模型設計和實施、模型培訓和評估，以及部署和監測機器學習模型。成功的候選人將能夠展示他們使用Google Cloud Platform工具和服務設計和實施機器學習模型的能力，以及他們優化性能並確保機器學習系統的可靠性和可擴展性的能力。該認證被認為是從事機器學習領域的專業人士的有價值的憑證，它可以幫助增強職業機遇和收入潛力。

最新的 Google Cloud Certified Professional-Machine-Learning-Engineer 免費考試真題 (Q251-Q256):

問題 #251
You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano. Scikit-team, and custom libraries. What should you do?

* A. Create a library of VM images on Compute Engine; and publish these images on a centralized repository
* B. Configure Kubeflow to run on Google Kubernetes Engine and receive training jobs through TFJob
* C. Use the Al Platform custom containers feature to receive training jobs using any framework
* D. Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.
答案：C

解題說明：
A cloud-based backend system is a system that runs on a cloud platform and provides services or resources to other applications or users. A cloud-based backend system can be used to submit training jobs, which are tasks that involve training a machine learning model on a given dataset using a specific framework and configuration1 However, a cloud-based backend system can also have some drawbacks, such as:
* High maintenance: A cloud-based backend system may require a lot of administration and management, such as provisioning, scaling, monitoring, and troubleshooting the cloud resources and services. This can be time-consuming and costly, and may distract from the core business objectives2
* Low flexibility: A cloud-based backend system may not support all the frameworks and libraries that the data scientists need to use for their training jobs. This can limit the choices and capabilities of the data scientists, and affect the quality and performance of their models3
* Poor integration: A cloud-based backend system may not integrate well with other cloud services or tools that the data scientists need to use for their machine learning workflows, such as data processing, model deployment, or model monitoring. This can create compatibility and interoperability issues, and reduce the efficiency and productivity of the data scientists.
Therefore, it may be better to use a managed service instead of a cloud-based backend system to submit training jobs. A managed service is a service that is provided and operated by a third-party provider, and offers various benefits, such as:
* Low maintenance: A managed service handles the administration and management of the cloud resources and services, and abstracts away the complexity and details of the underlying infrastructure. This can save time and money, and allow the data scientists to focus on their core tasks2
* High flexibility: A managed service can support multiple frameworks and libraries that the data scientists need to use for their training jobs, and allow them to customize and configure their training environments and parameters. This can enhance the choices and capabilities of the data scientists, and improve the quality and performance of their models3
* Easy integration: A managed service can integrate seamlessly with other cloud services or tools that the data scientists need to use for their machine learning workflows, and provide a unified and consistent interface and experience. This can solve the compatibility and interoperability issues, and increase the efficiency and productivity of the data scientists.
One of the best options for using a managed service to submit training jobs is to use the AI Platform custom containers feature to receive training jobs using any framework. AI Platform is a Google Cloud service that provides a platform for building, deploying, and managing machine learning models. AI Platform supports various machine learning frameworks, such as TensorFlow, PyTorch, scikit-learn, and XGBoost, and provides various features, such as hyperparameter tuning, distributed training, online prediction, and model monitoring.
The AI Platform custom containers feature allows the data scientists to use any framework or library that they want for their training jobs, and package their training application and dependencies as a Docker container image. The data scientists can then submit their training jobs to AI Platform, and specify the container image and the training parameters. AI Platform will run the training jobs on the cloud infrastructure, and handle the scaling, logging, and monitoring of the training jobs. The data scientists can also use the AI Platform features to optimize, deploy, and manage their models.
The other options are not as suitable or feasible. Configuring Kubeflow to run on Google Kubernetes Engine and receive training jobs through TFJob is not ideal, as Kubeflow is mainly designed for TensorFlow-based training jobs, and does not support other frameworks or libraries. Creating a library of VM images on Compute Engine and publishing these images on a centralized repository is not optimal, as Compute Engine is a low-level service that requires a lot of administration and management, and does not provide the features and integrations of AI Platform. Setting up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure is not relevant, as Slurm is a tool for managing and scheduling jobs on a cluster of nodes, and does not provide a managed service for training jobs.
References: 1: Cloud computing 2: Managed services 3: Machine learning frameworks : : :

問題 #252
You are developing an ML model to identify your company s products in images. You have access to over one million images in a Cloud Storage bucket. You plan to experiment with different TensorFlow models by using Vertex Al Training You need to read images at scale during training while minimizing data I/O bottlenecks What should you do?

* A. Load the images directly into the Vertex Al compute nodes by using Cloud Storage FUSE Read the images by using the tf .data.Dataset.from_tensor_slices function.
* B. Create a Vertex Al managed dataset from your image data Access the aip_training_data_uri environment variable to read the images by using the tf. data. Dataset. Iist_flies function.
* C. Convert the images to TFRecords and store them in a Cloud Storage bucket Read the TFRecords by using the tf. ciata.TFRecordDataset function.
* D. Store the URLs of the images in a CSV file Read the file by using the tf.data.experomental.CsvDataset function.
答案：C

解題說明：
TFRecords are a binary file format that can store large amounts of data efficiently. By converting the images to TFRecords and storing them in a Cloud Storage bucket, you can reduce the data size and improve the data transfer speed. You can then read the TFRecords by using the tf.data.TFRecordDataset function, which creates a dataset of tensors from the TFRecord files. This way, you can read images at scale during training while minimizing data I/O bottlenecks. References:
* TFRecord documentation
* tf.data.TFRecordDataset documentation
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

問題 #253
You are developing an ML pipeline using Vertex Al Pipelines. You want your pipeline to upload a new version of the XGBoost model to Vertex Al Model Registry and deploy it to Vertex Al End points for online inference. You want to use the simplest approach. What should you do?

* A. Use the Vertex Al SDK for Python within a custom component based on a python: 3.10 Image.
* B. Chain the Vertex Al ModelUploadOp and ModelDeployop components together.
* C. Use the Vertex Al ModelEvaluationOp component to evaluate the model.
* D. Use the Vertex Al REST API within a custom component based on a vertex-ai/prediction/xgboost-cpu image.
答案：B

解題說明：
According to the web search results, Vertex AI Pipelines is a serverless orchestrator for running ML pipelines, using either the KFP SDK or TFX1. Vertex AI Pipelines provides a set of prebuilt components that can be used to perform common ML tasks, such as training, evaluation, deployment, and more2. Vertex AI ModelUploadOp and ModelDeployOp are two such components that can be used to upload a new version of the XGBoost model to Vertex AI Model Registry and deploy it to Vertex AI Endpoints for online inference3.
Therefore, option D is the best way to use the simplest approach for the given use case, as it only requires chaining two prebuilt components together. The other options are not relevant or optimal for this scenario.
References:
* Vertex AI Pipelines
* Google Cloud Pipeline Components
* Vertex AI ModelUploadOp and ModelDeployOp
* Google Professional Machine Learning Certification Exam 2023
* Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

問題 #254
You need to train a natural language model to perform text classification on product descriptions that contain millions of examples and 100,000 unique words. You want to preprocess the words individually so that they can be fed into a recurrent neural network. What should you do?

* A. Assign a numerical value to each word from 1 to 100,000 and feed the values as inputs in your model.
* B. Sort the words by frequency of occurrence, and use the frequencies as the encodings in your model.
* C. Create a hot-encoding of words, and feed the encodings into your model.
* D. Identify word embeddings from a pre-trained model, and use the embeddings in your model.
答案：D

問題 #255
A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age.
Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population How should the Data Scientist correct this issue?

* A. Drop the age feature from the dataset and train the model using the rest of the features.
* B. Replace the age field value for records with a value of 0 with the mean or median value from the dataset
* C. Drop all records from the dataset where age has been set to 0.
* D. Use k-means clustering to handle missing features
答案：C

解題說明：
Explanation

問題 #256
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