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Google Professional Machine Learning Engineer 자격증을 취득하면 고용주 및 클라이언트에게 Google Cloud Platform에서 효과적인 기계 학습 솔루션을 디자인하고 구현할 수 있는 기술과 지식이 있다는 것을 증명합니다. 이 자격증은 기계 학습과 클라우드 컴퓨팅에서 기술을 개발하고자하는 데이터 과학자, 소프트웨어 엔지니어 및 기타 전문가들에게 유용한 자격증입니다.

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시험대비 Professional-Machine-Learning-Engineer유효한 공부 공부문제

만약 아직도Google Professional-Machine-Learning-Engineer인증시험 위하여 많은 시간과 정력을 소모하며 열심히 공부하고 있습니까? 아직도 어덯게하면Google Professional-Machine-Learning-Engineer인증시험을 빠르게 취득할 수 있는 방법을 못찿고 계십니까? 지금Itcertkr에서Google Professional-Machine-Learning-Engineer인증시험을 안전하게 넘을 수 있도록 대책을 내드리겠습니다. 아주 신기한 효과가 있을 것입니다.

시험을 치르려면 응시자는 감독 및 감독 학습, 딥 러닝 및 강화 학습을 포함한 기계 학습 개념에 대해 강력한 이해를 가져야합니다. 또한 Automl, AI 플랫폼 및 TensorFlow와 같은 Google Cloud의 기계 학습 서비스와 함께 작업 한 경험이 있어야합니다.

Google Professional Machine Learning Engineer 인증 시험은 기계 학습 엔지니어링 분야 전문가의 기술과 지식을 인증하는 인증 프로그램입니다. 이 인증은 Google Cloud에서 제공되며, Google Cloud 기술을 사용하여 기계 학습 모델을 구축하고 배포하는 경험이 있는 개인을 대상으로 합니다. 이 인증 시험은 후보자의 비즈니스 목표를 충족하는 기계 학습 솔루션을 설계, 구축 및 운영할 수 있는 능력을 평가합니다.

최신 Google Cloud Certified Professional-Machine-Learning-Engineer 무료샘플문제 (Q225-Q230):

질문 # 225
Your company manages an application that aggregates news articles from many different online sources and sends them to users. You need to build a recommendation model that will suggest articles to readers that are similar to the articles they are currently reading. Which approach should you use?

• A. Encode all articles into vectors using word2vec, and build a model that returns articles based on vector similarity.
• B. Manually label a few hundred articles, and then train an SVM classifier based on the manually classified articles that categorizes additional articles into their respective categories.
• C. Create a collaborative filtering system that recommends articles to a user based on the user's past behavior.
• D. Build a logistic regression model for each user that predicts whether an article should be recommended to a user.

정답：A

설명：
Option A is incorrect because creating a collaborative filtering system that recommends articles to a user based on the user's past behavior is not the best approach to suggest articles that are similar to the articles they are currently reading. Collaborative filtering is a method of recommendation that uses the ratings or preferences of other users to predict the preferences of a target user1. However, this method does not consider the content or features of the articles, and may not be able to find articles that are similar in terms of topic, style, or sentiment.
Option B is correct because encoding all articles into vectors using word2vec, and building a model that returns articles based on vector similarity is a suitable approach to suggest articles that are similar to the articles they are currently reading. Word2vec is a technique that learns low-dimensional and dense representations of words from a large corpus of text, such that words that are semantically similar have similar vectors2. By applying word2vec to the articles, we can obtain vector representations of the articles that capture their meaning and usage. Then, we can use a similarity measure, such as cosine similarity, to find articles that have similar vectors to the current article3.
Option C is incorrect because building a logistic regression model for each user that predicts whether an article should be recommended to a user is not a feasible approach to suggest articles that are similar to the articles they are currently reading. Logistic regression is a supervised learning method that models the probability of a binary outcome (such as recommend or not) based on some input features (such as user profile or article content)4. However, this method requires a large amount of labeled data for each user, which may not be available or scalable. Moreover, this method does not directly measure the similarity between articles, but rather the likelihood of a user's preference.
Option D is incorrect because manually labeling a few hundred articles, and then training an SVM classifier based on the manually classified articles that categorizes additional articles into their respective categories is not an effective approach to suggest articles that are similar to the articles they are currently reading. SVM (support vector machine) is a supervised learning method that finds a hyperplane that separates the data into different classes (such as news categories) with the maximum margin5. However, this method also requires a large amount of labeled data, which may be costly and time-consuming to obtain. Moreover, this method does not account for the fine-grained similarity between articles within the same category, or the cross-category similarity between articles from different categories.
Reference:
Collaborative filtering
Word2vec
Cosine similarity
Logistic regression
SVM

질문 # 226
You work for a large technology company that wants to modernize their contact center. You have been asked to develop a solution to classify incoming calls by product so that requests can be more quickly routed to the correct support team. You have already transcribed the calls using the Speech-to-Text API. You want to minimize data preprocessing and development time. How should you build the model?

• A. Use the Cloud Natural Language API to extract custom entities for classification.
• B. Use AutoMlL Natural Language to extract custom entities for classification.
• C. Use the AI Platform Training built-in algorithms to create a custom model.
• D. Build a custom model to identify the product keywords from the transcribed calls, and then run the keywords through a classification algorithm.

정답：B

설명：
AutoML is appropriate to classify incoming calls by product (Custom) to be routed to the correct support team.
Cloud Natural Language API is for general case (not particular business).

질문 # 227
You need to develop an image classification model by using a large dataset that contains labeled images in a Cloud Storage Bucket. What should you do?

• A. Convert the image dataset to a tabular format using Dataflow Load the data into BigQuery and use BigQuery ML to tram the model.
• B. Use Vertex Al Pipelines with TensorFlow Extended (TFX) to create a pipeline that reads the images from Cloud Storage and trams the model.
• C. Import the labeled images as a managed dataset in Vertex Al: and use AutoML to tram the model.
• D. Use Vertex Al Pipelines with the Kubeflow Pipelines SDK to create a pipeline that reads the images from Cloud Storage and trains the model.

정답：C

설명：
The best option for developing an image classification model by using a large dataset that contains labeled images in a Cloud Storage bucket is to import the labeled images as a managed dataset in Vertex AI and use AutoML to train the model. This option allows you to leverage the power and simplicity of Google Cloud to create and deploy a high-quality image classification model with minimal code and configuration. Vertex AI is a unified platform for building and deploying machine learning solutions on Google Cloud. Vertex AI can create a managed dataset from a Cloud Storage bucket that contains labeled images, which can be used to train an AutoML model. AutoML is a service that can automatically build and optimize machine learning models for various tasks, such as image classification, object detection, natural language processing, and tabular data analysis. AutoML can handle the complex aspects of machine learning, such as feature engineering, model architecture, hyperparameter tuning, and model evaluation. AutoML can also evaluate, deploy, and monitor the image classification model, and provide online or batch predictions. By using Vertex AI and AutoML, users can develop an image classification model by using a large dataset with ease and efficiency.
The other options are not as good as option C, for the following reasons:
* Option A: Using Vertex AI Pipelines with the Kubeflow Pipelines SDK to create a pipeline that reads the images from Cloud Storage and trains the model would require more skills and steps than using
* Vertex AI and AutoML. Vertex AI Pipelines is a service that can orchestrate machine learning workflows using Vertex AI. Vertex AI Pipelines can run preprocessing and training steps on custom Docker images, and evaluate, deploy, and monitor the machine learning model. Kubeflow Pipelines SDK is a Python library that can create and run pipelines on Vertex AI Pipelines or on Kubeflow, an open-source platform for machine learning on Kubernetes. However, using Vertex AI Pipelines and Kubeflow Pipelines SDK would require writing code, building Docker images, defining pipeline components and steps, and managing the pipeline execution and artifacts. Moreover, Vertex AI Pipelines and Kubeflow Pipelines SDK are not specialized for image classification, and users would need to use other libraries or frameworks, such as TensorFlow or PyTorch, to build and train the image classification model.
* Option B: Using Vertex AI Pipelines with TensorFlow Extended (TFX) to create a pipeline that reads the images from Cloud Storage and trains the model would require more skills and steps than using Vertex AI and AutoML. TensorFlow Extended (TFX) is a framework that can create and run end-to-end machine learning pipelines on TensorFlow, a popular library for building and training deep learning models. TFX can preprocess the data, train and evaluate the model, validate and push the model, and serve the model for online or batch predictions. However, using Vertex AI Pipelines and TFX would require writing code, building Docker images, defining pipeline components and steps, and managing the pipeline execution and artifacts. Moreover, TFX is not optimized for image classification, and users would need to use other libraries or tools, such as TensorFlow Data Validation, TensorFlow Transform, and TensorFlow Hub, to handle the image data and the model architecture.
* Option D: Converting the image dataset to a tabular format using Dataflow, loading the data into BigQuery, and using BigQuery ML to train the model would not handle the image data properly and could result in a poor model performance. Dataflow is a service that can create scalable and reliable pipelines to process large volumes of data from various sources. Dataflow can preprocess the data by using Apache Beam, a programming model for defining and executing data processing workflows.
BigQuery is a serverless, scalable, and cost-effective data warehouse that can perform fast and interactive queries on large datasets. BigQuery ML is a service that can create and train machine learning models by using SQL queries on BigQuery. However, converting the image data to a tabular format would lose the spatial and semantic information of the images, which are essential for image classification. Moreover, BigQuery ML is not specialized for image classification, and users would need to use other tools or techniques, such as feature hashing, embedding, or one-hot encoding, to handle the categorical features.

질문 # 228
You work for a magazine distributor and need to build a model that predicts which customers will renew their subscriptions for the upcoming year. Using your company's historical data as your training set, you created a TensorFlow model and deployed it to Vertex AI. You need to determine which customer attribute has the most predictive power for each prediction served by the model. What should you do?

• A. Use the What-If tool in Google Cloud to determine how your model will perform when individual features are excluded. Rank the feature importance in order of those that caused the most significant performance drop when removed from the model.
• B. Stream prediction results to BigQuery. Use BigQuery's CORR(X1, X2) function to calculate the Pearson correlation coefficient between each feature and the target variable.
• C. Use Vertex AI Workbench user-managed notebooks to perform a Lasso regression analysis on your model, which will eliminate features that do not provide a strong signal.
• D. Use Vertex Explainable AI. Submit each prediction request with the explain' keyword to retrieve feature attributions using the sampled Shapley method.

정답：D

질문 # 229
You recently deployed a pipeline in Vertex Al Pipelines that trains and pushes a model to a Vertex Al endpoint to serve real-time traffic. You need to continue experimenting and iterating on your pipeline to improve model performance. You plan to use Cloud Build for CI/CD You want to quickly and easily deploy new pipelines into production and you want to minimize the chance that the new pipeline implementations will break in production. What should you do?

• A. Set up a CI/CD pipeline that builds and tests your source code and then deploys built artifacts into a pre-production environment. After a successful pipeline run in the pre-production environment deploy the pipeline to production
• B. Set up a CI/CD pipeline that builds and tests your source code and then deploys built arrets into a pre-production environment After a successful pipeline run in the pre-production environment, rebuild the source code, and deploy the artifacts to production
• C. Set up a CI/CD pipeline that builds and tests your source code If the tests are successful use the Google Cloud console to upload the built container to Artifact Registry and upload the compiled pipeline to Vertex Al Pipelines.
• D. Set up a CI/CD pipeline that builds your source code and then deploys built artifacts into a pre-production environment Run unit tests in the pre-production environment If the tests are successful deploy the pipeline to production.

정답：A

설명：
The best option for continuing experimenting and iterating on your pipeline to improve model performance, using Cloud Build for CI/CD, and deploying new pipelines into production quickly and easily, is to set up a CI/CD pipeline that builds and tests your source code and then deploys built artifacts into a pre-production environment. After a successful pipeline run in the pre-production environment, deploy the pipeline to production. This option allows you to leverage the power and simplicity of Cloud Build to automate, monitor, and manage your pipeline development and deployment workflow. Cloud Build is a service that can create and run continuous integration and continuous delivery (CI/CD) pipelines on Google Cloud. Cloud Build can build your source code, run unit tests, and deploy built artifacts to various Google Cloud services, such as Vertex AI Pipelines, Vertex AI Endpoints, and Artifact Registry. A CI/CD pipeline is a workflow that can automate the process of building, testing, and deploying software. A CI/CD pipeline can help you improve the quality and reliability of your software, accelerate the development and delivery cycle, and reduce the manual effort and errors. A pre-production environment is an environment that can simulate the production environment, but is isolated from the real users and data. A pre-production environment can help you test and validate your software before deploying it to production, and catch any bugs or issues that may affect the user experience or the system performance. By setting up a CI/CD pipeline that builds and tests your source code and then deploys built artifacts into a pre-production environment, you can ensure that your pipeline code is consistent and error-free, and that your pipeline artifacts are compatible and functional. After a successful pipeline run in the pre-production environment, you can deploy the pipeline to production, which is the environment where your software is accessible and usable by the real users and data. By deploying the pipeline to production after a successful pipeline run in the pre-production environment, you can minimize the chance that the new pipeline implementations will break in production, and ensure that your software meets the user expectations and requirements1.
The other options are not as good as option C, for the following reasons:
* Option A: Setting up a CI/CD pipeline that builds and tests your source code, and if the tests are successful, using the Google Cloud console to upload the built container to Artifact Registry and upload the compiled pipeline to Vertex AI Pipelines would not allow you to deploy new pipelines into production quickly and easily, and could increase the manual effort and errors. The Google Cloud console is a web-based user interface that can help you access and manage various Google Cloud services, such as Artifact Registry and Vertex AI Pipelines. Artifact Registry is a service that can store and manage your container images and other artifacts on Google Cloud. Artifact Registry can help you upload and organize your container images, and track the image versions and metadata. Vertex AI Pipelines is a service that can orchestrate machine learning workflows using Vertex AI. Vertex AI Pipelines can run preprocessing and training steps on custom Docker images, and evaluate, deploy, and monitor the machine learning model. However, setting up a CI/CD pipeline that builds and tests your source code, and if the tests are successful, using the Google Cloud console to upload the built container to Artifact Registry and upload the compiled pipeline to Vertex AI Pipelines would not allow you to deploy new pipelines into production quickly and easily, and could increase the manual effort and errors. You would need to write code, create and run the CI/CD pipeline, use the Google Cloud console to upload the built container to Artifact Registry, and use the Google Cloud console to upload the compiled pipeline to Vertex AI Pipelines. Moreover, this option would not use a pre-production environment to test and validate your pipeline before deploying it to production, which could increase the chance that the new pipeline implementations will break in production1.
* Option B: Setting up a CI/CD pipeline that builds your source code and then deploys built artifacts into a pre-production environment, running unit tests in the pre-production environment, and if the tests are successful, deploying the pipeline to production would not allow you to test and validate your pipeline before deploying it to production, and could cause errors or poor performance. A unit test is a type of test that can verify the functionality and correctness of a small and isolated unit of code, such as a function or a class. A unit test can help you debug and improve your code quality, and catch any bugs or issues that may affect the code logic or output. However, setting up a CI/CD pipeline that builds your source code and then deploys built artifacts into a pre-production environment, running unit tests in the pre-production environment, and if the tests are successful, deploying the pipeline to production would not allow you to test and validate your pipeline before deploying it to production, and could cause errors or poor performance. You would need to write code, create and run the CI/CD pipeline, deploy the built artifacts to the pre-production environment, run the unit tests in the pre-production environment, and deploy the pipeline to production. Moreover, this option would not run the pipeline in the pre-production environment, which could prevent you from testing and validating the pipeline functionality and compatibility, and catching any bugs or issues that may affect the pipeline workflow or output1.
* Option D: Setting up a CI/CD pipeline that builds and tests your source code and then deploys built artifacts into a pre-production environment, after a successful pipeline run in the pre-production environment, rebuilding the source code, and deploying the artifacts to production would not allow you to deploy new pipelines into production quickly and easily, and could increase the complexity and cost of the pipeline development and deployment. Rebuilding the source code is a process that can recompile and repackage the source code into executable artifacts, such as container images and pipeline files.
Rebuilding the source code can help you incorporate any changes or updates that may have occurred in the source code, and ensure that the artifacts are consistent and up-to-date. However, setting up a CI/CD pipeline that builds and tests your source code and then deploys built artifacts into a pre-production environment, after a successful pipeline run in the pre-production environment, rebuilding the source code, and deploying the artifacts to production would not allow you to deploy new pipelines into production quickly and easily, and could increase the complexity and cost of the pipeline development and deployment. You would need to write code, create and run the CI/CD pipeline, deploy the built
* artifacts to the pre-production environment, run the pipeline in the pre-production environment, rebuild the source code, and deploy the artifacts to production. Moreover, this option would increase the pipeline development and deployment time, as rebuilding the source code can be a time-consuming and resource-intensive process1.
References:
* Preparing for Google Cloud Certification: Machine Learning Engineer, Course 3: Production ML Systems, Week 3: MLOps
* Google Cloud Professional Machine Learning Engineer Exam Guide, Section 3: Scaling ML models in production, 3.2 Automating ML workflows
* Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 6:
Production ML Systems, Section 6.4: Automating ML Workflows
* Cloud Build
* Vertex AI Pipelines
* Artifact Registry
* Pre-production environment

질문 # 230
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