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Professional-Machine-Learning-Engineer資格トレーニング、Professional-Machine-Learning-Engineer勉強ガイド

Professional-Machine-Learning-Engineer試験はIT業界でのあなたにとって重要です。あなたはProfessional-Machine-Learning-Engineer試験に悩んでいますか？試験に合格できないことを心配していますか？我々の提供した一番新しくて全面的なGoogleのProfessional-Machine-Learning-Engineer問題集はあなたのすべての需要を満たすことができます。資格をもらうのはあなたの発展の第一歩で、我々のProfessional-Machine-Learning-Engineer日本語対策はあなたを助けて試験に合格して資格をもらうことができます。

Google Machine-Machine-Learning-Enginer認定試験は、Google Cloudプラットフォームに機械学習モデルの構築と展開に関する専門知識を実証したいと考えている機械学習エンジニア、データサイエンティスト、およびソフトウェア開発者を対象としています。この試験では、データの準備と分析、機能エンジニアリング、モデルの選択とトレーニング、モデルの評価と最適化、Googleクラウドプラットフォームでの機械学習モデルの展開と管理など、幅広いトピックをカバーしています。

Google Professional Machine Learning Engineer認定を取得することは、この分野の専門家にとって多数の利点を提供することができます。これにより、潜在的な雇用主やクライアントに自分の専門知識を証明することができ、競争優位性を得ることができます。さらに、機械学習エンジニアリングの分野でキャリアを発展させ、収益性を高めることができます。全体的に、Google Professional Machine Learning Engineer認定試験は、この急速に成長する分野でのスキルと知識を検証するための優れた機会です。

>> Professional-Machine-Learning-Engineer資格トレーニング <<

Professional-Machine-Learning-Engineer勉強ガイド、Professional-Machine-Learning-Engineer復習攻略問題

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Google Professional Machine Learning Engineer 認定 Professional-Machine-Learning-Engineer 試験問題 (Q115-Q120):

質問 # 115
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. Build a logistic regression model for each user that predicts whether an article should be recommended to a user.
• D. Create a collaborative filtering system that recommends articles to a user based on the user's past behavior.

正解：D

質問 # 116
You are creating a model training pipeline to predict sentiment scores from text-based product reviews. You want to have control over how the model parameters are tuned, and you will deploy the model to an endpoint after it has been trained You will use Vertex Al Pipelines to run the pipeline You need to decide which Google Cloud pipeline components to use What components should you choose?

• A.
• B.
• C.
• D.

正解：B

解説：
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 ModelEvaluationOp and ModelDeployOp are two such components that can be used to evaluate and deploy a model to an endpoint for online inference3. However, Vertex AI Pipelines does not provide a prebuilt component for hyperparameter tuning. Therefore, to have control over how the model parameters are tuned, you need to use a custom component that calls the Vertex AI HyperparameterTuningJob service4. Therefore, option A is the best way to decide which Google Cloud pipeline components to use for the given use case, as it includes a custom component for hyperparameter tuning, and prebuilt components for model evaluation and deployment. The other options are not relevant or optimal for this scenario. References:
* Vertex AI Pipelines
* Google Cloud Pipeline Components
* Vertex AI ModelEvaluationOp and ModelDeployOp
* Vertex AI HyperparameterTuningJob
* Google Professional Machine Learning Certification Exam 2023
* Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

質問 # 117
Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which platform components should you choose for this system?

• A. Cloud Composer, Vertex AI Training with custom containers, and App Engine
• B. Vertex AI Pipelines and App Engine
• C. Cloud Composer, BigQuery ML, and Vertex AI Prediction
• D. Vertex AI Pipelines, Vertex AI Prediction, and Vertex AI Model Monitoring

正解：D

解説：
* Option A is incorrect because Vertex AI Pipelines and App Engine do not meet all the requirements of the system. Vertex AI Pipelines is a service that allows you to create, run, and manage ML workflows using TensorFlow Extended (TFX) components or custom components1. App Engine is a service that allows you to build and deploy scalable web applications using standard or flexible
* environments2. However, App Engine does not support Docker containers in the standard environment, and does not provide a dedicated service for online prediction and monitoring of ML models3.
* Option B is correct because Vertex AI Pipelines, Vertex AI Prediction, and Vertex AI Model Monitoring meet all the requirements of the system. Vertex AI Prediction is a service that allows you to deploy and serve ML models for online or batch prediction, with support for autoscaling and custom containers4. Vertex AI Model Monitoring is a service that allows you to monitor the performance and fairness of your deployed models, and get alerts for any issues or anomalies5.
* Option C is incorrect because Cloud Composer, BigQuery ML, and Vertex AI Prediction do not meet all the requirements of the system. Cloud Composer is a service that allows you to create, schedule, and manage workflows using Apache Airflow. BigQuery ML is a service that allows you to create and use ML models within BigQuery using SQL queries. However, BigQuery ML does not support custom containers, and Vertex AI Prediction does not support scheduled model retraining or model monitoring.
* Option D is incorrect because Cloud Composer, Vertex AI Training with custom containers, and App Engine do not meet all the requirements of the system. Vertex AI Training is a service that allows you to train ML models using built-in algorithms or custom containers. However, Vertex AI Training does not support online prediction or model monitoring, and App Engine does not support Docker containers in the standard environment or online prediction and monitoring of ML models3.
References:
* Vertex AI Pipelines overview
* App Engine overview
* Choosing an App Engine environment
* Vertex AI Prediction overview
* Vertex AI Model Monitoring overview
* [Cloud Composer overview]
* [BigQuery ML overview]
* [BigQuery ML limitations]
* [Vertex AI Training overview]

質問 # 118
You have a custom job that runs on Vertex Al on a weekly basis The job is Implemented using a proprietary ML workflow that produces the datasets. models, and custom artifacts, and sends them to a Cloud Storage bucket Many different versions of the datasets and models were created Due to compliance requirements, your company needs to track which model was used for making a particular prediction, and needs access to the artifacts for each model. How should you configure your workflows to meet these requirement?

• A. Configure a TensorFlow Extended (TFX) ML Metadata database, and use the ML Metadata API.
• B. Create a Vertex Al experiment, and enable autologging inside the custom job
• C. Register each model in Vertex Al Model Registry, and use model labels to store the related dataset and model information.
• D. Use the Vertex Al Metadata API inside the custom Job to create context, execution, and artifacts for each model, and use events to link them together.

正解：C

質問 # 119
You are designing an ML recommendation model for shoppers on your company's ecommerce website. You will use Recommendations Al to build, test, and deploy your system. How should you develop recommendations that increase revenue while following best practices?

• A. Import your user events and then your product catalog to make sure you have the highest quality event stream
• B. Use the "Other Products You May Like" recommendation type to increase the click-through rate
• C. Use the "Frequently Bought Together' recommendation type to increase the shopping cart size for each order.
• D. Because it will take time to collect and record product data, use placeholder values for the product catalog to test the viability of the model.

正解：C

解説：
Recommendations AI is a service that allows users to build, test, and deploy personalized product recommendations for their ecommerce websites. It uses Google's deep learning models to learn from user behavior and product data, and generate high-quality recommendations that can increase revenue, click-through rate, and customer satisfaction. One of the best practices for using Recommendations AI is to choose the right recommendation type for the business objective. The "Frequently Bought Together" recommendation type shows products that are often purchased together with the current product, and encourages users to add more items to their shopping cart. This can increase the average order value and the revenue for each transaction. The other options are not as effective or feasible for this objective. The "Other Products You May Like" recommendation type shows products that are similar to the current product, and may increase the click-through rate, but not necessarily the shopping cart size. Importing the user events and then the product catalog is not a recommended order, as it may cause data inconsistency and missing recommendations. The product catalog should be imported first, and then the user events. Using placeholder values for the product catalog is not a viable option, as it will not produce meaningful recommendations or reflect the real performance of the model. Reference:
Recommendations AI documentation
Choosing a recommendation type
Importing data to Recommendations AI

質問 # 120
......

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