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Google Professional Machine Learning Engineer認定試験に合格することで、キャリアに多くのメリットがあります。まず、潜在的な雇用主に、機械学習の概念を深く理解し、現実世界の問題に適用できることを証明します。次に、新しい仕事の機会を拓き、収入の向上につながります。最後に、より複雑な機械学習プロジェクトに取り組む自信を与え、キャリアを新しい高みに進めることができます。
Google Professional Machine Learning Engineer 認定試験は、機械学習エンジニアの分野でのスキルと知識を検証するために設計された認定プログラムです。この認定は、Google Cloudによって提供され、Google Cloudテクノロジを使用して機械学習モデルを構築および展開する経験がある個人を対象としています。この認定試験では、候補者のビジネス目標を満たす機械学習ソリューションを設計、構築、運用する能力が評価されます。
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Google Professional Machine Learning Engineer認定を取得するには、候補者は、複数選択およびシナリオベースの質問で構成される2時間の試験に合格する必要があります。この試験では、Google Cloudプラットフォームを使用して、スケーラブルで効率的で安全な機械学習モデルを設計および開発する能力について候補者を評価します。この認定は世界的に認識されており、専門家の専門知識と機械学習の分野で最先端のテクノロジーを扱う能力を実証するため、雇用主は高く評価されています。この認定はまた、Googleのリソースと機械学習の専門家コミュニティへのアクセスを提供し、この分野でのキャリアを進歩させたい人にとって貴重な資産となっています。
質問 # 223
You work for a gaming company that develops massively multiplayer online (MMO) games. You built a TensorFlow model that predicts whether players will make in-app purchases of more than $10 in the next two weeks. The model's predictions will be used to adapt each user's game experience. User data is stored in BigQuery. How should you serve your model while optimizing cost, user experience, and ease of management?
正解：C
解説：
The best option to serve the model while optimizing cost, user experience, and ease of management is to deploy the model to Vertex AI Prediction, which is a managed service that can scale up or down according to the demand and provide low latency and high availability. Vertex AI Prediction can also handle TensorFlow models natively, without requiring any additional steps or conversions. By using batch prediction, the model can process large volumes of data efficiently and periodically, without affecting the user experience. The data can be read from Cloud Bigtable, which is a scalable and performant NoSQL database that can store user data in a flexible schema. The predictions can then be pushed to Cloud SQL, which is a fully managed relational database that can store the predictions in a structured format and enable easy querying and analysis. This option also simplifies the management of the model and the data, as it leverages the existing Google Cloud services and does not require any additional infrastructure or code.
The other options are not optimal for the following reasons:
* A. Importing the model into BigQuery ML is not a good option, as it requires converting the TensorFlow model into a format that BigQuery ML can understand, which can introduce errors and reduce the performance. Moreover, BigQuery ML is not designed for serving real-time predictions, but rather for training and evaluating models using SQL queries. Reading and writing data from BigQuery and Cloud SQL can also incur additional costs and latency, as they are both relational databases that require schema definition and data transformation.
* C. Embedding the model in the mobile application is not a good option, as it increases the size and complexity of the application, and requires updating the application every time the model changes.
Moreover, it exposes the model to the users, which can pose security and privacy risks, as well as potential misuse or abuse. Additionally, it does not leverage the benefits of the cloud, such as scalability, reliability, and performance.
* D. Embedding the model in the streaming Dataflow pipeline is not a good option, as it requires building
* and maintaining a custom pipeline that can handle the model inference and data processing. This can increase the development and operational costs and complexity, as well as the potential for errors and failures. Moreover, it does not take advantage of the batch prediction feature of Vertex AI Prediction, which can optimize the resource utilization and cost efficiency.
References:
* Professional ML Engineer Exam Guide
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
* Google Cloud launches machine learning engineer certification
* Vertex AI Prediction documentation
* Cloud Bigtable documentation
* Cloud SQL documentation
質問 # 224
You are training a Resnet model on Al Platform using TPUs to visually categorize types of defects in automobile engines. You capture the training profile using the Cloud TPU profiler plugin and observe that it is highly input-bound. You want to reduce the bottleneck and speed up your model training process. Which modifications should you make to the tf .data dataset?
Choose 2 answers
正解：B、C
質問 # 225
You need to analyze user activity data from your company's mobile applications. Your team will use BigQuery for data analysis, transformation, and experimentation with ML algorithms. You need to ensure real- time ingestion of the user activity data into BigQuery. What should you do?
正解：D
解説：
The best option to ensure real-time ingestion of the user activity data into BigQuery is to run a Dataflow streaming job to ingest the data into BigQuery. Dataflow is a fully managed service that can handle both batch and stream processing of data, and can integrate seamlessly with BigQuery and other Google Cloud services.
Dataflow can also use Apache Beam as the programming model, which provides a unified and portable API for developing data pipelines. By using Dataflow, you can avoid the complexity and overhead of managing your own infrastructure, and focus on the logic and transformation of your data. Dataflow can also handle various types of data, such as structured, unstructured, or binary data, and can apply windowing, aggregation, and other operations on the data streams.
The other options are not optimal for the following reasons:
* A. Configuring Pub/Sub to stream the data into BigQuery is not a good option, as Pub/Sub is a messaging service that can publish and subscribe to data streams, but cannot perform any transformation or processing on the data. Pub/Sub can be used as a source or a sink for Dataflow, but not as a standalone solution for ingesting data into BigQuery.
* B. Running an Apache Spark streaming job on Dataproc to ingest the data into BigQuery is not a good option, as it requires setting up and managing your own cluster of virtual machines, which can increase the cost and complexity of your solution. Moreover, Apache Spark is not natively integrated with BigQuery, and requires using connectors or intermediate storage to write data to BigQuery, which can introduce latency and inefficiency.
* D. Configuring Pub/Sub and a Dataflow streaming job to ingest the data into BigQuery is not a bad option, but it is not necessary, as Dataflow can directly read data from the mobile applications without using Pub/Sub as an intermediary. Using Pub/Sub can add an extra layer of abstraction and reliability, but it can also increase the cost and complexity of your solution, and introduce some delay in the data ingestion.
:
Professional ML En gineer Exam Guide
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate Google Cloud launches machine learning engineer certification Dataflow documentation BigQuery documentation
質問 # 226
You work for a retail company that is using a regression model built with BigQuery ML to predict product sales. This model is being used to serve online predictions Recently you developed a new version of the model that uses a different architecture (custom model) Initial analysis revealed that both models are performing as expected You want to deploy the new version of the model to production and monitor the performance over the next two months You need to minimize the impact to the existing and future model users How should you deploy the model?
正解：C
解説：
Vertex AI Model Registry is a central repository where you can manage the lifecycle of your ML models 1
. You can import models from various sources, such as BigQuery ML, AutoML, or custom models, and assign them to different versions and aliases 1 . You can also deploy models to endpoints, which are resources that provide a service URL for online prediction 2 .
By importing the new model to the same Vertex AI Model Registry as a different version of the existing model, you can keep track of the model versions and compare their performance metrics 1 . You can also use aliases to label the model versions according to their readiness for production, such as default or staging 1 .
By deploying the new model to the same Vertex AI endpoint as the existing model, you can use traffic splitting to gradually shift the production traffic from the old model to the new model 2 . Traffic splitting is a feature that allows you to specify the percentage of prediction requests that each deployed model in an endpoint should handle 2 . This way, you can minimize the impact to the existing and future model users, and monitor the performance of the new model over time 2 .
The other options are not suitable for your scenario, because they either require creating a separate endpoint or a Cloud Run service, which would increase the complexity and maintenance of your deployment, or they do not allow you to use traffic splitting, which would create a sudden change in your prediction results.
References :
* Introduction to Vertex AI Model Registry | Google Cloud
* Deploy a model to an endpoint | Vertex AI | Google Cloud
質問 # 227
You created an ML pipeline with multiple input parameters. You want to investigate the tradeoffs between different parameter combinations. The parameter options are
* input dataset
* Max tree depth of the boosted tree regressor
* Optimizer learning rate
You need to compare the pipeline performance of the different parameter combinations measured in F1 score, time to train and model complexity. You want your approach to be reproducible and track all pipeline runs on the same platform. What should you do?
正解：A
解説：
The best option for investigating the tradeoffs between different parameter combinations is to create an experiment in Vertex AI Experiments, create a Vertex AI pipeline with a custom model training job as part of the pipeline, configure the pipeline's parameters to include those you are investigating, and submit multiple runs to the same experiment using different values for the parameters. This option allows you to leverage the power and flexibility of Google Cloud to compare the pipeline performance of the different parameter combinations measured in F1 score, time to train, and model complexity. Vertex AI Experiments is a service that can track and compare the results of multiple machine learning runs. Vertex AI Experiments can record the metrics, parameters, and artifacts of each run, and display them in a dashboard for easy visualization and analysis. Vertex AI Experiments can also help users optimize the hyperparameters of their models by using different search algorithms, such as grid search, random search, or Bayesian optimization1. 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. A custom model training job is a type of pipeline step that can train a custom model by using a user-provided script or container. A custom model training job can accept pipeline parameters as inputs, which can be used to control the training logic or data source. By creating an experiment in Vertex AI Experiments, creating a Vertex AI pipeline with a custom model training job as part of the pipeline, configuring the pipeline's parameters to include those you are investigating, and submitting multiple runs to the same experiment using different values for the parameters, you can create a reproducible and trackable approach to investigate the tradeoffs between different parameter combinations.
The other options are not as good as option D, for the following reasons:
* Option A: Using BigQuery ML to create a boosted tree regressor and use the hyperparameter tuning capability, configuring the hyperparameter syntax to select different input datasets, max tree depths, and optimizer learning rates, and choosing the grid search option would not be able to handle different input datasets as a hyperparameter, and would not be as flexible and scalable as using Vertex AI Experiments and Vertex AI Pipelines. BigQuery ML is a service that can create and train machine learning models by using SQL queries on BigQuery. BigQuery ML can perform hyperparameter tuning by using the ML.
FORECAST or ML.PREDICT functions, and specifying the hyperparameters option. BigQuery ML can also use different search algorithms, such as grid search, random search, or Bayesian optimization, to find the optimal hyperparameters. However, BigQuery ML can only tune the hyperparameters that are related to the model architecture or training process, such as max tree depth or learning rate. BigQuery ML cannot tune the hyperparameters that are related to the data source, such as input dataset. Moreover, BigQuery ML is not designed to work with Vertex AI Experiments or Vertex AI Pipelines, which can provide more features and flexibility for tracking and orchestrating machine learning workflows2.
* Option B: Creating a Vertex AI pipeline with a custom model training job as part of the pipeline, configuring the pipeline's parameters to include those you are investigating, and using the Bayesian optimization method with F1 score as the target to maximize in the custom training step would not be able to track and compare the results of multiple runs, and would require more skills and steps than using Vertex AI Experiments and Vertex AI Pipelines. 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. A custom model training job is a type of pipeline step that can train a custom model by using a user-provided script or container. A custom model training job can accept pipeline parameters as inputs, which can be used to control the training logic or data source. However, using the Bayesian optimization method with F1 score as the target to maximize in the custom training step would require writing code, implementing the optimization algorithm, and defining the objective function. Moreover, this option would not be able to track and compare the results of multiple runs, as Vertex AI Pipelines does not have a built-in feature for recording and displaying the metrics, parameters, and artifacts of each run3.
* Option C: Creating a Vertex AI Workbench notebook for each of the different input datasets, running different local training jobs with different combinations of the max tree depth and optimizer learning rate parameters, and appending the results to a BigQuery table would not be able to track and compare the results of multiple runs on the same platform, and would require more skills and steps than using Vertex AI Experiments and Vertex AI Pipelines. Vertex AI Workbench is a service that provides an integrated development environment for data science and machine learning. Vertex AI Workbench allows users to create and run Jupyter notebooks on Google Cloud, and access various tools and libraries for data analysis and machine learning. However, creating a Vertex AI Workbench notebook for each of the different input datasets, running different local training jobs with different combinations of the max tree depth and optimizer learning rate parameters, and appending the results to a BigQuery table would require creating multiple notebooks, writing code, setting up local environments, connecting to BigQuery, loading and preprocessing the data, training and evaluating the model, and writing the results to a BigQuery table. Moreover, this option would not be able to track and compare the results of multiple runs on the same platform, as BigQuery is a separate service from Vertex AI Workbench, and does not have a dashboard for visualizing and analyzing the metrics, parameters, and artifacts of each run4.
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 1: Architecting low-code ML solutions, 1.1 Developing ML models by using BigQuery ML
* Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 3: Data Engineering for ML, Section 3.2: BigQuery for ML
* Vertex AI Experiments
* Vertex AI Pipelines
* BigQuery ML
* Vertex AI Workbench
質問 # 228
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