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Google Professional Machine Learning Engineer認定試験は、Google Cloudプラットフォームで機械学習モデルとシステムの設計と実装に習熟しようとする個人にとって、非常に人気の高い資格です。この認定は、機械学習の概念と機械学習モデルの構築と展開における実践的な経験を持つ強固な基盤を持つ専門家向けに設計されています。

認定プログラムは、生産環境での機械学習モデルの開発と展開に関与する専門家を対象としています。データサイエンティスト、機械学習エンジニア、ソフトウェア開発者、および機械学習の分野で知識とスキルを向上させたい他のIT専門家に最適です。認定プログラムには、監視された学習、監視されていない学習、深い学習、ニューラルネットワークなど、機械学習のさまざまな側面をカバーする包括的なカリキュラムが含まれています。

Google Professional Machine Learning Engineer Examは、機械学習の分野で非常に求められている認定です。 Google Cloud Platform Technologiesを使用して、機械学習モデルとワークフローの設計と実装に豊富な経験を持つ専門家を対象としています。この試験では、データの前処理、機能エンジニアリング、モデル選択、ハイパーパラメーターチューニング、モデル評価、展開など、幅広いトピックをカバーしています。この試験に合格すると、候補者は、Google Cloudプラットフォームに生産グレードの機械学習モデルを設計、開発、展開するために必要なスキルと知識を持っていることが示されています。

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Google Professional Machine Learning Engineer 認定 Professional-Machine-Learning-Engineer 試験問題 (Q154-Q159):

質問 # 154
Your team has a model deployed to a Vertex Al endpoint You have created a Vertex Al pipeline that automates the model training process and is triggered by a Cloud Function. You need to prioritize keeping the model up-to-date, but also minimize retraining costs. How should you configure retraining'?

• A. Configure a Cloud Scheduler job that calls the Cloud Function at a predetermined frequency that fits your team's budget.
• B. Configure Pub/Sub to call the Cloud Function when a sufficient amount of new data becomes available.
• C. Enable model monitoring on the Vertex Al endpoint Configure Pub/Sub to call the Cloud Function when anomalies are detected.
• D. Enable model monitoring on the Vertex Al endpoint Configure Pub/Sub to call the Cloud Function when feature drift is detected.

正解：D

解説：
According to the official exam guide1, one of the skills assessed in the exam is to "configure and optimize model monitoring jobs". Vertex AI Model Monitoring documentation states that "model monitoring helps you detect when your model's performance degrades over time due to changes in the data that your model receives or returns" and that "you can configure model monitoring to send notifications to Pub/Sub when it detects anomalies or drift in your model's predictions"2. Therefore, enabling model monitoring on the Vertex AI endpoint and configuring Pub/Sub to call the Cloud Function when feature drift is detected would help you keep the model up-to-date and minimize retraining costs. The other options are not relevant or optimal for this scenario. Reference:
Professional ML Engineer Exam Guide
Vertex AI Model Monitoring
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

質問 # 155
You need to execute a batch prediction on 100 million records in a BigQuery table with a custom TensorFlow DNN regressor model, and then store the predicted results in a BigQuery table. You want to minimize the effort required to build this inference pipeline. What should you do?

• A. Use the TensorFlow BigQuery reader to load the data, and use the BigQuery API to write the results to BigQuery.
• B. Load the TensorFlow SavedModel in a Dataflow pipeline. Use the BigQuery I/O connector with a custom function to perform the inference within the pipeline, and write the results to BigQuery.
• C. Create a Dataflow pipeline to convert the data in BigQuery to TFRecords. Run a batch inference on Vertex AI Prediction, and write the results to BigQuery.
• D. Import the TensorFlow model with BigQuery ML, and run the ml.predict function.

正解：D

解説：
Option A is correct because importing the TensorFlow model with BigQuery ML, and running the ml.predict function is the easiest way to execute a batch prediction on a large BigQuery table with a custom TensorFlow model, and store the predicted results in another BigQuery table. BigQuery ML allows you to import TensorFlow models that are stored in Cloud Storage, and use them for prediction with SQL queries1. The ml.predict function returns a table with the predicted values, which can be saved to another BigQuery table2.
Option B is incorrect because using the TensorFlow BigQuery reader to load the data, and using the BigQuery API to write the results to BigQuery requires more effort to build the inference pipeline than option A. The TensorFlow BigQuery reader is a way to read data from BigQuery into TensorFlow datasets, which can be used for training or prediction3. However, this option also requires writing code to load the TensorFlow model, run the prediction, and use the BigQuery API to write the results back to BigQuery4.
Option C is incorrect because creating a Dataflow pipeline to convert the data in BigQuery to TFRecords, running a batch inference on Vertex AI Prediction, and writing the results to BigQuery requires more effort to build the inference pipeline than option A. Dataflow is a service for creating and running data processing pipelines, such as ETL (extract, transform, load) or batch processing5. Vertex AI Prediction is a service for deploying and serving ML models for online or batch prediction. However, this option also requires writing code to create the Dataflow pipeline, convert the data to TFRecords, run the batch inference, and write the results to BigQuery.
Option D is incorrect because loading the TensorFlow SavedModel in a Dataflow pipeline, using the BigQuery I/O connector with a custom function to perform the inference within the pipeline, and writing the results to BigQuery requires more effort to build the inference pipeline than option A. The BigQuery I/O connector is a way to read and write data from BigQuery within a Dataflow pipeline. However, this option also requires writing code to load the TensorFlow SavedModel, create the custom function for inference, and write the results to BigQuery.
Reference:
Importing models into BigQuery ML
Using imported models for prediction
TensorFlow BigQuery reader
BigQuery API
Dataflow overview
[Vertex AI Prediction overview]
[Batch prediction with Dataflow]
[BigQuery I/O connector]
[Using TensorFlow models in Dataflow]

質問 # 156
You are training an LSTM-based model on Al Platform to summarize text using the following job submission script:

You want to ensure that training time is minimized without significantly compromising the accuracy of your model. What should you do?

• A. Modify the batch size' parameter
• B. Modify the 'epochs' parameter
• C. Modify the 'learning rate' parameter
• D. Modify the 'scale-tier' parameter

正解：B

質問 # 157
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：D

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 # 158
You recently joined an enterprise-scale company that has thousands of datasets. You know that there are accurate descriptions for each table in BigQuery, and you are searching for the proper BigQuery table to use for a model you are building on AI Platform. How should you find the data that you need?

• A. Use Data Catalog to search the BigQuery datasets by using keywords in the table description.
• B. Maintain a lookup table in BigQuery that maps the table descriptions to the table ID. Query the lookup table to find the correct table ID for the data that you need.
• C. Tag each of your model and version resources on AI Platform with the name of the BigQuery table that was used for training.
• D. Execute a query in BigQuery to retrieve all the existing table names in your project using the INFORMATION_SCHEMA metadata tables that are native to BigQuery. Use the result o find the table that you need.

正解：C

質問 # 159
......

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