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Professional-Machine-Learning-Engineer試験の準備方法｜素晴らしいProfessional-Machine-Learning-Engineer受験資料更新版試験｜高品質なGoogle Professional Machine Learning Engineer学習資料

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Google Professional-Machine-Learning-Engineer 認定試験の出題範囲：

トピック 出題範囲
トピック 1
  • Training a model as a job in different environments
  • Constructing and testing of parameterized pipeline definition in SDK
トピック 2
  • Batching and streaming data pipelines at scale
  • Managing incorrect results
  • Identifying nonML solutions
トピック 3
  • Organization and tracking experiments and pipeline runs
  • Hooking models into existing CI
  • CD deployment system
トピック 4
  • Defining the input (features) and predicted output format
  • Modeling techniques given interpretability requirements
トピック 5
  • Choose appropriate Google Cloud hardware components
  • Privacy implications of data usage
  • Identifying potential regulatory issues
トピック 6
  • Automation of data preparation and model training
  • deployment
  • Determination of when a model is deemed unsuccessful

Google Professional Machine Learning Engineer 認定 Professional-Machine-Learning-Engineer 試験問題 (Q24-Q29):

質問 # 24
You have successfully deployed to production a large and complex TensorFlow model trained on tabular dat a. You want to predict the lifetime value (LTV) field for each subscription stored in the BigQuery table named subscription. subscriptionPurchase in the project named my-fortune500-company-project.
You have organized all your training code, from preprocessing data from the BigQuery table up to deploying the validated model to the Vertex AI endpoint, into a TensorFlow Extended (TFX) pipeline. You want to prevent prediction drift, i.e., a situation when a feature data distribution in production changes significantly over time. What should you do?

  • A. Add a model monitoring job where 10% of incoming predictions are sampled every hour.
  • B. Add a model monitoring job where 10% of incoming predictions are sampled 24 hours.
  • C. Add a model monitoring job where 90% of incoming predictions are sampled 24 hours.
  • D. Implement continuous retraining of the model daily using Vertex AI Pipelines.

正解：C
質問 # 25
You work as an ML engineer at a social media company, and you are developing a visual filter for users' profile photos. This requires you to train an ML model to detect bounding boxes around human faces. You want to use this filter in your company's iOS-based mobile phone application. You want to minimize code development and want the model to be optimized for inference on mobile phones. What should you do?

  • A. Train a custom TensorFlow model and convert it to TensorFlow Lite (TFLite).
  • B. Train a model using AutoML Vision and use the "export for TensorFlow.js" option.
  • C. Train a model using AutoML Vision and use the "export for Core ML" option.
  • D. Train a model using AutoML Vision and use the "export for Coral" option.

正解：C
質問 # 26
A data scientist uses an Amazon SageMaker notebook instance to conduct data exploration and analysis. This requires certain Python packages that are not natively available on Amazon SageMaker to be installed on the notebook instance.
How can a machine learning specialist ensure that required packages are automatically available on the notebook instance for the data scientist to use?

  • A. Use the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook.
  • B. Create a Jupyter notebook file (.ipynb) with cells containing the package installation commands to execute and place the file under the /etc/init directory of each Amazon SageMaker notebook instance.
  • C. Install AWS Systems Manager Agent on the underlying Amazon EC2 instance and use Systems Manager Automation to execute the package installation commands.
  • D. Create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance.

正解：B 解説：
Explanation
Explanation/Reference: https://towardsdatascience.com/automating-aws-sagemaker-notebooks-2dec62bc2c84
質問 # 27
A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet of a corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook instance's Amazon EBS volume, and needs to take a snapshot of that EBS volume. However, the ML Specialist cannot find the Amazon SageMaker notebook instance's EBS volume or Amazon EC2 instance within the VPC.
Why is the ML Specialist not seeing the instance visible in the VPC?

  • A. Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.
  • B. Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.
  • C. Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs.
  • D. Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts.

正解：A 解説：
Explanation/Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/gs-setup-working-env.html
質問 # 28
You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure. Which additional readiness check should you recommend to the team?

  • A. Ensure that training is reproducible
  • B. Ensure that feature expectations are captured in the schema
  • C. Ensure that model performance is monitored
  • D. Ensure that all hyperparameters are tuned

正解：A 解説：
https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/aad9f93b86b7addfea4c419b9100c6cdd26cacea.pdf
質問 # 29
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