高通過率的Professional-Machine-Learning-Engineer考古題|第一次嘗試輕鬆學習並通過考試，優秀的Professional-Machine-Learning-Engineer：Google Professional Machine Learning Engineer

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Professional-Machine-Learning-Engineer考古題, Professional-Machine-Learning-Engineer參考資料, Professional-Machine-Learning-Engineer題庫資料, 新版Professional-Machine-Learning-Engineer題庫上線, Professional-Machine-Learning-Engineer資訊

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Google專業機器學習工程師考試包括多項選擇題和實踐考試，要求考生使用Google Cloud的機器學習技術解決現實世界的問題。通過此考試表明候選人具備在Google Cloud上設計、構建和部署生產級ML模型所需的知識和技能，並可信任於專業環境中領導機器學習項目。

要成為 Google 專業機器學習工程師，候選人必須深入了解機器學習概念，包括監督和非監督學習、深度學習和強化學習，以及使用 Google Cloud 的機器學習工具（如TensorFlow、Cloud ML Engine 和 BigQuery）的經驗。他們還必須對軟件工程原則（如版本控制、測試和部署）有深入了解，並能夠將這些原則應用於機器學習模型。

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Google 專業機器學習工程師考試是由 Google Cloud 提供的認證考試，專為展示在設計、構建和部署可擴展機器學習模型方面具有精通水平的專業人士而設計。該考試旨在評估候選人使用 Google Cloud 的機器學習技術開發和部署生產級 ML 模型的能力，以及優化和維護它們以確保它們的可靠性、準確性和可擴展性。

最新的 Google Cloud Certified Professional-Machine-Learning-Engineer 免費考試真題 (Q324-Q329):

問題 #324
You work for a pharmaceutical company based in Canad
a. Your team developed a BigQuery ML model to predict the number of flu infections for the next month in Canada Weather data is published weekly and flu infection statistics are published monthly. You need to configure a model retraining policy that minimizes cost What should you do?

  • A. Download the weather and flu data each week Configure Cloud Scheduler to execute a Vertex Al pipeline to retrain the model every month.
  • B. Download the weather and flu data each week Configure Cloud Scheduler to execute a Vertex Al pipeline to retrain the model weekly.
  • C. Download the weather and flu data each month Configure Cloud Scheduler to execute a Vertex Al pipeline to retrain the model monthly.
  • D. Download the weather data each week, and download the flu data each month Deploy the model to a Vertex Al endpoint with feature drift monitoring. and retrain the model if a monitoring alert is detected.

答案：D

解題說明：
To configure a model retraining policy that minimizes cost, you should follow these steps:
Download the weather data each week, and download the flu data each month. This way, you can keep your data up to date with the latest information available, without downloading unnecessary or redundant data.
Deploy the model to a Vertex AI endpoint with feature drift monitoring. This feature allows you to detect when the distribution of the input data changes significantly from the training data, which could affect the model performance1.
Retrain the model if a monitoring alert is detected. This way, you can update your model only when needed, instead of retraining it on a fixed schedule, which could incur more cost and time.
Reference:
1: Monitor models for feature drift | Vertex AI | Google Cloud


問題 #325
You work for a textile manufacturer and have been asked to build a model to detect and classify fabric defects. You trained a machine learning model with high recall based on high resolution images taken at the end of the production line. You want quality control inspectors to gain trust in your model. Which technique should you use to understand the rationale of your classifier?

  • A. Use k-means clustering to group similar images together, and calculate the Davies-Bouldin index to evaluate the separation between clusters.
  • B. Use the Integrated Gradients method to efficiently compute feature attributions for each predicted image.
  • C. Use PCA (Principal Component Analysis) to reduce the original feature set to a smaller set of easily understood features.
  • D. Use K-fold cross validation to understand how the model performs on different test datasets.

答案：B

解題說明：
A is not correct because K-fold cross validation offers no explanation on the predictions made by the model.
B is correct because it identifies the pixel of the input image that leads to the classification of the image itself.
C is not correct because PCA simplifies higher dimensional datasets but offers no added benefit to the scenario.
D is not correct because clustering images does not provide any insight into why the classification model made the predictions that it did.
https://cloud.google.com/ml-engine/docs/ai-explanations/overview
https://storage.googleapis.com/cloud-ai-whitepapers/AI%20Explainability%20Whitepaper.pdf


問題 #326
Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers1 account balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?

  • A. 1. Build a notification system on Firebase
    2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when the average of all account balance predictions drops below the $25 threshold
  • B. 1. Create a Pub/Sub topic for each user
    2 Deploy a Cloud Function that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.
  • C. 1. Create a Pub/Sub topic for each user
    2. Deploy an application on the App Engine standard environment that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold
  • D. 1 Build a notification system on Firebase
    2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user's account balance will drop below the $25 threshold

答案：C


問題 #327
You work for an online retailer. Your company has a few thousand short lifecycle products. Your company has five years of sales data stored in BigQuery. You have been asked to build a model that will make monthly sales predictions for each product. You want to use a solution that can be implemented quickly with minimal effort. What should you do?

  • A. Use TensorFlow on Vertex Al Training to build a custom model.
  • B. Use Vertex Al Forecast to build a NN-based model.
  • C. Use Prophet on Vertex Al Training to build a custom model.
  • D. Use BigQuery ML to build a statistical AR1MA_PLUS model.

答案：B


問題 #328
You recently developed a deep learning model using Keras, and now you are experimenting with different training strategies. First, you trained the model using a single GPU, but the training process was too slow.
Next, you distributed the training across 4 GPUs using tf.distribute.MirroredStrategy (with no other changes), but you did not observe a decrease in training time. What should you do?

  • A. Distribute the dataset with tf.distribute.Strategy.experimental_distribute_dataset
  • B. Create a custom training loop.
  • C. Use a TPU with tf.distribute.TPUStrategy.
  • D. Increase the batch size.

答案：D

解題說明：
* Option A is incorrect because distributing the dataset with tf.distribute.Strategy.
experimental_distribute_dataset is not the most effective way to decrease the training time. This method allows you to distribute your dataset across multiple devices or machines, by creating a tf.data.Dataset instance that can be iterated over in parallel1. However, this option may not improve the training time significantly, as it does not change the amount of data or computation that each device or machine has to process. Moreover, this option may introduce additional overhead or complexity, as it requires you to handle the data sharding, replication, and synchronization across the devices or machines1.
* Option B is incorrect because creating a custom training loop is not the easiest way to decrease the training time. A custom training loop is a way to implement your own logic for training your model, by using low-level TensorFlow APIs, such as tf.GradientTape, tf.Variable, or tf.function2. A custom training loop may give you more flexibility and control over the training process, but it also requires more effort and expertise, as you have to write and debug the code for each step of the training loop, such as computing the gradients, applying the optimizer, or updating the metrics2. Moreover, a custom training loop may not improve the training time significantly, as it does not change the amount of data or computation that each device or machine has to process.
* Option C is incorrect because using a TPU with tf.distribute.TPUStrategy is not a valid way to decrease the training time. A TPU (Tensor Processing Unit) is a custom hardware accelerator designed for high- performance ML workloads3. A tf.distribute.TPUStrategy is a distribution strategy that allows you to distribute your training across multiple TPUs, by creating a tf.distribute.TPUStrategy instance that can be used with high-level TensorFlow APIs, such as Keras4. However, this option is not feasible, as Vertex AI Training does not support TPUs as accelerators for custom training jobs5. Moreover, this option may require significant code changes, as TPUs have different requirements and limitations than GPUs.
* Option D is correct because increasing the batch size is the best way to decrease the training time. The batch size is a hyperparameter that determines how many samples of data are processed in each iteration of the training loop. Increasing the batch size may reduce the training time, as it reduces the number of iterations needed to train the model, and it allows each device or machine to process more data in parallel. Increasing the batch size is also easy to implement, as it only requires changing a single hyperparameter. However, increasing the batch size may also affect the convergence and the accuracy of the model, so it is important to find the optimal batch size that balances the trade-off between the training time and the model performance.
References:
* tf.distribute.Strategy.experimental_distribute_dataset
* Custom training loop
* TPU overview
* tf.distribute.TPUStrategy
* Vertex AI Training accelerators
* [TPU programming model]
* [Batch size and learning rate]
* [Keras overview]
* [tf.distribute.MirroredStrategy]
* [Vertex AI Training overview]
* [TensorFlow overview]


問題 #329
......

Professional-Machine-Learning-Engineer參考資料: https://www.kaoguti.com/Professional-Machine-Learning-Engineer_exam-pdf.html

順便提一下，可以從雲存儲中下載KaoGuTi Professional-Machine-Learning-Engineer考試題庫的完整版：https://drive.google.com/open?id=1y1Xk-vbDFgTRrjKb5igBo-WioCAHXI15

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