最新的最新Professional-Machine-Learning-Engineer題庫和最新的Google認證培訓 -高通過率的Google Google Professional Machine Learning Engineer

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Google Professional-Machine-Learning-Engineer 認證作為全球IT領域專家 Google 熱門認證之一，是許多大中IT企業選擇人才標準的必備條件。Google Professional-Machine-Learning-Engineer 考題由全球領先的IT認證考試中心授權，幫助考生一次性順利取得通過 Professional-Machine-Learning-Engineer 考試；否則將全額退費，這一舉動保證考生權利不受任何的損失。考生考試前需要在全球的Prometric考試中心進行報名並預約考試時間。

Google Professional-Machine-Learning-Engineer 考試大綱：

主題	簡介
主題 1	Choose appropriate Google Cloud hardware components Privacy implications of data usage Identifying potential regulatory issues
主題 2	Optimization and simplification of input pipeline for training Aligning with Google AI principles and practices
主題 3	Performance and business quality of ML model predictions Establishing continuous evaluation metrics
主題 4	Choose appropriate Google Cloud software components Assessing and communicating business impact
主題 5	Design architecture that complies with regulatory and security concerns Define business success criteria

最新Professional-Machine-Learning-Engineer題庫：Google Professional Machine Learning Engineer確定通過考試

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最新的 Google Cloud Certified Professional-Machine-Learning-Engineer 免費考試真題 (Q122-Q127):

問題 #122
You deployed an ML model into production a year ago. Every month, you collect all raw requests that were sent to your model prediction service during the previous month. You send a subset of these requests to a human labeling service to evaluate your model's performance. After a year, you notice that your model's performance sometimes degrades significantly after a month, while other times it takes several months to notice any decrease in performance. The labeling service is costly, but you also need to avoid large performance degradations. You want to determine how often you should retrain your model to maintain a high level of performance while minimizing cost. What should you do?

A. Identify temporal patterns in your model's performance over the previous year. Based on these patterns, create a schedule for sending serving data to the labeling service for the next year.
B. Train an anomaly detection model on the training dataset, and run all incoming requests through this model. If an anomaly is detected, send the most recent serving data to the labeling service.
C. Run training-serving skew detection batch jobs every few days to compare the aggregate statistics of the features in the training dataset with recent serving data. If skew is detected, send the most recent serving data to the labeling service.
D. Compare the cost of the labeling service with the lost revenue due to model performance degradation over the past year. If the lost revenue is greater than the cost of the labeling service, increase the frequency of model retraining; otherwise, decrease the model retraining frequency.

答案：B

問題 #123
A Machine Learning Specialist is developing a custom video recommendation model for an application. The dataset used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket.
The Specialist wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance.
Which approach allows the Specialist to use all the data to train the model?

A. Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatible with Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
B. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
C. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to the instance. Train on a small amount of the data to verify the training code and hyperparameters. Go back to Amazon SageMaker and train using the full dataset
D. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to train the full dataset.

答案：B

問題 #124
You have been asked to build a model using a dataset that is stored in a medium-sized (~10 GB) BigQuery table. You need to quickly determine whether this data is suitable for model development. You want to create a one-time report that includes both informative visualizations of data distributions and more sophisticated statistical analyses to share with other ML engineers on your team. You require maximum flexibility to create your report. What should you do?

A. Use the output from TensorFlow Data Validation on Dataflow to generate the report.
B. Use the Google Data Studio to create the report.
C. Use Vertex AI Workbench user-managed notebooks to generate the report.
D. Use Dataprep to create the report.

答案：A

問題 #125
You are building an ML model to detect anomalies in real-time sensor dat a. You will use Pub/Sub to handle incoming requests. You want to store the results for analytics and visualization. How should you configure the pipeline?

A. 1 = BigQuery, 2 = Al Platform, 3 = Cloud Storage
B. 1 = Dataflow, 2 - Al Platform, 3 = BigQuery
C. 1 = BigQuery, 2 = AutoML, 3 = Cloud Functions
D. 1 = DataProc, 2 = AutoML, 3 = Cloud Bigtable

答案：B

問題 #126
Your team trained and tested a DNN regression model with good results. Six months after deployment, the model is performing poorly due to a change in the distribution of the input dat a. How should you address the input differences in production?

A. Perform feature selection on the model, and retrain the model on a monthly basis with fewer features
B. Perform feature selection on the model, and retrain the model with fewer features
C. Retrain the model, and select an L2 regularization parameter with a hyperparameter tuning service
D. Create alerts to monitor for skew, and retrain the model.

答案：D

解題說明：
Data drift doesn't necessarily require feature reselection (e.g. by L2 regularization). https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning#challenges Data values skews: These skews are significant changes in the statistical properties of data, which means that data patterns are changing, and you need to trigger a retraining of the model to capture these changes. https://developers.google.com/machine-learning/guides/rules-of-ml/#rule_37_measure_trainingserving_skew

問題 #127
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