Google Professional-Machine-Learning-Engineer Exam With Confidence Using Practice Dumps

Option A is incorrect because implementing continuous retraining of the model daily using Vertex AI Pipelines is not the most efficient way to prevent prediction drift. Vertex AI Pipelines is a service that allows you to create and run scalable and portable ML pipelines on Google Cloud1. You can use Vertex AI Pipelines to retrain your model daily using the latest data from the BigQuery table. However, this option may be unnecessary or wasteful, as the data distribution may not change significantly every day, and retraining the model may consume a lot of resources and time. Moreover, this option does not monitor the model performance or detect the prediction drift, which are essential steps for ensuring the quality and reliability of the model.

Option B is correct because adding a model monitoring job where 10% of incoming predictions are sampled 24 hours is the best way to prevent prediction drift. Model monitoring is a service that allows you to track the performance and health of your deployed models over time2. You can use model monitoring to sample a fraction of the incoming predictions and compare them with the ground truth labels, which can be obtained from the BigQuery table or other sources. You can also use model monitoring to compute various metrics, such as accuracy, precision, recall, or F1-score, and set thresholds or alerts for them. By using model monitoring, you can detect and diagnose the prediction drift, and decide when to retrain or update your model. Sampling 10% of the incoming predictions every 24 hours is a reasonable choice, as it balances the trade-off between the accuracy and the cost of the monitoring job.

Option C is incorrect because adding a model monitoring job where 90% of incoming predictions are sampled 24 hours is not a optimal way to prevent prediction drift. This option has the same advantages as option B, as it uses model monitoring to track the performance and health of the deployed model. However, this option is not cost-effective, as it samples a very large fraction of the incoming predictions, which may incur a lot of storage and processing costs. Moreover, this option may not improve the accuracy of the monitoring job significantly, as sampling 10% of the incoming predictions may already provide a representative sample of the data distribution.

Option D is incorrect because adding a model monitoring job where 10% of incoming predictions are sampled every hour is not a necessary way to prevent prediction drift. This option also has the same advantages as option B, as it uses model monitoring to track the performance and health of the deployed model. However, this option may be excessive, as it samples the incoming predictions too frequently, which may not reflect the actual changes in the data distribution. Moreover, this option may incur more storage and processing costs than option B, as it generates more samples and metrics.

References:

Vertex AI Pipelines documentation

Model monitoring documentation

[Prediction drift]

[TensorFlow Extended documentation]

[BigQuery documentation]

[Vertex AI documentation]

Option A is incorrect because Vertex AI Pipelines and App Engine do not meet all the requirements of the system. Vertex AI Pipelines is a service that allows you to create, run, and manage ML workflows using TensorFlow Extended (TFX) components or custom components1. App Engine is a service that allows you to build and deploy scalable web applications using standard or flexible environments2. However, App Engine does not support Docker containers in the standard environment, and does not provide a dedicated service for online prediction and monitoring of ML models3.

Option B is correct because Vertex AI Pipelines, Vertex AI Prediction, and Vertex AI Model Monitoring meet all the requirements of the system. Vertex AI Prediction is a service that allows you to deploy and serve ML models for online or batch prediction, with support for autoscaling and custom containers4. Vertex AI Model Monitoring is a service that allows you to monitor the performance and fairness of your deployed models, and get alerts for any issues or anomalies5.

Option C is incorrect because Cloud Composer, BigQuery ML, and Vertex AI Prediction do not meet all the requirements of the system. Cloud Composer is a service that allows you to create, schedule, and manage workflows using Apache Airflow. BigQuery ML is a service that allows you to create and use ML models within BigQuery using SQL queries. However, BigQuery ML does not support custom containers, and Vertex AI Prediction does not support scheduled model retraining or model monitoring.

Option D is incorrect because Cloud Composer, Vertex AI Training with custom containers, and App Engine do not meet all the requirements of the system. Vertex AI Training is a service that allows you to train ML models using built-in algorithms or custom containers. However, Vertex AI Training does not support online prediction or model monitoring, and App Engine does not support Docker containers in the standard environment or online prediction and monitoring of ML models3.

References:

Vertex AI Pipelines overview

App Engine overview

Choosing an App Engine environment

Vertex AI Prediction overview

Vertex AI Model Monitoring overview

[Cloud Composer overview]

[BigQuery ML overview]

[BigQuery ML limitations]

[Vertex AI Training overview]

 Comparing the training and evaluation losses of the current run is a good way to check if the model is overfitting or underfitting. If the losses are similar, it means that the model is generalizing well and can be deployed to a Vertex AI endpoint. Vertex AI endpoint is a service that allows you to serve your ML models online and scale them automatically. By using a training/serving skew threshold model monitoring, you can detect if there is a significant difference between the data used for training and the data used for serving. This can indicate that the model is becoming stale or inaccurate over time. When the model monitoring threshold is triggered, it means that the model needs to be retrained with the latest data. By redeploying the pipeline, you can automate the retraining process and update the model with the new data. This way, you can minimize the cost of retraining and ensure that your model is always up-to-date and accurate. References:

Vertex AI documentation

Vertex AI endpoint documentation

Model monitoring documentation

Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate