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• Option A is incorrect because training a time-series model to predict the machines’ performance values, and configuring an alert if a machine’s actual performance values significantly differ from the predicted performance values, is not the best way to build a predictive maintenance solution that uses monitoring data from the VMs to detect potential failures and then alerts the service desk team. This option assumes that the performance values follow a predictable pattern, which may not be the case for complex systems. Moreover, this option does not use any historical incident data, which may contain useful information for identifying failures. Furthermore, this option does not involve any model evaluation or validation, which are essential steps for ensuring the quality and reliability of the model.
• Option B is correct because implementing a simple heuristic (e.g., based on z-score) to label the machines’ historical performance data, and training a model to predict anomalies based on this labeled dataset, is a reasonable way to build a predictive maintenance solution that uses monitoring data from the VMs to detect potential failures and then alerts the service desk team. This option uses a simple and fast method to label the historical performance data, which is necessary for supervised learning. A z-score is a measure of how many standard deviations a value is away from the mean of a distribution1. By using a z-score, we can label the performance values that are unusually high or low as anomalies, which may indicate failures. Then, we can train a model to learn the patterns of normal and anomalous performance values, and use it to predict anomalies on new data. We can also evaluate and validate the model using metrics such as precision, recall, or F1-score, and compare it with other models or methods.
• Option C is incorrect because developing a simple heuristic (e.g., based on z-score) to label the machines’ historical performance data, and testing this heuristic in a production environment, is not a safe way to build a predictive maintenance solution that uses monitoring data from the VMs to detect potential failures and then alerts the service desk team. This option does not involve any model training or evaluation, which are essential steps for ensuring the quality and reliability of the solution. Moreover, this option does not test the heuristic on a separate dataset, such as a validation or test set, before deploying it to production, which may lead to errors or failures in the production environment.
• Option D is incorrect because hiring a team of qualified analysts to review and label the machines’ historical performance data, and training a model based on this manually labeled dataset, is not a feasible way to build a predictive maintenance solution that uses monitoring data from the VMs to detect potential failures and then alerts the service desk team. This option may produce high-quality labels, but it is also costly, time-consuming, and prone to human errors or biases. Moreover, this option may not scale well with large or complex datasets, which may require more analysts or more time to label.

References:

• Z-score
• [Predictive maintenance]
• [Anomaly detection]
• [Time-series analysis]
• [Model evaluation]

According to the official exam guide1, one of the skills assessed in the exam is to “design, build, and productionalize ML models to solve business challenges using Google Cloud technologies”. Dataflow2 is a fully managed, fast, and easy-to-use service for running Apache Spark and Apache Hadoop clusters on Google Cloud. Dataflow supports both batch and streaming data processing pipelines. However, if your use case requires real-time inference, you need to ensure that the data preprocessing logic is applied consistently between training and serving. One way to achieve this is to refactor the transformation code in the batch data pipeline so that it can be used outside of the pipeline, and use the same code in the endpoint. This way, you can avoid data skew and drift issues that might arise from using different preprocessing methods for training and serving. Therefore, option B is the best way to ensure the data preprocessing logic is applied consistently between training and serving. The other options are not relevant or optimal for this scenario. References:

• Professional ML Engineer Exam Guide
• Dataflow
• Google Professional Machine Learning Certification Exam 2023
• Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

 AutoML Natural Language is a service that allows users to create custom natural language models using their own data and labels. It supports various natural language tasks, such as text classification, entity extraction, and sentiment analysis. AutoML Natural Language can be used to build a model to classify incoming calls by product, as it can extract custom entities from the transcribed calls and assign them to predefined categories. AutoML Natural Language also minimizes data preprocessing and development time, as it handles the data preparation, model training, and evaluation automatically. The other options are not as suitable for this scenario. AI Platform Training built-in algorithms are a set of pre-defined algorithms that can be used to train ML models on AI Platform, but they do not support natural language processing tasks. Cloud Natural Language API is a pre-trained service that provides natural language understanding capabilities, such as sentiment analysis, entity analysis, syntax analysis, and content classification. However, it does not support custom entities or categories, and may not recognize the product names from the calls. Building a custom model to identify the product keywords and then running them through a classification algorithm would require more data preprocessing and development time, as well as more coding and testing. References:

• AutoML Natural Language documentation
• AI Platform Training built-in algorithms documentation
• Cloud Natural Language API documentation

Cloud Data Fusion is a fully managed, cloud-native data integration service that helps users efficiently build and manage ETL/ELT data pipelines. It provides a graphical interface to increase time efficiency and reduce complexity, and allows users to easily create and explore data pipelines using a code-free, point and click visual interface. Cloud Data Fusion also supports a broad range of data sources and formats, including on-premises data marts, and ensures data quality and security by using built-in transformation capabilities and Cloud Data Loss Prevention. Cloud Data Fusion lowers the total cost of ownership by handling performance, scalability, availability, security, and compliance needs automatically. References:

• Cloud Data Fusion documentation
• Cloud Data Fusion overview