Authentic Google Certification Exam Professional-Machine-Learning-Engineer Questions Answers

This option is the best way to architect the workflow, as it allows you to use event-driven and serverless components to automate the ML training process. Cloud Storage triggers are a feature that allows you to send notifications to a Pub/Sub topic when an object is created, deleted, or updated in a storage bucket. Pub/Sub is a service that allows you to publish and subscribe to messages on various topics. Pub/Sub-triggered Cloud Functions are a type of Cloud Functions that are invoked when a message is published to a specific Pub/Sub topic. Cloud Functions are a serverless platform that allows you to run code in response to events. By using these components, you can create a workflow that starts the training job on a GKE cluster as soon as a new file is available in the Cloud Storage bucket, without having to manage any servers or poll for changes. The other options are not as efficient or scalable as this option. Dataflow is a service that allows you to create and run data processing pipelines, but it is not designed to trigger ML training jobs on GKE. App Engine is a service that allows you to build and deploy web applications, but it is not suitable for polling Cloud Storage for new files, as it may incur unnecessary costs and latency. Cloud Scheduler is a service that allows you to schedule jobs at regular intervals, but it is not ideal for triggering ML training jobs based on data availability, as it may miss some files or run unnecessary jobs. References:

• Cloud Storage triggers documentation
• Pub/Sub documentation
• Pub/Sub-triggered Cloud Functions documentation
• Cloud Functions documentation
• Kubeflow Pipelines documentation

 Before building an insurance approval model, an ML engineer should consider the factors of traceability, reproducibility, and explainability, as these are important aspects of responsible AI and fairness in a regulated domain. Traceability is the ability to track the provenance and lineage of the data, models, and decisions throughout the ML lifecycle. It helps to ensure the quality, reliability, and accountability of the ML system, and to comply with the regulatory and ethical standards. Reproducibility is the ability to recreate the same results and outcomes using the same data, models, and parameters. It helps to verify the validity, consistency, and robustness of the ML system, and to debug and improve the performance. Explainability is the ability to understand and interpret the logic, behavior, and outcomes of the ML system. It helps to increase the transparency, trust, and confidence of the ML system, and to identify and mitigate any potential biases, errors, or risks. The other options are not as relevant or comprehensive as this option. Redaction is the process of removing sensitive or confidential information from the data or documents, but it is not a factor that the ML engineer should consider before building the model, as it is more related to the data preparation and protection. Federated learning is a technique that allows training ML models on decentralized data without transferring the data to a central server, but it is not a factor that the ML engineer should consider before building the model, as it is more related to the model architecture and privacy preservation. Differential privacy is a method that adds noise to the data or the model outputs to protect the individual privacy of the data subjects, but it is not a factor that the ML engineer should consider before building the model, as it is more related to the model evaluation and deployment. References:

• Responsible AI documentation
• Traceability documentation
• Reproducibility documentation
• Explainability documentation

The best option for building an ML model to predict customer purchase behavior in BigQuery ML is to use the transform clause with the ML.ONE_HOT_ENCODER function on the categorical features at model creation and select the categorical and non-categorical features. This option allows you to encode the categorical features as one-hot vectors, which are binary vectors that have only one non-zero element. One-hot encoding is a common technique for handling categorical features in ML models, as it can reduce the dimensionality and sparsity of the data, and avoid the ordinality problem that arises when using numerical labels for categorical values1. The transform clause is a feature of BigQuery ML that lets you apply SQL expressions to transform the input data at model creation time. The transform clause can perform feature engineering, such as one-hot encoding, on the fly, without requiring you to create and store a new table with the transformed data2. By using the transform clause with the ML.ONE_HOT_ENCODER function, you can create and train an ML model in BigQuery ML with a single SQL statement, and export it to Cloud Storage for online prediction.

The other options are not as good as option A, for the following reasons:

• Option B: Using the ML.ONE_HOT_ENCODER function on the categorical features, and selecting the encoded categorical features and non-categorical features as inputs to create your model, would require more steps and storage than using the transform clause. The ML.ONE_HOT_ENCODER function is a BigQuery ML function that returns a one-hot encoded vector for a given categorical value. However, using this function alone would not apply the one-hot encoding to the input data at model creation time. You would need to create a new table with the encoded features, and use that table as the input to create your model. This would incur additional storage costs and reduce the performance of the queries.
• Option C: Using the create model statement and selecting the categorical and non-categorical features, would not handle the categorical features properly and could result in a poor model performance. The create model statement is a BigQuery ML statement that creates and trains an ML model from a SQL query. However, if the input data contains categorical features, you need to encode them as one-hot vectors or use the category_count option to specify the number of categories for each feature. Otherwise, BigQuery ML would treat the categorical features as numerical values, which can introduce bias and noise into the model3.
• Option D: Using the ML.ONE_HOT_ENCODER function on the categorical features, and selecting the encoded categorical features and non-categorical features as inputs to create your model, is the same as option B, and has the same drawbacks.

References:

• Preparing for Google Cloud Certification: Machine Learning Engineer, Course 2: Data Engineering for ML on Google Cloud, Week 2: Feature Engineering
• Google Cloud Professional Machine Learning Engineer Exam Guide, Section 1: Architecting low-code ML solutions, 1.1 Developing ML models by using BigQuery ML
• Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 3: Data Engineering for ML, Section 3.2: BigQuery for ML
• One-hot encoding
• Using the TRANSFORM clause for feature engineering
• Creating a model
• ML.ONE_HOT_ENCODER function