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| Section | Objectives |
|---|---|
| Topic 1: Collaborating within and across teams to manage data and models | - Collaboration between Data Scientists, Data Engineers, and ML Engineers - Data management and governance - Version control and reproducibility (e.g., DVC, MLOps) |
| Topic 2: Architecting low-code ML solutions | - Implementing BigQuery ML for basic models - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) - AutoML capabilities and implementation |
| Topic 3: Automating and orchestrating ML pipelines | - Vertex AI Pipelines (Kubeflow Pipelines) - Triggering and scheduling pipelines - CI/CD for ML systems |
| Topic 4: Scaling prototypes into ML models | - Hyperparameter tuning - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) - Training at scale (Distributed training, TPUs) |
| Topic 5: Monitoring ML solutions | - Performance monitoring and drift detection - Logging and alerting (Cloud Monitoring) - Model retraining strategies |
| Topic 6: Serving and scaling models | - Online prediction (Vertex AI Prediction) - Hardware accelerators (GPU/TPU) in serving - Model optimization (Quantization, Distillation) - Batch prediction |
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NEW QUESTION # 341
You built a custom ML model using scikit-learn. Training time is taking longer than expected. You decide to migrate your model to Vertex AI Training, and you want to improve the model's training time. What should you try out first?
Answer: C
NEW QUESTION # 342
You are the lead ML engineer for a large ecommerce company. The Data Analytics team has prepared a dataset of 50 million transaction rows, which is stored in a partitioned BigQuery table.
The team consists primarily of SQL experts with limited experience in Python or TensorFlow.
You need to build a binary classification model to predict the likelihood of a customer making a purchase in the next seven days. You need to provide daily batch predictions. You need to minimize development effort, data movement, and the overall complexity of the solution. What should you do?
Answer: C
Explanation:
BigQuery ML AutoML classification allows the SQL-focused team to train a high-quality binary classifier directly on the 50-million-row BigQuery table without moving the data or developing custom Python training code. ML.PREDICT can be integrated into existing nightly warehouse jobs to generate daily batch predictions entirely within BigQuery, minimizing operational complexity and development effort.
Reference:
https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-predict
https://docs.cloud.google.com/bigquery/docs/create-machine-learning-model
https://docs.cloud.google.com/bigquery/docs/export-model-tutorial
NEW QUESTION # 343
You are building a linear regression model on BigQuery ML to predict a customer's likelihood of purchasing your company's products. Your model uses a city name variable as a key predictive component. In order to train and serve the model, your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables. What should you do?
Answer: D
NEW QUESTION # 344
You have a demand forecasting pipeline in production that uses Dataflow to preprocess raw data prior to model training and prediction. During preprocessing, you employ Z-score normalization on data stored in BigQuery and write it back to BigQuery. New training data is added every week.
You want to make the process more efficient by minimizing computation time and manual intervention. What should you do?
Answer: D
Explanation:
BiqQuery definitely minimizes computational time for normalization. I think it would also minimize manual intervention. For data normalization in dataflow you'd have to pass in values of mean and standard deviation as a side-input. That seems more work than a simple SQL query.
NEW QUESTION # 345
You work for a bank. You have created a custom model to predict whether a loan application should be flagged for human review. The input features are stored in a BigQuery table. The model is performing well and you plan to deploy it to production. Due to compliance requirements the model must provide explanations for each prediction. You want to add this functionality to your model code with minimal effort and provide explanations that are as accurate as possible What should you do?
Answer: A
Explanation:
The best option for adding explanations to your model code with minimal effort and providing explanations that are as accurate as possible is to upload the custom model to Vertex AI Model Registry and configure feature-based attribution by using sampled Shapley with input baselines. This option allows you to leverage the power and simplicity of Vertex Explainable AI to generate feature attributions for each prediction, and understand how each feature contributes to the model output. Vertex Explainable AI is a service that can help you understand and interpret predictions made by your machine learning models, natively integrated with a number of Google's products and services. Vertex Explainable AI can provide feature-based and example- based explanations to provide better understanding of model decision making. Feature-based explanations are explanations that show how much each feature in the input influenced the prediction. Feature-based explanations can help you debug and improve model performance, build confidence in the predictions, and understand when and why things go wrong. Vertex Explainable AI supports various feature attribution methods, such as sampled Shapley, integrated gradients, and XRAI. Sampled Shapley is a feature attribution method that is based on the Shapley value, which is a concept from game theory that measures how much each player in a cooperative game contributes to the total payoff. Sampled Shapley approximates the Shapley value for each feature by sampling different subsets of features, and computing the marginal contribution of each feature to the prediction. Sampled Shapley can provide accurate and consistent feature attributions, but it can also be computationally expensive. To reduce the computation cost, you can use input baselines, which are reference inputs that are used to compare with the actual inputs. Input baselines can help you define the starting point or the default state of the features, and calculate the feature attributions relative to the input baselines. By uploading the custom model to Vertex AI Model Registry and configuring feature-based attribution by using sampled Shapley with input baselines, you can add explanations to your model code with minimal effort and provide expl anations that are as accurate as possible 1 .
The other options are not as good as option C, for the following reasons:
* Option A: Creating an AutoML tabular model by using the BigQuery data with integrated Vertex Explainable AI would require more skills and steps than uploading the custom model to Vertex AI Model Registry and configuring feature-based attribution by using sampled Shapley with input baselines. AutoML tabular is a service that can automatically build and train machine learning models for structured or tabular data. AutoML tabular can use BigQuery as the data source, and provide feature- based explanations by using integrated gradients as the feature attribution method. However, creating an AutoML tabular model by using the BigQuery data with integrated Vertex Explainable AI would require more skills and steps than uploading the custom model to Vertex AI Model Registry and configuring feature-based attribution by using sampled Shapley with input baselines. You would need to create a new AutoML tabular model, import the BigQuery data, configure the model settings, train and evaluate the model, and deploy the model. Moreover, this option would not use your existing custom model, which is already performing well, but create a new model, which may not have the same performance or behavior as your custom model 2 .
* Option B: Creating a BigQuery ML deep neural network model, and using the ML.
EXPLAIN_PREDICT method with the num_integral_steps parameter would not allow you to deploy the model to production, and could provide less accurate explanations than using sampled Shapley with input baselines. BigQuery ML is a service that can create and train machine learning models by using SQL queries on BigQuery. BigQuery ML can create a deep neural network model, which is a type of machine learning model that consists of multiple layers of neurons, and can learn complex patterns and relationships from the data. BigQuery ML can also provide feature-based explanations by using the ML.
EXPLAIN_PREDICT method, which is a SQL function that returns the feature attributions for each prediction. The ML.EXPLAIN_PREDICT method uses integrated gradients as the feature attribution method, which is a method that calculates the average gradient of the prediction output with respect to the feature values along the path from the input baseline to the input. The num_integral_steps parameter is a parameter that determines the number of steps along the path from the input baseline to the input.
However, creating a BigQuery ML deep neural network model, and using the ML.
EXPLAIN_PREDICT method with the num_integral_steps parameter would not allow you to deploy the model to production, and could provide less accurate explanations than using sampled Shapley with input baselines. BigQuery ML does not support deploying the model to Vertex AI Endpoints, which is a service that can provide low-latency predictions for individual instances. BigQuery ML only supports batch prediction, which is a service that can provide high-throughput predictions for a large batch of instances. Moreover, integrated gradients can provide less accurate and consistent explanations than sampled Shapley, as integrated gradients can be sensitive to the choice of the input baseline and the num_integral_s teps parameter 3 .
* Option D: Updating the custom serving container to include sampled Shapley-based explanations in the prediction outputs would require more skills and steps than uploading the custom model to Vertex AI Model Registry and configuring feature-based attribution by using sampled Shapley with input baselines. A custom serving container is a container image that contains the model, the dependencies, and a web server. A custom serving container can help you customize the prediction behavior of your model, and handle complex or non-standard data formats. However, updating the custom serving container to include sampled Shapley-based explanations in the prediction outputs would require more skills and steps than uploading the custom model to Vertex AI Model Registry and configuring feature- based attribution by using sampled Shapley with input baselines. You would need to write code, implement the sampled Shapley algorithm, build and test the container image, and upload and deploy the container image. Moreover, this option would not leverage the power and simplicity of Vertex Explain able AI, which can provide feature-based explanations natively integrated with Vertex AI services 4 .
References:
Preparing for Google Cloud Certification: Machine Learning Engineer , Course 3: Production ML Systems, Week 4: Evaluation Google Cloud Professional Machine Learning Engineer Exam Guide , Section 3: Scaling ML models in production, 3.3 Monitoring ML models in production Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 6: Production ML Systems, Section 6.3: Monitoring ML Models Vertex Explainable AI AutoML Tables BigQuery ML Using custom containers for prediction
NEW QUESTION # 346
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