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| Section | Weight | Objectives |
|---|---|---|
| Monitor and optimize AI solutions | 16% | - Monitor model performance, fairness, and drift - Troubleshoot and maintain production systems - Optimize cost, latency, and resource usage - Monitor data quality and pipeline health |
| Scale prototypes into AI models | 18% | - Work with foundation models and generative AI techniques - Design and run experiments - Select appropriate model architectures and frameworks - Optimize model performance and generalization |
| Automate and orchestrate ML pipelines | 18% | - Design end-to-end ML workflows - Implement CI/CD for ML systems - Automate retraining and model updates - Use Vertex AI Pipelines, TFX, and other orchestration tools |
| Collaborate to manage data and models | 16% | - Manage datasets and features in Vertex AI - Address data privacy, compliance, and governance - Organize and prepare enterprise data
|
| Architect low-code AI solutions | 12% | - Apply responsible AI principles to low-code designs - Identify use cases for low-code/no-code AI tools - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder |
| Train and deploy models | 20% | - Implement generative AI deployment patterns - Configure training jobs and environments - Deploy models for online, batch, and streaming prediction - Use Vertex AI deployment features and infrastructure |
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NEW QUESTION # 144
You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent.
Which data transformation strategy would likely improve the performance of your classifier?
Answer: B
NEW QUESTION # 145
Your team has been tasked with creating an ML solution in Google Cloud to classify support requests for one of your platforms. You analyzed the requirements and decided to use TensorFlow to build the classifier so that you have full control of the model's code, serving, and deployment. You will use Kubeflow pipelines for the ML platform. To save time, you want to build on existing resources and use managed services instead of building a completely new model. How should you build the classifier?
Answer: D
Explanation:
Transfer learning is a technique that leverages the knowledge and weights of a pre-trained model and adapts them to a new task or domain1. Transfer learning can save time and resources by avoiding training a model from scratch, and can also improve the performance and generalization of the model by using a larger and more diverse dataset2. AI Platform provides several established text classification models that can be used for transfer learning, such as BERT, ALBERT, or XLNet3. These models are based on state-of-the-art natural language processing techniques and can handle various text classification tasks, such as sentiment analysis, topic classification, or spam detection4. By using one of these models on AI Platform, you can customize the model's code, serving, and deployment, and use Kubeflow pipelines for the ML platform. Therefore, using an established text classification model on AI Platform to perform transfer learning is the best option for this use case.
References:
* Transfer Learning - Machine Learning's Next Frontier
* A Comprehensive Hands-on Guide to Transfer Learning with Real-World Applications in Deep Learning
* Text classification models
* Text Classification with Pre-trained Models in TensorFlow
NEW QUESTION # 146
You are developing an ML model that predicts the cost of used automobiles based on data such as location, condition model type color, and engine-'battery efficiency. The data is updated every night Car dealerships will use the model to determine appropriate car prices. You created a Vertex Al pipeline that reads the data splits the data into training/evaluation/test sets performs feature engineering trains the model by using the training dataset and validates the model by using the evaluation dataset. You need to configure a retraining workflow that minimizes cost What should you do?
Answer: C
Explanation:
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
NEW QUESTION # 147
You work at a large organization that recently decided to move their ML and data workloads to Google Cloud. The data engineering team has exported the structured data to a Cloud Storage bucket in Avro format.
You need to propose a workflow that performs analytics, creates features, and hosts the features that your ML models use for online prediction How should you configure the pipeline?
Answer: C
Explanation:
BigQuery is a service that allows you to store and query large amounts of data in a scalable and cost-effective way. You can use BigQuery to ingest the Avro files from the Cloud Storage bucket and perform analytics on the structured data. Avro is a binary file format that can store complex data types and schemas. You can use the bq load command or the BigQuery API to load the Avro files into a BigQuery table. You can then use SQL queries to analyze the data and generate insights. Dataflow is a service that allows you to create and run scalable and portable data processing pipelines on Google Cloud. You can use Dataflow to create the features for your ML models, such as transforming, aggregating, and encoding the data. You can use the Apache Beam SDK to write your Dataflow pipeline code in Python or Java. You can also use the built-in transforms or custom transforms to apply the feature engineering logic to your data. Vertex AI Feature Store is a service that allows you to store and manage your ML features on Google Cloud. You can use Vertex AI Feature Store to host the features that your ML models use for online prediction. Online prediction is a type of prediction that provides low-latency responses to individual or small batches of input data. You can use the Vertex AI Feature Store API to write the features from your Dataflow pipeline to a feature store entity type. You can then use the Vertex AI Feature Store online serving API to read the features from the feature store and pass them to your ML models for online prediction. By using BigQuery, Dataflow, and Vertex AI Feature Store, you can configure a pipeline that performs analytics, creates features, and hosts the features that your ML models use for online prediction. References:
* BigQuery documentation
* Dataflow documentation
* Vertex AI Feature Store documentation
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
NEW QUESTION # 148
A data scientist needs to identify fraudulent user accounts for a company's ecommerce platform. The company wants the ability to determine if a newly created account is associated with a previously known fraudulent user.
The data scientist is using AWS Glue to cleanse the company's application logs during ingestion.
Which strategy will allow the data scientist to identify fraudulent accounts?
Answer: D
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/glue/latest/dg/machine-learning.html
NEW QUESTION # 149
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