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| Section | Weight | Objectives |
|---|---|---|
| Architect low-code AI solutions | 12% | - Apply responsible AI principles to low-code designs - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder - Identify use cases for low-code/no-code AI tools |
| Automate and orchestrate ML pipelines | 18% | - Automate retraining and model updates - Use Vertex AI Pipelines, TFX, and other orchestration tools - Design end-to-end ML workflows - Implement CI/CD for ML systems |
| Collaborate to manage data and models | 16% | - Manage datasets and features in Vertex AI - Organize and prepare enterprise data
|
| Monitor and optimize AI solutions | 16% | - Monitor data quality and pipeline health - Monitor model performance, fairness, and drift - Optimize cost, latency, and resource usage - Troubleshoot and maintain production systems |
| Train and deploy models | 20% | - Configure training jobs and environments - Implement generative AI deployment patterns - Deploy models for online, batch, and streaming prediction - Use Vertex AI deployment features and infrastructure |
| Scale prototypes into AI models | 18% | - Select appropriate model architectures and frameworks - Work with foundation models and generative AI techniques - Design and run experiments - Optimize model performance and generalization |
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NEW QUESTION # 162
Your team needs to build a model that predicts whether images contain a driver's license, passport, or credit card. The data engineering team already built the pipeline and generated a dataset composed of 10,000 images with driver's licenses, 1,000 images with passports, and 1,000 images with credit cards. You now have to train a model with the following label map: ['driversjicense', 'passport', 'credit_card']. Which loss function should you use?
Answer: B
Explanation:
se sparse_categorical_crossentropy. Examples for above 3-class classification problem: [1] , [2], [3]
NEW QUESTION # 163
You are responsible for building a unified analytics environment across a variety of on-premises data marts. Your company is experiencing data quality and security challenges when integrating data across the servers, caused by the use of a wide range of disconnected tools and temporary solutions. You need a fully managed, cloud-native data integration service that will lower the total cost of work and reduce repetitive work. Some members on your team prefer a codeless interface for building Extract, Transform, Load (ETL) process. Which service should you use?
Answer: A
Explanation:
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. Reference:
Cloud Data Fusion documentation
Cloud Data Fusion overview
NEW QUESTION # 164
You work in the credit card fraud detection department of a bank that processes millions of transactions per day. You need to create and serve a real-time fraud detection model. To address the challenge of feature engineering at this scale with limited raw data, you plan to create time- related aggregate features, such as the average transaction amount within the last month or the amount of time since the last transaction. How should you process the data for model training and serving?
Answer: B
Explanation:
A streaming Dataflow pipeline can process millions of transaction events from Pub/Sub and use stateful, windowed aggregations to calculate time-dependent features such as monthly average transaction amounts and time since the previous transaction. Storing these engineered values in Agent Platform Feature Store makes consistent features available for both model training and low-latency online serving.
Reference:
https://docs.cloud.google.com/dataflow/docs/concepts/streaming-pipelines
https://docs.cloud.google.com/dataflow/docs/concepts/streaming-with-cloud-pubsub
NEW QUESTION # 165
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: B
Explanation:
One-hot encoding is a technique that converts categorical variables into numerical variables by creating dummy variables for each possible category. Each dummy variable has a value of 1 if the original variable belongs to that category, and 0 otherwise1. One-hot encoding can help linear regression models to capture the effect of different categories on the target variable without imposing any ordinal relationship among them2.
Dataprep is a service that allows you to explore, clean, and transform your data for analysis and machine learning. You can use Dataprep to apply one-hot encoding to your city name variable and make each city a column with binary values3. This way, you can prepare your data using the least amount of coding while maintaining the predictive variables. Therefore, using Dataprep to transform the state column using a one-hot encoding method is the best option for this use case.
References:
* One Hot Encoding: A Beginner's Guide
* One-Hot Encoding in Linear Regression Models
* Dataprep documentation
NEW QUESTION # 166
You have deployed multiple versions of an image classification model on AI Platform. You want to monitor the performance of the model versions over time. How should you perform this comparison?
Answer: A
Explanation:
Comparing the mean average precision across the models using the Continuous Evaluation feature is the best way to compare the models' performance over time. Mean average precision is a measure of a model's ability to rank positive examples higher than negative examples. The Continuous Evaluation feature automatically evaluates the models' performance on new data as it becomes available.
https://cloud.google.com/ai-platform/prediction/docs/continuous-evaluation#how_it_works
NEW QUESTION # 167
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