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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Monitor and optimize AI solutions | 16% | - Monitor data quality and pipeline health - Troubleshoot and maintain production systems - Monitor model performance, fairness, and drift - Optimize cost, latency, and resource usage |
| Topic 2: Automate and orchestrate ML pipelines | 18% | - Use Vertex AI Pipelines, TFX, and other orchestration tools - Automate retraining and model updates - Design end-to-end ML workflows - Implement CI/CD for ML systems |
| Topic 3: Collaborate to manage data and models | 16% | - Address data privacy, compliance, and governance - Organize and prepare enterprise data
|
| Topic 4: Scale prototypes into AI models | 18% | - Design and run experiments - Work with foundation models and generative AI techniques - Optimize model performance and generalization - Select appropriate model architectures and frameworks |
| Topic 5: 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 |
| Topic 6: Architect low-code AI solutions | 12% | - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder - Identify use cases for low-code/no-code AI tools - Apply responsible AI principles to low-code designs |
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NEW QUESTION # 22
You work for a retail company. You have a managed tabular dataset in Vertex Al that contains sales data from three different stores. The dataset includes several features such as store name and sale timestamp. You want to use the data to train a model that makes sales predictions for a new store that will open soon You need to split the data between the training, validation, and test sets What approach should you use to split the data?
Answer: D
NEW QUESTION # 23
You are working on a binary classification ML algorithm that detects whether an image of a classified scanned document contains a company's logo. In the dataset, 96% of examples don't have the logo, so the dataset is very skewed. Which metrics would give you the most confidence in your model?
Answer: A
Explanation:
Even a model which always says that don't have the logo will have a good precision because is the most common. What we need is improve recall.
NEW QUESTION # 24
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: C
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 # 25
A retail company intends to use machine learning to categorize new products. A labeled dataset of current products was provided to the Data Science team. The dataset includes 1,200 products. The labeled dataset has 15 features for each product such as title dimensions, weight, and price. Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies.
Which model should be used for categorizing new products using the provided dataset for training?
Answer: B
NEW QUESTION # 26
You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using Al Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness. Which actions should you take?
Choose 2 answers
Answer: D,E
Explanation:
Reference:
https://cloud.google.com/ai-platform/training/docs/using-hyperparameter-tuning#early-stopping
NEW QUESTION # 27
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