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Question 75/112
You are training models in Vertex Al by using data that spans across multiple Google Cloud Projects You need to find track, and compare the performance of the different versions of your models Which Google Cloud services should you include in your ML workflow?
Correct Answer: B
Vertex AI Pipelines is a service that allows you to orchestrate and automate your machine learning (ML) workflows using pipelines1. A pipeline is a description of an ML workflow, including all of the components in the workflow, how the components are connected as a graph, and the runtime parameters that the pipeline accepts1. Vertex AI Pipelines helps you manage the end-to-end lifecycle of your ML projects, from data preprocessing to model deployment1.
Vertex AI Feature Store is a service that enables you to serve, share, and reuse ML features across different models and projects2. A feature is a measurable property or characteristic of an entity, such asthe age of a person or the price of a product2. Vertex AI Feature Store helps you reduce data duplication, ensure data consistency, and improve model performance2.
Vertex AI Experiments is a service that helps you track and compare the performance of different versions of your models3. You can use Vertex AI Experiments to run multiple training jobs with different hyperparameters, architectures, or data sources, and then compare the results using metrics, visualizations, and reports3. Vertex AI Experiments helps you identify the best model for your use case and optimize your model performance3. References:
* Vertex AI Pipelines | Google Cloud
* Vertex AI Feature Store | Google Cloud
* Vertex AI Experiments | Google Cloud
Vertex AI Feature Store is a service that enables you to serve, share, and reuse ML features across different models and projects2. A feature is a measurable property or characteristic of an entity, such asthe age of a person or the price of a product2. Vertex AI Feature Store helps you reduce data duplication, ensure data consistency, and improve model performance2.
Vertex AI Experiments is a service that helps you track and compare the performance of different versions of your models3. You can use Vertex AI Experiments to run multiple training jobs with different hyperparameters, architectures, or data sources, and then compare the results using metrics, visualizations, and reports3. Vertex AI Experiments helps you identify the best model for your use case and optimize your model performance3. References:
* Vertex AI Pipelines | Google Cloud
* Vertex AI Feature Store | Google Cloud
* Vertex AI Experiments | Google Cloud
- Question List (112q)
- Question 1: You work for a retail company. You have been tasked with bui...
- Question 2: You manage a team of data scientists who use a cloud-based b...
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- Question 9: You have trained a model on a dataset that required computat...
- Question 10: You have created a Vertex Al pipeline that automates custom ...
- Question 11: You recently deployed a scikit-learn model to a Vertex Al en...
- Question 12: You need to build classification workflows over several stru...
- Question 13: Your team needs to build a model that predicts whether image...
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- Question 21: You are training an ML model on a large dataset. You are usi...
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- Question 24: You are an ML engineer at a bank that has a mobile applicati...
- Question 25: You recently trained an XGBoost model on tabular data You pl...
- Question 26: You work for a biotech startup that is experimenting with de...
- Question 27: You have a functioning end-to-end ML pipeline that involves ...
- Question 28: You lead a data science team at a large international corpor...
- Question 29: You recently created a new Google Cloud Project After testin...
- Question 30: You need to execute a batch prediction on 100 million record...
- Question 31: You are a data scientist at an industrial equipment manufact...
- Question 32: You work for a social media company. You need to detect whet...
- Question 33: You manage a team of data scientists who use a cloud-based b...
- Question 34: You have been tasked with deploying prototype code to produc...
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- Question 37: You recently deployed a model lo a Vertex Al endpoint and se...
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- Question 43: You have recently created a proof-of-concept (POC) deep lear...
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- Question 50: You have trained a model by using data that was preprocessed...
- Question 51: You are training a custom language model for your company us...
- Question 52: Your team is building a convolutional neural network (CNN)-b...
- Question 53: You developed a custom model by using Vertex Al to predict y...
- Question 54: You built a deep learning-based image classification model b...
- Question 55: You developed a custom model by using Vertex Al to forecast ...
- Question 56: You are developing a training pipeline for a new XGBoost cla...
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- Question 58: You recently trained a XGBoost model that you plan to deploy...
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- Question 66: You are developing an ML model using a dataset with categori...
- Question 67: You work for the AI team of an automobile company, and you a...
- Question 68: You have created a Vertex Al pipeline that includes two step...
- Question 69: You are experimenting with a built-in distributed XGBoost mo...
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- Question 103: You are pre-training a large language model on Google Cloud....
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- Question 107: You work for a social media company. You want to create a no...
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- Question 109: You are developing a custom image classification model in Py...
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- Question 111: You have deployed a scikit-learn model to a Vertex Al endpoi...
- Question 112: You are creating a deep neural network classification model ...
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