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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Architect low-code AI solutions | 12% | - Identify use cases for low-code/no-code AI tools - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder - Apply responsible AI principles to low-code designs |
| Topic 2: Monitor and optimize AI solutions | 16% | - Optimize cost, latency, and resource usage - Monitor model performance, fairness, and drift - Monitor data quality and pipeline health - Troubleshoot and maintain production systems |
| Topic 3: Automate and orchestrate ML pipelines | 18% | - Use Vertex AI Pipelines, TFX, and other orchestration tools - Design end-to-end ML workflows - Implement CI/CD for ML systems - Automate retraining and model updates |
| Topic 4: Train and deploy models | 20% | - Deploy models for online, batch, and streaming prediction - Implement generative AI deployment patterns - Use Vertex AI deployment features and infrastructure - Configure training jobs and environments |
| Topic 5: Collaborate to manage data and models | 16% | - Organize and prepare enterprise data
- Manage datasets and features in Vertex AI |
| Topic 6: Scale prototypes into AI models | 18% | - Optimize model performance and generalization - Design and run experiments - Select appropriate model architectures and frameworks - Work with foundation models and generative AI techniques |
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NEW QUESTION # 375
You built a custom Vertex AI pipeline job that preprocesses images and trains an object detection model. The pipeline currently uses 1 n1-standard-8 machine with 1 NVIDIA Tesla V100 GPU.
You want to reduce the model training time without compromising model accuracy. What should you do?
Answer: C
Explanation:
Increasing the number of GPUs while scaling CPU resources allows parallel training, which reduces training time without compromising accuracy. Using 3 NVIDIA Tesla V100 GPUs provides more compute power for efficient model training in Vertex AI Pipelines.
NEW QUESTION # 376
You have recently developed a new ML model in a Jupyter notebook. You want to establish a reliable and repeatable model training process that tracks the versions and lineage of your model artifacts. You plan to retrain your model weekly. How should you operationalize your training process?
Answer: D
Explanation:
The best way to operationalize your training process is to use Vertex AI Pipelines, which allows you to create and run scalable, portable, and reproducible workflows for your ML models. Vertex AI Pipelines also integrates with Vertex AI Metadata, which tracks the provenance, lineage, and artifacts of your ML models.
By using a Vertex AI CustomTrainingJobOp component, you can train your model using the same code as in your Jupyter notebook. By using a ModelUploadOp component, you can upload your trained model to Vertex AI Model Registry, which manages the versions and endpoints of your models. By using Cloud Scheduler and Cloud Functions, you can trigger your Vertex AI pipeline to run weekly, according to your plan. References:
* Vertex AI Pipelines documentation
* Vertex AI Metadata documentation
* Vertex AI CustomTrainingJobOp documentation
* ModelUploadOp documentation
* Cloud Scheduler documentation
* [Cloud Functions documentation]
NEW QUESTION # 377
You work for an online retailer. Your company has a few thousand short lifecycle products. Your company has five years of sales data stored in BigQuery. You have been asked to build a model that will make monthly sales predictions for each product. You want to use a solution that can be implemented quickly with minimal effort. What should you do?
Answer: D
NEW QUESTION # 378
You have successfully deployed to production a large and complex TensorFlow model trained on tabular data.
You want to predict the lifetime value (LTV) field for each subscription stored in the BigQuery table named subscription. subscriptionPurchase in the project named my-fortune500-company-project.
You have organized all your training code, from preprocessing data from the BigQuery table up to deploying the validated model to the Vertex AI endpoint, into a TensorFlow Extended (TFX) pipeline. You want to prevent prediction drift, i.e., a situation when afeature data distribution in production changes significantly over time. What should you do?
Answer: A
Explanation:
* Option A is incorrect because implementing continuous retraining of the model daily using Vertex AI Pipelines is not the most efficient way to prevent prediction drift. Vertex AI Pipelines is a service that allows you to create and run scalable and portable ML pipelines on Google Cloud1. You can use Vertex AI Pipelines to retrain your model daily using the latest data from the BigQuery table. However, this option may be unnecessary or wasteful, as the data distribution may not change significantly every day, and retraining the model may consume a lot of resources and time. Moreover, this option does not monitor the model performance or detect the prediction drift, which are essential steps for ensuring the quality and reliability of the model.
* Option B is correct because adding a model monitoring job where 10% of incoming predictions are sampled 24 hours is the best way to prevent prediction drift. Model monitoring is a service that allows you to track the performance and health of your deployed models over time2. You can use model monitoring to sample a fraction of the incoming predictions and compare them with the ground truth labels, which can be obtained from the BigQuery table or other sources. You can also use model monitoring to compute various metrics, such as accuracy, precision, recall, or F1-score, and set thresholds or alerts for them. By using model monitoring, you can detect and diagnose the prediction drift, and decide when to retrain or update your model. Sampling 10% of the incoming predictions every
24 hours is a reasonable choice, as it balances the trade-off between the accuracy and the cost of the monitoring job.
* Option C is incorrect because adding a model monitoring job where 90% of incoming predictions are sampled 24 hours is not a optimal way to prevent prediction drift. This option has the same advantages as option B, as it uses model monitoring to track the performance and health of the deployed model.
However, this option is not cost-effective, as it samples a very large fraction of the incoming predictions, which may incur a lot of storage and processing costs. Moreover, this option may not improve the accuracy of the monitoring job significantly, as sampling 10% of the incoming predictions may already provide a representative sample of the data distribution.
* Option D is incorrect because adding a model monitoring job where 10% of incoming predictions are sampled every hour is not a necessary way to prevent prediction drift. This option also has the same advantages as option B, as it uses model monitoring to track the performance and health of the deployed model. However, this option may be excessive, as it samples the incoming predictions too frequently, which may not reflect the actual changes in the data distribution. Moreover, this option may incur more storage and processing costs than option B, as it generates more samples and metrics.
References:
* Vertex AI Pipelines documentation
* Model monitoring documentation
* [Prediction drift]
* [TensorFlow Extended documentation]
* [BigQuery documentation]
* [Vertex AI documentation]
NEW QUESTION # 379
You are creating a model training pipeline to predict sentiment scores from text-based product reviews. You want to have control over how the model parameters are tuned, and you will deploy the model to an endpoint after it has been trained You will use Vertex Al Pipelines to run the pipeline You need to decide which Google Cloud pipeline components to use What components should you choose?
Answer: B
Explanation:
According to the web search results, Vertex AI Pipelines is a serverless orchestrator for running ML pipelines, using either the KFP SDK or TFX1. Vertex AI Pipelines provides a set of prebuilt components that can be used to perform common ML tasks, such as training, evaluation, deployment, and more2. Vertex AI ModelEvaluationOp and ModelDeployOp are two such components that can be used to evaluate and deploy a model to an endpoint for online inference3. However, Vertex AI Pipelines does not provide a prebuilt component for hyperparameter tuning. Therefore, to have control over how the model parameters are tuned, you need to use a custom component that calls the Vertex AI HyperparameterTuningJob service4. Therefore, option A is the best way to decide which Google Cloud pipeline components to use for the given use case, as it includes a custom component for hyperparameter tuning, and prebuilt components for model evaluation and deployment. The other options are not relevant or optimal for this scenario. Reference:
Vertex AI Pipelines
Google Cloud Pipeline Components
Vertex AI ModelEvaluationOp and ModelDeployOp
Vertex AI HyperparameterTuningJob
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
NEW QUESTION # 380
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