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| Section | Weight | Objectives |
|---|---|---|
| Scale prototypes into AI models | 18% | - Design and run experiments - Optimize model performance and generalization - Work with foundation models and generative AI techniques - Select appropriate model architectures and frameworks |
| Automate and orchestrate ML pipelines | 18% | - Use Vertex AI Pipelines, TFX, and other orchestration tools - Automate retraining and model updates - Implement CI/CD for ML systems - Design end-to-end ML workflows |
| Train and deploy models | 20% | - Deploy models for online, batch, and streaming prediction - Configure training jobs and environments - Use Vertex AI deployment features and infrastructure - Implement generative AI deployment patterns |
| Collaborate to manage data and models | 16% | - Address data privacy, compliance, and governance - Manage datasets and features in Vertex AI - Organize and prepare enterprise data
|
| Monitor and optimize AI solutions | 16% | - Monitor model performance, fairness, and drift - Troubleshoot and maintain production systems - Monitor data quality and pipeline health - Optimize cost, latency, and resource usage |
| 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 # 209
You built and manage a production system that is responsible for predicting sales numbers. Model accuracy is crucial, because the production model is required to keep up with market changes. Since being deployed to production, the model hasn't changed; however the accuracy of the model has steadily deteriorated. What issue is most likely causing the steady decline in model accuracy?
Answer: A
Explanation:
Retraining is needed as the market is changing. its how the Model keep updated and predictions accuracy.
NEW QUESTION # 210
You have developed an application that uses a chain of multiple scikit-learn models to predict the optimal price for your company's products. The workflow logic is shown in the diagram. Members of your team use the individual models in other solution workflows. You want to deploy this workflow while ensuring version control for each individual model and the overall workflow. Your application needs to be able to scale down to zero. You want to minimize the compute resource utilization and the manual effort required to manage this solution. What should you do?
Answer: A
NEW QUESTION # 211
You work for a gaming company that develops massively multiplayer online (MMO) games. You built a TensorFlow model that predicts whether players will make in-app purchases of more than $10 in the next two weeks. The model's predictions will be used to adapt each user's game experience. User data is stored in BigQuery. How should you serve your model while optimizing cost, user experience, and ease of management?
Answer: B
NEW QUESTION # 212
You work for a gaming company that has millions of customers around the world. All games offer a chat feature that allows players to communicate with each other in real time. Messages can be typed in more than
20 languages and are translated in real time using the Cloud Translation API. You have been asked to build an ML system to moderate the chat in real time while assuring that the performance is uniform across the various languages and without changing the serving infrastructure.
You trained your first model using an in-house word2vec model for embedding the chat messages translated by the Cloud Translation API. However, the model has significant differences in performance across the different languages. How should you improve it?
Answer: B
Explanation:
The problem with the current approach is that it relies on the Cloud Translation API to translate the chat messages into a common language before embedding them with the in-house word2vec model. This introduces two sources of error: the translation quality and the word2vec quality. The transla tion quality may vary across different languages, depending on the availability of data and the complexity of the grammar and vocabulary. The word2vec quality may also vary depending on the size and diversity of the corpus used to train it. These errors may affect the performance of the classifier that moderates the chat messages, resulting in significant differences across the languages.
A better approach would be to train a classifier using the chat messages in their original language, without relying on the Cloud Translation API or the in-house word2vec model. This way, the classifier can learn the nuances and subtleties of each language, and avoid the errors introduced by the translation and embedding processes. This would also reduce the latency and cost of the moderation system, as it would not need to invoke the Cloud Translation API for every message. To train a classifier using the chat messages in their original language, one could use a multilingual pre-trained model such as mBERT or XLM-R, which can handle multiple languages and domains. Alternatively, one could train a separate classifier for each language, using a monolingual pre-trained model such as BERT or a custom model tailored to the specific language and task.
:
Professional ML Engineer Exam Guide
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate Google Cloud launches machine learning engineer certification
[mBERT: Bidirectional Encoder Representations from Transformers]
[XLM-R: Unsupervised Cross-lingual Representation Learning at Scale]
[BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding]
NEW QUESTION # 213
You are developing an automated training workflow using Agent Platform Pipelines. The pipeline trains a new custom model and registers it to Model Registry. You need to validate the model's performance on a held-out test dataset. You want to ensure that the model is deployed to a production Agent Platform endpoint only if its evaluation metrics meet a specific threshold. You want to minimize cost and administrative overhead. What should you do?
Answer: A
Explanation:
Batch prediction evaluates the newly trained model against the held-out test dataset without provisioning an online endpoint. A dedicated evaluation component can calculate the required metrics, and the pipeline can use those outputs in a conditional task so deployment occurs only when the defined performance threshold is met.
NEW QUESTION # 214
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