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| Section | Objectives |
|---|---|
| Topic 1: Automating and orchestrating ML pipelines | - Triggering and scheduling pipelines - Vertex AI Pipelines (Kubeflow Pipelines) - CI/CD for ML systems |
| Topic 2: Collaborating within and across teams to manage data and models | - Collaboration between Data Scientists, Data Engineers, and ML Engineers - Version control and reproducibility (e.g., DVC, MLOps) - Data management and governance |
| Topic 3: Serving and scaling models | - Batch prediction - Model optimization (Quantization, Distillation) - Hardware accelerators (GPU/TPU) in serving - Online prediction (Vertex AI Prediction) |
| Topic 4: Scaling prototypes into ML models | - Training at scale (Distributed training, TPUs) - Hyperparameter tuning - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) |
| Topic 5: Architecting low-code ML solutions | - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) - Implementing BigQuery ML for basic models - AutoML capabilities and implementation |
| Topic 6: Monitoring ML solutions | - Logging and alerting (Cloud Monitoring) - Model retraining strategies - Performance monitoring and drift detection |
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NEW QUESTION # 282
You are training models in Vertex AI 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?
Answer: B
NEW QUESTION # 283
Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which platform components should you choose for this system?
Answer: B
Explanation:
* Option A is incorrect because Vertex AI Pipelines and App Engine do not meet all the requirements of the system. Vertex AI Pipelines is a service that allows you to create, run, andmanage ML workflows using TensorFlow Extended (TFX) components or custom components1. App Engine is a service that allows you to build and deploy scalable web applications using standard or flexible environments2. However, App Engine does not support Docker containers in the standard environment, and does not provide a dedicated service for online prediction and monitoring of ML models3.
* Option B is correct because Vertex AI Pipelines, Vertex AI Prediction, and Vertex AI Model Monitoring meet all the requirements of the system. Vertex AI Prediction is a service that allows you to deploy and serve ML models for online or batch prediction, with support for autoscaling and custom containers4. Vertex AI Model Monitoring is a service that allows you to monitor the performance and fairness of your deployed models, and get alerts for any issues or anomalies5.
* Option C is incorrect because Cloud Composer, BigQuery ML, and Vertex AI Prediction do not meet all the requirements of the system. Cloud Composer is a service that allows you to create, schedule, and
* manage workflows using Apache Airflow. BigQuery ML is a service that allows you to create and use ML models within BigQuery using SQL queries. However, BigQuery ML does not support custom containers, and Vertex AI Prediction does not support scheduled model retraining or model monitoring.
* Option D is incorrect because Cloud Composer, Vertex AI Training with custom containers, and App Engine do not meet all the requirements of the system. Vertex AI Training is a service that allows you to train ML models using built-in algorithms or custom containers. However, Vertex AI Training does not support online prediction or model monitoring, and App Engine does not support Docker containers in the standard environment or online prediction and monitoring of ML models3.
References:
* Vertex AI Pipelines overview
* App Engine overview
* Choosing an App Engine environment
* Vertex AI Prediction overview
* Vertex AI Model Monitoring overview
* [Cloud Composer overview]
* [BigQuery ML overview]
* [BigQuery ML limitations]
* [Vertex AI Training overview]
NEW QUESTION # 284
You are an ML engineer at a manufacturing company. You need to build a model that identifies defects in products based on images of the product taken at the end of the assembly line. You want your model to preprocess the images with lower computation to quickly extract features of defects in products. Which approach should you use to build the model?
Answer: B
NEW QUESTION # 285
While monitoring your model training's GPU utilization, you discover that you have a native synchronous implementation. The training data is split into multiple files. You want to reduce the execution time of your input pipeline. What should you do?
Answer: D
Explanation:
https://www.tensorflow.org/guide/data_performance
NEW QUESTION # 286
You have trained a new version of a pricing model that performs better offline than the version currently serving all production traffic. Business stakeholders require evidence of improvement on live traffic and demand an immediate rollback path if revenue metrics decline. What should you do?
Answer: D
Explanation:
Deploying both versions to a single endpoint with a traffic split produces a live canary comparison, and rollback is a traffic-percentage change rather than a redeployment. Model Registry provides the version lineage needed to track which artifact served which traffic.
NEW QUESTION # 287
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