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| Section | Objectives |
|---|---|
| Collaborating within and across teams to manage data and models | - Data management and governance - Collaboration between Data Scientists, Data Engineers, and ML Engineers - Version control and reproducibility (e.g., DVC, MLOps) |
| Serving and scaling models | - Hardware accelerators (GPU/TPU) in serving - Model optimization (Quantization, Distillation) - Online prediction (Vertex AI Prediction) - Batch prediction |
| Architecting low-code ML solutions | - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) - AutoML capabilities and implementation - Implementing BigQuery ML for basic models |
| Monitoring ML solutions | - Logging and alerting (Cloud Monitoring) - Performance monitoring and drift detection - Model retraining strategies |
| Scaling prototypes into ML models | - Training at scale (Distributed training, TPUs) - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) - Hyperparameter tuning |
| Automating and orchestrating ML pipelines | - CI/CD for ML systems - Vertex AI Pipelines (Kubeflow Pipelines) - Triggering and scheduling pipelines |
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NEW QUESTION # 247
You are the lead ML engineer on a mission-critical project that involves analyzing massive datasets using Apache Spark. You need to establish a robust environment that allows your team to rapidly prototype Spark models using Jupyter notebooks. What is the fastest way to achieve this?
Answer: A
Explanation:
Dataproc provides a managed Spark environment and integrates with Jupyter notebooks, ideal for large datasets and rapid prototyping. It reduces setup time compared to manual Spark configurations on Compute Engine or Vertex AI. Colab Enterprise is more suitable for small-scale prototyping rather than extensive Spark-based analysis.
NEW QUESTION # 248
You recently trained an XGBoost model on tabular data. You plan to expose the model for internal use as an HTTP microservice. After deployment, you expect a small number of incoming requests. You want to productionize the model with the least amount of effort and latency. What should you do?
Answer: C
NEW QUESTION # 249
You are building an application that extracts information from invoices and receipts. You want to implement this application with minimal custom code and training. What should you do?
Answer: D
Explanation:
The Cloud Document AI API is specifically designed for processing and extracting structured information from documents such as invoices and receipts. It provides pre-trained models that can recognize and extract relevant fields (e.g., invoice number, date, total amount) with minimal custom code or training. This solution requires minimal setup and is highly efficient for structured documents, making it the ideal choice for this use case.
NEW QUESTION # 250
You are developing an image recognition model using PyTorch based on ResNet50 architecture.
Your code is working fine on your local laptop on a small subsample. Your full dataset has 200k labeled images. You want to quickly scale your training workload while minimizing cost. You plan to use 4 V100 GPUs. What should you do?
Answer: C
NEW QUESTION # 251
You are building an ML model to detect anomalies in real-time sensor data. You will use Pub/Sub to handle incoming requests. You want to store the results for analytics and visualization. How should you configure the pipeline?
Answer: D
Explanation:
* Dataflow is a fully managed service for executing Apache Beam pipelines that can process streaming or batch data1.
* Al Platform is a unified platform that enables you to build and run machine learning applications across Google Cloud2.
* BigQuery is a serverless, highly scalable, and cost-effective cloud data warehouse designed for business agility3.
These services are suitable for building an ML model to detect anomalies in real-time sensor data, as they can handle large-scale data ingestion, preprocessing, training, serving, storage, and visualization. The other options are not as suitable because:
* DataProc is a service for running Apache Spark and Apache Hadoop clusters, which are not optimized for streaming data processing4.
* AutoML is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs5. However, it does not support custom models or real-time predictions.
* Cloud Bigtable is a scalable, fully managed NoSQL database service for large analytical and operational workloads. However, it is not designed for ad hoc queries or interactive analysis.
* Cloud Functions is a serverless execution environment for building and connecting cloud services.
However, it is not suitable for storing or visualizing data.
* Cloud Storage is a service for storing and accessing data on Google Cloud. However, it is not a data warehouse and does not support SQL queries or visualization tools.
NEW QUESTION # 252
......
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