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| Section | Objectives |
|---|---|
| Topic 1: Monitoring ML solutions | - Model retraining strategies - Performance monitoring and drift detection - Logging and alerting (Cloud Monitoring) |
| Topic 2: Architecting low-code ML solutions | - AutoML capabilities and implementation - Implementing BigQuery ML for basic models - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) |
| Topic 3: Scaling prototypes into ML models | - Training at scale (Distributed training, TPUs) - Hyperparameter tuning - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) |
| Topic 4: 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) |
| Topic 5: Serving and scaling models | - Online prediction (Vertex AI Prediction) - Batch prediction - Model optimization (Quantization, Distillation) - Hardware accelerators (GPU/TPU) in serving |
| Topic 6: Automating and orchestrating ML pipelines | - Triggering and scheduling pipelines - Vertex AI Pipelines (Kubeflow Pipelines) - CI/CD for ML systems |
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NEW QUESTION # 271
You created a model that uses BigQuery ML to perform linear regression. You need to retrain the model on the cumulative data collected every week. You want to minimize the development effort and the scheduling cost. What should you do?
Answer: B
Explanation:
BigQuery is a serverless data warehouse that allows you to perform SQL queries on large-scale data. BigQuery ML is a feature of BigQuery that enables you to create and execute machine learning models using standard SQL queries. You can use BigQuery ML to perform linear regression on your data and create a model. BigQuery also provides a scheduling service that allows you to create and manage recurring SQL queries. You can use BigQuery's scheduling service to run the model retraining query periodically, such as every week. You can specify the destination table for the query results, and the schedule options, such as start date, end date, frequency, and time zone. You can also monitor the status and history of your scheduled queries. This solution can help you retrain the model on the cumulative data collected every week, while minimizing the development effort and the scheduling cost. Reference:
BigQuery ML | Google Cloud
Scheduling queries | BigQuery
NEW QUESTION # 272
A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU.
Which architecture changes would ensure that provisioned resources are being utilized effectively?
Answer: A
NEW QUESTION # 273
You work at a manufacturing company. You are building an assistant to help engineers write scripts for a proprietary, legacy machine control language called "MechScript." MechScript is highly specialized, uses unique syntax not found in public datasets, and is strictly internal to your company.
You built the base Gemini model using Agent Platform RAG Engine and grounded the model with the MechScript reference manual. The model correctly answers questions about the language, but it consistently fails to generate syntactically correct code snippets, often hallucinating Python- like syntax instead. What should you do?
Answer: D
Explanation:
Supervised fine-tuning with high-quality MechScript input-output examples teaches the model the language's proprietary syntax and code-generation patterns. RAG supplies reference knowledge at inference time, but fine-tuning is more appropriate when the model must consistently produce outputs in a specialized format that is absent from its original training data.
NEW QUESTION # 274
You work on a team that builds state-of-the-art deep learning models by using the TensorFlow framework. Your team runs multiple ML experiments each week which makes it difficult to track the experiment runs. You want a simple approach to effectively track, visualize and debug ML experiment runs on Google Cloud while minimizing any overhead code. How should you proceed?
Answer: B
Explanation:
Vertex AI Experiments is a service that allows you to track, compare, and optimize your ML experiments on Google Cloud. You can use Vertex AI Experiments to log metrics and parameters from your TensorFlow models, and then visualize them in Vertex AI TensorBoard. Vertex AI TensorBoard is a managed service that provides a web interface for viewing and debugging your ML experiments. You can use Vertex AI TensorBoard to compare different runs, inspect model graphs, analyze scalars, histograms, images, and more. By using Vertex AI Experiments and Vertex AI TensorBoard, you can simplify your ML experiment tracking and visualization workflow, and avoid the overhead of setting up and maintaining your own Cloud Functions, Cloud Storage buckets, or VMs. Reference:
[Vertex AI Experiments documentation]
[Vertex AI TensorBoard documentation]
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
NEW QUESTION # 275
You are an ML engineer at a manufacturing company. You are creating a classification model for a predictive maintenance use case. You need to predict whether a crucial machine will fail in the next three days so that the repair crew has enough time to fix the machine before it breaks.
Regular maintenance of the machine is relatively inexpensive, but a failure would be very costly.
You have trained several binary classifiers to predict whether the machine will fail, where a prediction of 1 means that the ML model predicts a failure.
You are now evaluating each model on an evaluation dataset. You want to choose a model that prioritizes detection while ensuring that more than 50% of the maintenance jobs triggered by your model address an imminent machine failure. Which model should you choose?
Answer: D
Explanation:
Priority is to detect(Pointing to Recall) and correctly detect (more that 50% - pointing to Precision).
NEW QUESTION # 276
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