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| Section | Objectives |
|---|---|
| Architecting low-code ML solutions | - Implementing BigQuery ML for basic models - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) - AutoML capabilities and implementation |
| 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 |
| Automating and orchestrating ML pipelines | - Triggering and scheduling pipelines - CI/CD for ML systems - Vertex AI Pipelines (Kubeflow Pipelines) |
| Serving and scaling models | - Hardware accelerators (GPU/TPU) in serving - Batch prediction - Model optimization (Quantization, Distillation) - Online prediction (Vertex AI Prediction) |
| Scaling prototypes into ML models | - Training at scale (Distributed training, TPUs) - Hyperparameter tuning - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) |
| Monitoring ML solutions | - Model retraining strategies - Performance monitoring and drift detection - Logging and alerting (Cloud Monitoring) |
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NEW QUESTION # 373
You have deployed a custom classification model to an Agent Platform endpoint to predict the likelihood of customer churn in real-time. You need to collect the data to calculate the model's exact precision and recall by comparing production predictions with ground truth data in BigQuery. You want to minimize development effort. What should you do?
Answer: B
Explanation:
Request-response logging automatically records every online prediction request and response in BigQuery when the sampling rate is set to 1.0. These logged predictions can then be joined with the ground truth data to calculate exact production precision and recall without modifying the serving container or building a separate prediction workflow.
NEW QUESTION # 374
You are an ML engineer at a regulated insurance company. You are asked to develop an insurance approval model that accepts or rejects insurance applications from potential customers. What factors should you consider before building the model?
Answer: A
Explanation:
https://www.oecd.org/finance/Impact-Big-Data-AI-in-the-Insurance-Sector.pdf
https://medium.com/artefact-engineering-and-data-science/including-ethics-best-practices-in-your-data-science-project-from-day-one-c15b26c2bf99
NEW QUESTION # 375
You work on the data science team at a manufacturing company. You are reviewing the company's historical sales data, which has hundreds of millions of records. For your exploratory data analysis, you need to calculate descriptive statistics such as mean, median, and mode; conduct complex statistical tests for hypothesis testing; and plot variations of the features over time You want to use as much of the sales data as possible in your analyses while minimizing computational resources. What should you do?
Answer: B
Explanation:
D Use BigQuery to calculate the descriptive statistics, and use Google Data Studio to visualize the time plots. Use Vertex Al Workbench user-managed notebooks to run the statistical analyses.
Explanation:
BigQuery is a powerful tool for analyzing large datasets and can be used to quickly calculate descriptive statistics, such as mean, median, and mode, on large amounts of data. By using BigQuery, you can analyze the entire dataset and minimize the computational resources required for your analyses.
Once you have calculated the descriptive statistics, you can use Vertex Al Workbench user-managed notebooks to visualize the time plots and run the statistical analyses. Vertex Al Workbench allows you to interactively explore the data, create visualizations, and perform advanced statistical analysis. It's also possible to run these notebooks on a powerful GPU which will help to increase the speed of the analysis.
NEW QUESTION # 376
You are a data scientist at an industrial equipment manufacturing company. You are developing a regression model to estimate the power consumption in the company's manufacturing plants based on sensor data collected from all of the plants. The sensors collect tens of millions of records every day. You need to schedule daily training runs for your model that use all the data collected up to the current date. You want your model to scale smoothly and require minimal development work. What should you do?
Answer: C
NEW QUESTION # 377
You developed an ML model with AI Platform, and you want to move it to production. You serve a few thousand queries per second and are experiencing latency issues. Incoming requests are served by a load balancer that distributes them across multiple Kubeflow CPU-only pods running on Google Kubernetes Engine (GKE). Your goal is to improve the serving latency without changing the underlying infrastructure.
What should you do?
Answer: B
Explanation:
To improve the serving latency of an ML model on AI Platform, you can recompile TensorFlow Serving using the source to support CPU-specific optimizations and instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes, this way GKE will schedule the pods on nodes with at least that CPU platform.
NEW QUESTION # 378
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