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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: Monitoring ML solutions | - Model retraining strategies - Logging and alerting (Cloud Monitoring) - Performance monitoring and drift detection |
| Topic 3: 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 |
| Topic 4: Collaborating within and across teams to manage data and models | - Collaboration between Data Scientists, Data Engineers, and ML Engineers - Data management and governance - Version control and reproducibility (e.g., DVC, MLOps) |
| Topic 5: Serving and scaling models | - Hardware accelerators (GPU/TPU) in serving - Model optimization (Quantization, Distillation) - Batch prediction - Online prediction (Vertex AI Prediction) |
| Topic 6: Scaling prototypes into ML models | - Hyperparameter tuning - Training at scale (Distributed training, TPUs) - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) |
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NEW QUESTION # 85
Your company manages an ecommerce platform and has a large dataset of customer reviews. Each review has a positive, negative, or neutral label. You need to quickly prototype a sentiment analysis model that accurately predicts the sentiment labels of new customer reviews while minimizing time and cost. What should you do?
Answer: B
Explanation:
The keywords here are " quickly prototype " and " minimize time and cost. "
* Natural Language API: Google's pre-trained Natural Language API provides out-of-the-box sentiment analysis. It requires zero training time, zero infrastructure management, and no labeling effort. This is the fastest and most cost-effective way to get a baseline/prototype.
* Why other options are incorrect:
* Option A: Fine-tuning a BERT model is computationally expensive and requires significant engineering time.
* Option C: AutoML is a great secondary step if the API is not accurate enough, but it still requires time to train and evaluate, making it slower than the pre-trained API.
* Option D: Vectorizing text and training a regression model is unnecessarily complex for a standard sentiment classification task.
NEW QUESTION # 86
You are an ML engineer at a bank. You have developed a binary classification model using AutoML Tables to predict whether a customer will make loan payments on time. The output is used to approve or reject loan requests. One customer's loan request has been rejected by your model, and the bank's risks department is asking you to provide the reasons that contributed to the model's decision. What should you do?
Answer: C
Explanation:
This would allow you to identify which features contributed the most to the model's decision, which would in turn help you to better explain why the model rejected the customer's loan request.
Varying features independently could also be a useful approach, as this would help to identify specific threshold values that change the classification. However, this strategy would be more time-consuming and would not provide as much insight into the inner-workings of the model compared to using local feature importance.
NEW QUESTION # 87
A large company has developed a BI application that generates reports and dashboards using data collected from various operational metrics. The company wants to provide executives with an enhanced experience so they can use natural language to get data from the reports. The company wants the executives to be able ask questions using written and spoken interfaces.
Which combination of services can be used to build this conversational interface? (Choose three.)
Answer: A,B,F
NEW QUESTION # 88
A retail company intends to use machine learning to categorize new products. A labeled dataset of current products was provided to the Data Science team. The dataset includes 1,200 products. The labeled dataset has 15 features for each product such as title dimensions, weight, and price. Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies.
Which model should be used for categorizing new products using the provided dataset for training?
Answer: A
NEW QUESTION # 89
You have been asked to build a model using a dataset that is stored in a medium-sized (~10 GB) BigQuery table. You need to quickly determine whether this data is suitable for model development. You want to create a one-time report that includes both informative visualizations of data distributions and more sophisticated statistical analyses to share with other ML engineers on your team. You require maximum flexibility to create your report. What should you do?
Answer: A
NEW QUESTION # 90
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