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Machine Learning and AI Services (GCP PDE) Flashcards
GCP Professional Data Engineer Flashcards
| Front | Back |
| How can Vertex AI Workbench assist in data engineering | Vertex AI Workbench provides an integrated environment for developing and managing data and ML workflows. |
| How does BigQuery integrate with Vertex AI | BigQuery integrates with Vertex AI to provide a seamless pipeline for data analysis, training, and serving ML models. |
| How does Explainable AI in Vertex AI improve trust | Explainable AI improves trust by providing transparency into the decision-making process of ML models. |
| What data storage options integrate with GCP's AI tools | GCP's AI tools integrate with BigQuery, Cloud Storage, and Datastore. |
| What does Vertex AI Pipeline enable | Vertex AI Pipeline enables orchestration of ML workflows across GCP with reusable pipelines. |
| What feature of TensorFlow is helpful for distributed training | TensorFlow's strategies like tf.distribute help in distributed training. |
| What is a key feature of Cloud TPU in GCP for ML | Cloud TPUs offer high computational power optimized for speeding up TensorFlow workloads. |
| What is AutoML in GCP | AutoML allows non-experts to create high-quality machine learning models with minimal effort. |
| What is BigQuery ML | BigQuery ML allows training and managing ML models directly within BigQuery using SQL-like queries. |
| What is Deep Learning Containers on GCP | Deep Learning Containers are pre-configured Docker images optimized for ML and deep learning tasks. |
| What is Explainable AI in Vertex AI | Explainable AI provides insights into how your ML models make decisions. |
| What is the benefit of using AI Platform Prediction | AI Platform Prediction enables you to run predictions on models hosted in GCP with low latency. |
| What is the main benefit of Vertex AI | Vertex AI simplifies the ML lifecycle by combining data engineering, training, and deployment features. |
| What is the primary function of AI Hub in GCP | AI Hub is a repository for sharing and discovering AI and ML workflows within GCP. |
| What is the primary function of TensorFlow on GCP | TensorFlow on GCP is used for building machine learning and deep learning models. |
| What is the purpose of Model Monitoring in Vertex AI | Model Monitoring in Vertex AI detects data drift and ensures models perform effectively over time. |
| What is the role of pre-built algorithms in Vertex AI | Pre-built algorithms in Vertex AI allow users to quickly train ML models without starting from scratch. |
| What is the use of Feature Store in Vertex AI | Feature Store in Vertex AI helps manage and serve ML features for training and serving. |
| What is the use of Training Pipelines in Vertex AI | Training Pipelines help automate and streamline the training, validation, and deployment of ML models. |
| What is Vertex AI | Vertex AI is Google's unified platform for developing and deploying ML models. |
| What purpose does Dataflow serve in GCP for AI/ML workflows | Dataflow enables real-time and batch data processing needed for ML model ingestion and preparation. |
| What types of ML problems can AutoML tackle | AutoML can tackle vision, language, and structured data problems. |
| Which GCP ML tool is best for non-coders | AutoML is the best tool for non-coders to create ML models. |
| Which GCP service allows you to annotate data for machine learning | Vertex AI provides a data labeling service for annotating data. |
| Which tool on GCP is best suited for deploying ML models at scale | Vertex AI is best suited for deploying ML models at scale. |
Front
How does Explainable AI in Vertex AI improve trust
Click the card to flip
Back
Explainable AI improves trust by providing transparency into the decision-making process of ML models.
Front
What feature of TensorFlow is helpful for distributed training
Back
TensorFlow's strategies like tf.distribute help in distributed training.
Front
Which tool on GCP is best suited for deploying ML models at scale
Back
Vertex AI is best suited for deploying ML models at scale.
Front
What is the main benefit of Vertex AI
Back
Vertex AI simplifies the ML lifecycle by combining data engineering, training, and deployment features.
Front
What does Vertex AI Pipeline enable
Back
Vertex AI Pipeline enables orchestration of ML workflows across GCP with reusable pipelines.
Front
What is the use of Training Pipelines in Vertex AI
Back
Training Pipelines help automate and streamline the training, validation, and deployment of ML models.
Front
What is Deep Learning Containers on GCP
Back
Deep Learning Containers are pre-configured Docker images optimized for ML and deep learning tasks.
Front
What is BigQuery ML
Back
BigQuery ML allows training and managing ML models directly within BigQuery using SQL-like queries.
Front
What is Explainable AI in Vertex AI
Back
Explainable AI provides insights into how your ML models make decisions.
Front
What is AutoML in GCP
Back
AutoML allows non-experts to create high-quality machine learning models with minimal effort.
Front
What is the use of Feature Store in Vertex AI
Back
Feature Store in Vertex AI helps manage and serve ML features for training and serving.
Front
What is the benefit of using AI Platform Prediction
Back
AI Platform Prediction enables you to run predictions on models hosted in GCP with low latency.
Front
What is a key feature of Cloud TPU in GCP for ML
Back
Cloud TPUs offer high computational power optimized for speeding up TensorFlow workloads.
Front
How does BigQuery integrate with Vertex AI
Back
BigQuery integrates with Vertex AI to provide a seamless pipeline for data analysis, training, and serving ML models.
Front
How can Vertex AI Workbench assist in data engineering
Back
Vertex AI Workbench provides an integrated environment for developing and managing data and ML workflows.
Front
What is the primary function of AI Hub in GCP
Back
AI Hub is a repository for sharing and discovering AI and ML workflows within GCP.
Front
What is Vertex AI
Back
Vertex AI is Google's unified platform for developing and deploying ML models.
Front
What is the primary function of TensorFlow on GCP
Back
TensorFlow on GCP is used for building machine learning and deep learning models.
Front
What is the purpose of Model Monitoring in Vertex AI
Back
Model Monitoring in Vertex AI detects data drift and ensures models perform effectively over time.
Front
What data storage options integrate with GCP's AI tools
Back
GCP's AI tools integrate with BigQuery, Cloud Storage, and Datastore.
Front
Which GCP ML tool is best for non-coders
Back
AutoML is the best tool for non-coders to create ML models.
Front
Which GCP service allows you to annotate data for machine learning
Back
Vertex AI provides a data labeling service for annotating data.
Front
What types of ML problems can AutoML tackle
Back
AutoML can tackle vision, language, and structured data problems.
Front
What purpose does Dataflow serve in GCP for AI/ML workflows
Back
Dataflow enables real-time and batch data processing needed for ML model ingestion and preparation.
Front
What is the role of pre-built algorithms in Vertex AI
Back
Pre-built algorithms in Vertex AI allow users to quickly train ML models without starting from scratch.
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This deck provides an overview of using GCP's AI and ML tools, such as Vertex AI, TensorFlow on GCP, and AutoML, for data engineering purposes.