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.

GCP Professional Data Engineer

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.

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