ML, RAG and Data Sharing
This topic covers feature preparation, BigQuery ML, unstructured data, embeddings, retrieval-augmented generation, sharing rules, Analytics Hub, datasets, reports, and visualizations.
How to study for Google Professional Data Engineer
Treat each item as a data workload decision: identify source, sink, velocity, schema, governance, storage pattern, processing mode, and operational risk.
Core concepts
Concept 1
ML, RAG and Data Sharing questions test data engineering design decisions across ingestion, storage, analysis, automation, governance, and reliability.
Exam cue: Identify the source, sink, processing mode, data model, access pattern, freshness requirement, and governance boundary.
Concept 2
The best answer maps data shape, velocity, quality, access pattern, compliance, processing model, and operations needs to the right Google Cloud service.
Exam cue: Choose the service pattern that satisfies batch, streaming, analytics, ML, storage, security, and operational requirements.
Concept 3
Eliminate answers that ignore schema evolution, late data, IAM, regional constraints, lineage, cost, quotas, or recovery behavior.
Exam cue: Prefer managed, observable, repeatable, secure, cost-aware, and fault-tolerant data pipelines when requirements support them.
Risk pitfalls and guardrails
Choosing a storage system without checking query pattern, latency, consistency, cost, and lifecycle requirements.
Guardrail: Avoid answers that ignore IAM, privacy, schema quality, late data, storage access patterns, query cost, quotas, or pipeline failure handling.
Treating streaming data like batch data when event time, windows, and late arrivals matter.
Guardrail: Avoid answers that ignore IAM, privacy, schema quality, late data, storage access patterns, query cost, quotas, or pipeline failure handling.
Ignoring data governance, privacy, monitoring, or automation until after the pipeline is built.
Guardrail: Avoid answers that ignore IAM, privacy, schema quality, late data, storage access patterns, query cost, quotas, or pipeline failure handling.
Memory anchors
Feature Engineering
Feature engineering transforms raw data into useful inputs for machine learning models.
BigQuery ML
BigQuery ML creates and runs machine learning models using SQL in BigQuery.
Embedding
An embedding represents text, image, or other content as vectors for similarity and retrieval tasks.
RAG
Retrieval-augmented generation grounds generative AI responses in retrieved enterprise content.
Unstructured Data
Unstructured data such as documents, images, or audio often needs extraction, embeddings, or metadata.
Analytics Hub
Analytics Hub shares BigQuery datasets and data exchanges with controlled access.
Dataset Publishing
Dataset publishing makes curated data available to approved consumers.
Sharing Rule
A sharing rule defines who can access data and under which conditions.
Visualization
A visualization communicates analytical results through charts, dashboards, or reports.
ML Serving Data
ML serving data must match training expectations for freshness, quality, and feature definitions.
Checkpoint rule
Do the check-up only after you can summarize each concept in one sentence and identify one dangerous pitfall from memory.
Knowledge Check (after reading)
Short check-up to confirm understanding of this module.
Check-up Questions
What does BigQuery ML let analysts do without moving data out of BigQuery?
What is Vertex AI on Google Cloud?
Answer all questions to submit.
Next step personalized recommendations
Continue learning
Move forward only after this module is stable.
What is Pass Harbor?
Completely free exam prep for 317 U.S. exams.
- Practice questions
- Flashcards
- Study guides
- Mock exams
- No registration
- No paywall
- Start instantly
“No more expensive exam prep. Quality study tools should be accessible to everyone.”