You are building a real-time analytics dashboard using GCP services. The data comes from various IoT devices and must be ingested, processed, and made available for query in real-time. Which combination of GCP services should you use?

This combination is ideal for real-time IoT analytics. Cloud IoT Core is specifically designed for securely connecting and managing IoT devices, making it the best choice for ingestion. Dataflow provides a fully managed, serverless service for stream and batch data processing, perfectly suited for real-time transformations and aggregations of the ingested IoT data. BigQuery is a highly scalable, serverless data warehouse optimized for analytics, offering excellent performance for querying large datasets in real-time. Firestore is a NoSQL document database, better for mobile/web applications than large-scale analytics. Cloud Endpoints is for API management, not data processing. Dataprep is for data preparation and cleansing, not real-time stream processing. Dataproc is for Apache Hadoop/Spark clusters, which can be overkill for real-time stream processing compared to Dataflow, and Cloud Spanner is a globally distributed relational database, more for transactional workloads than analytical queries.