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Zorluk: OrtaCreating Conceptual, Logical, and Physical Architectures

An automotive manufacturer is establishing a cloud architecture on Google Cloud to handle real-time telemetry and high-definition map updates for a connected fleet of autonomous vehicles. The conceptual architecture defines three logical tiers: high-throughput regional data ingestion, real-time time-series processing for operational status lookups, and long-term analytical reporting for fleet performance metrics. Which TWO physical GCP component choices correctly translate these logical requirements into Google Cloud resources while maintaining architectural efficiency? (Select TWO.)

  1. Provision Cloud Pub/Sub for regional telemetry ingestion coupled with Cloud Bigtable for low-latency time-series storage and high-throughput operational status lookups.Cevap
  2. B
    Provision Cloud Spanner as the primary storage engine for raw, high-frequency vehicle telemetry streams to support standard relational ACID transactions across all raw data points.
  3. Deploy Cloud Dataflow for unified stream processing to aggregate telemetry and load transformed historical data into BigQuery for fleet-wide analytical reporting.Cevap
  4. D
    Deploy a dedicated Google Kubernetes Engine (GKE) Autopilot cluster solely to host a single stateless microservice that forwards HTTP webhook payloads directly to Pub/Sub.
  5. E
    Configure granular Identity and Access Management (IAM) roles for application service accounts as the sole security boundary to prevent unauthorized copying of telemetry data to external cloud storage locations.

Cevap

The optimal architecture combines Cloud Pub/Sub and Cloud Bigtable for high-throughput streaming ingestion and low-latency operational time-series data, along with Cloud Dataflow and BigQuery for stream processing and analytical data warehousing.
Translating the conceptual architecture into concrete GCP physical resources requires matching workload characteristics to optimal services. High-velocity streaming ingestion requires Cloud Pub/Sub, operational low-latency time-series queries require Cloud Bigtable, and real-time transformation into an analytical data warehouse requires Cloud Dataflow combined with BigQuery.

Adım Adım Çözüm

1
Map the conceptual ingestion tier requirement to physical GCP services.
Cloud Pub/Sub handles scalable regional streaming ingestion from autonomous vehicle fleets.
Decouples data producers from downstream consumers and handles massive influx of telemetry payloads.
2
Map the operational time-series status lookup tier requirement.
Cloud Bigtable provides low-latency key-value and time-series reads and writes.
Bigtable scales seamlessly for high-frequency write operations and rapid index-based telemetry lookups.
3
Map the long-term processing and analytics tier requirement.
Cloud Dataflow transforms stream data and loads historical metrics into BigQuery.
Dataflow executes stream windowing and pipeline transformations, enabling BigQuery to serve as the analytical warehouse.

Anahtar Kavram

Translating Conceptual and Logical Tiering to GCP Physical Resources
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Konular

Creating Conceptual, Logical, and Physical Architectures