Associate Cloud Engineer · 17% of the exam

Planning and configuring a cloud solution: free practice questions

5 sample questions from our 43-question bank for this domain — answers and explanations included. These are the same scenario-based style as the real Google Cloud exam.

1. A small startup is launching an internal scheduling microservice that runs 5-minute background tasks triggered by Cloud Scheduler every hour. The tasks process in-memory data, take approximately 30 seconds to execute, and have variable resource requirements. Which compute solution is most cost-effective?

A. Compute Engine with a single n1-standard-1 instance running continuously
B. Cloud Functions with a 512 MB memory allocation and 60-second timeout✓ Correct
C. GKE cluster with a single node for containerized workload management
D. Cloud Run deployed as a service scaled to zero replicas when idle

Explanation

Cloud Functions is the best choice because the workload is short-lived (30 seconds), triggered on a schedule, and doesn't require constant availability. You pay only for execution time (0.1-second granularity), making it extremely cost-efficient for sporadic tasks. Cloud Run would work but requires at least one warm instance by default and higher memory overhead. Compute Engine with a continuously running instance wastes money since the task only runs 1 hour per day (60 minutes of compute vs. ~30 seconds of actual work). GKE adds unnecessary orchestration complexity and cost for a simple scheduled task.

2. Your organization runs a batch data processing job that loads 50 TB of CSV files from Cloud Storage and analyzes them once monthly. The job takes 2 hours to complete using a 10-node GKE cluster. Which component would provide the best cost reduction without redesigning the application?

A. Use preemptible VMs for the GKE nodes to reduce compute costs✓ Correct
B. Switch from Cloud Storage Standard to Coldline class for the CSV files
C. Migrate to BigQuery instead of running analysis on GKE
D. Implement committed use discounts for the GKE cluster's on-demand nodes

Explanation

Preemptible VMs offer up to 70% cost savings and are ideal for fault-tolerant batch jobs that can tolerate interruptions—this job can simply restart if a node is preempted. This requires minimal application changes. Switching to Coldline won't help since the files are accessed once monthly (within the 30-day threshold where retrieval costs become significant). BigQuery would require redesigning the entire application, not just a component change. Committed use discounts provide savings but less than preemptible VMs and don't offer the same financial upside for variable, short-duration workloads.

3. Your organization is building a real-time analytics dashboard that requires querying 500 GB of historical data plus ingesting 10 GB of new data daily. Queries typically scan 50-100 GB of data and must complete within 5 seconds. You also need to support ad-hoc SQL queries from analysts. Which database is most suitable?

A. Cloud SQL (PostgreSQL) with additional read replicas for query performance
B. Bigtable with custom Java client libraries for analytics queries
C. BigQuery with automated table partitioning and clustering✓ Correct
D. Spanner for its distributed query engine and strong consistency

Explanation

BigQuery (option 2) is correct. It's specifically designed for analytical queries over large datasets (500 GB historical + 10 GB daily ingestion). BigQuery can execute 50-100 GB table scans in seconds with partitioning/clustering optimization. Native SQL support meets the ad-hoc query requirement. Option 0 (Cloud SQL) is inappropriate; it's optimized for OLTP (row-level operations), not OLAP (analytical scans). SQL instances would struggle with 50-100 GB table scans in 5 seconds. Option 1 (Bigtable) is a wide-column store optimized for time-series data and single-row/narrow-range queries, not full-table analytical scans. Option 3 (Spanner) is a distributed relational database for transactional consistency at scale, not optimized for analytical queries at BigQuery's performance level.

4. Your organization runs a customer-facing e-commerce application. The application serves customers globally and must handle both HTTP/HTTPS traffic and TCP traffic on a custom port for a legacy payment processing service. You want to minimize latency for all users worldwide. Which combination of load balancers should you provision? (Select TWO)

A. Global External Application Load Balancer (HTTP/HTTPS) for the web frontend✓ Correct
B. Regional External Application Load Balancer (HTTP/HTTPS) for the web frontend
C. External Proxy Network Load Balancer (TCP/SSL) for the legacy payment service✓ Correct
D. External Passthrough Network Load Balancer for the legacy payment service
E. Internal Application Load Balancer for the web frontend
F. Internal Passthrough Network Load Balancer for the legacy payment service

Explanation

The Global External Application Load Balancer (option A) is correct for HTTP/HTTPS traffic because it is a globally distributed anycast load balancer that routes users to the nearest Google Point of Presence, minimizing latency worldwide. It supports SSL termination, URL-based routing, and Cloud CDN integration. The External Proxy Network Load Balancer (option C) is correct for the legacy TCP service — it operates globally (using Andromeda or Maglev) and can handle TCP traffic on arbitrary ports, which is necessary for the custom-port payment service, while still providing global anycast routing. The Regional External Application Load Balancer (option B) serves only a single region, so global customers would experience higher latency — it does not meet the global low-latency requirement. The External Passthrough Network Load Balancer (option D) is a regional, non-proxied load balancer that preserves client IPs and is suitable for protocols that cannot tolerate proxy termination, but it is regional and would not minimize latency for a global audience. The Internal Application Load Balancer (option E) and Internal Passthrough Network Load Balancer (option F) are internal-only load balancers that do not accept traffic from the public internet, so they are entirely inappropriate for customer-facing services.

5. A global SaaS platform serves customers across North America, Europe, and Asia. The application requires low-latency API responses globally and needs to route traffic to the nearest regional backend. Which load balancing configuration best meets these requirements?

A. Network Load Balancer with anycast routing to regional endpoints
B. Global HTTP(S) Load Balancer with Cloud Backend Services configured for health checking and traffic splitting across regions✓ Correct
C. Regional Internal Load Balancers in each region behind a DNS-based geolocation service (Cloud DNS)
D. Global TCP/UDP Load Balancer with session affinity enabled

Explanation

Global HTTP(S) Load Balancer (option 1) is correct. It's GCP's purpose-built solution for global load balancing, performs health checks, routes to nearest healthy backend based on user geography and latency, and supports advanced traffic management (traffic splitting, canary deployments). Option 0 (Network Load Balancer with anycast) is a Layer 4 solution designed for non-HTTP protocols; anycast is not standard GCP terminology for HTTP routing. Option 2 (Regional ILBs + DNS) is outdated and requires manual DNS management; doesn't automatically route based on latency or health. Option 3 (Global TCP/UDP) is for non-HTTP protocols and session affinity is inappropriate for latency-based routing.

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