Google Cloud Digital Leader (CDL) Exam Dumps with Solutions

A global retail company needs to centralize ingestion of custom application logs from 3 GKE clusters (total 150 microservices), 200 Compute Engine VMs, and 12 Cloud Run services. Requirements: accept JSON-formatted payloads with custom fields and resource labels; create log-based metrics for alerting; query and manage retention in a single place; avoid third-party collectors beyond the Google Ops Agent. Which Google Cloud tool should the company use?

A media-streaming company is migrating a 12-node analytics backend to a fully managed relational database service on Google Cloud that prohibits custom agents and targets a 99.95% monthly SLA; to reduce operational toil without writing maintenance scripts, which routine system-maintenance task will the managed platform perform automatically across all instances?

Your micromobility startup runs 8 telemetry-processing VMs on Windows Server Standard in a colocation facility. These workloads are needed only from 08:00 to 18:00, Monday–Friday, and you shut them down nights and weekends (about 65% idle time). Your Windows Server licenses expire in 30 days, and you want to minimize ongoing license spend while keeping the ability to stop instances when idle. What should you do?

A regional retail company is launching an inventory forecasting service on Google Cloud; finance mandates a single billing account with project-level budgets set to $18,000/month for production and $3,500/month for development, and only 8 engineers should have any permissions in development while none of them may access production; you must ensure that development workloads cannot affect production in terms of IAM, quotas, or network reachability. What should you do to meet these requirements while keeping spend tracking simple?

A regional logistics company needs to build two custom AI solutions—package damage detection from images (2.5 million labeled photos) and claim text classification (600,000 labeled records)—and go live in 4 weeks. They require a fully managed, end-to-end platform that supports data preparation, GPU training, automated hyperparameter tuning, pipeline orchestration, online and batch prediction with scalable HTTPS endpoints, model registry, and monitoring, and integrates easily with BigQuery, without stitching together multiple low-level services. Which Google Cloud product should they choose?