Free Google Cloud-Digital-Leader Actual Exam Questions

B. Projects are the real resource containers, and folders are just for grouping. Having folders by department and projects by environment keeps access control simple and resource boundaries clear.

B seems right since folders are meant to group projects, not the other way around. This keeps environment projects isolated but still under the right department umbrella.

C Using AutoML could be a good fit since it handles the model building without deep data science skills, and it works well with labeled data that they likely have from past rentals.

B/D? If they have SQL skills, BigQuery ML (B) is straightforward but only if data’s in BigQuery. Otherwise, using pre-trained APIs (D) might save hassle since no model building is needed.

Option A, better to organize by teams for clearer permissions and management.

A. Setting up folders per team makes sense to organize projects better and control access. It’s a cleaner structure than keeping everything flat, especially as they scale.

Option B, Firebase Hosting is mainly for static files, not actual dynamic content.

B, hosting handles static files, dynamic stuff is done via functions or backend.

Maybe B, since routing is essential to reach Google API IPs privately.

B/D? I agree the routing part is crucial, so B has to be right. But D isn’t totally off either if you consider that routing alone might not be enough without proper firewall rules. The question says “network requirements,” and routes are clearly one, but maybe firewall rules count too? Since A is about enabling APIs automatically, which doesn’t happen here, C is out. I’d stick with B because of the routes for sure, but keep in mind that just having routes might not cover all network aspects fully.

Option A makes sense since service accounts can be assigned roles regardless of LDAP syncing.

It’s C. Assuming the LDAP group is synced to a Google Group, assigning the IAM role directly to that Google Group in the project’s IAM policy is the cleanest way to manage access. Options A and B focus on service accounts, which isn’t what the question suggests for user access control. D doesn’t make much sense since folders aren’t named after LDAP groups and don’t handle access that way. The key is that syncing LDAP groups to Google Groups lets you manage access via IAM roles on the group itself, so C fits best here.

B, since it's the only one that actually logs read accesses comprehensively.

This one feels like B for sure, since the question is about recording all read requests. The Data Access audit logs are meant exactly for that, capturing who accessed what data. Options A, C, and D don’t really handle tracking every read. So B looks like the only choice that fits the requirement for detailed access logs.

Why not Cloud Logging (D)? It collects logs from containers, so if the issue shows up in runtime logs, searching there might help find the problem. Direct source search isn’t always possible.

Makes sense that only Cloud Console (A) lets you directly search source code across containers.

Maybe B makes sense since assigning roles directly to employees could be faster if the IAM roles aren’t pre-defined yet. It avoids extra setup time compared to creating department-based roles.

A makes more sense since managing by department roles is simpler and less error-prone.

I agree that D sounds off since data warehouses don’t usually turn structured into unstructured data. But A also seems unlikely because while some warehouses support dashboards, “natively visualizing both text and speech data in real time” isn’t their core strength. The question emphasizes why a cloud warehouse suits this scenario, so it’s really about ingesting and analyzing huge amounts of mixed data. Wouldn’t that make B the best fit? C feels unrelated since data security between environments isn’t the main problem here. Anyone else think the real focus is on scale and variety rather than v

B/D? Warehouses aren’t typically used to convert data types, so D feels off. The key is probably handling both data forms at scale, so B still clicks better from a capability angle.

D makes the most sense here because Dataflow is fully managed and designed for continuous data processing without any need to set up or maintain infrastructure. The other options all involve managing clusters or VMs, which the question rules out. Even if the data was batch, Dataflow handles that too, so it’s the most flexible and hassle-free choice.

It’s D for sure since Dataflow handles both streaming and batch without you worrying about infrastructure. The continuous aspect rules out anything needing cluster setup or VM management.

It’s B since AI Hub was created for exactly sharing reusable AI components.

D imo, Document AI is way too narrow, and Recommendations AI just handles personalized product suggestions. B fits best since AI Hub was designed exactly for sharing reusable AI components.

Probably D, since it reduces risk by limiting access to only what's needed.

B imo, because restricting on-premises software access with special permission also reflects limiting access, but it’s less relevant to cloud specifically compared to D’s focus on job-based cloud resource access.

B imo, only option with strong global consistency and seamless scaling.

It’s B for sure, only one that does global strong consistency with auto-sharding.

B sounds right since compute-optimized VMs boost raw CPU power without code changes.

It’s B because compute-optimized machines boost CPU performance directly without needing app changes.