PCD Practice Test 24 — 15 Questions

Question 1

A developer wants to quickly test changes to a containerized web application that will run on Cloud Run, without building and deploying a new container. Which approach should they use?

Accepted Answer

Run locally with Docker. Running the container locally with Docker lets the developer iterate on code and rebuild the image on their workstation without pushing to a registry or creating a new Cloud Run revision, giving the fastest feedback loop. Cloud Run's local development workflow (e.g., docker run with the same port and environment variables) mirrors the runtime environment closely enough for quick testing. This avoids the build-and-deploy cycle entirely.

Answer

Deploy to a staging Cloud Run service

Answer

Use traffic splitting to test a new revision

Answer

Use Cloud Run for Anthos

Question 2

A company uses Cloud SQL for PostgreSQL with a cross-region replica for disaster recovery. They need to perform a regional failover with minimal data loss. What configuration is required?

Accepted Answer

Promote the cross-region replica to a primary instance. For a Cloud SQL for PostgreSQL cross-region replica used for disaster recovery, a regional failover is performed by promoting the cross-region replica to a standalone primary instance. This is the documented failover mechanism: the replica becomes writable and takes over as the new primary, allowing the application to resume operations in the DR region with minimal data loss (bounded by replication lag).

Answer

Use Database Migration Service instead

Answer

Take a manual backup of the primary and restore to the replica

Answer

Enable synchronous replication

Question 3

A company is migrating from Oracle to Cloud SQL for PostgreSQL. They have a table with a column of type RAW(16). Which PostgreSQL data type should they use?

Accepted Answer

BYTEA. Oracle's RAW(16) is a variable-length binary data type that stores up to 16 bytes of raw binary data. PostgreSQL's equivalent binary data type is BYTEA, which stores variable-length binary strings. Migrating RAW(16) to BYTEA preserves the binary semantics and length.

Answer

BYTES

Answer

UUID

Answer

TEXT

Question 4

A developer wants to view real-time logs from a running application on Compute Engine. Which two methods can they use to stream logs? (Choose two.)

Accepted Answer

Using the Logs Explorer's 'Stream logs' feature. Option A is correct because the Logs Explorer in Cloud Logging provides a 'Stream logs' toggle that displays log entries in real time as they are ingested, which directly satisfies the requirement to view live logs from a Compute Engine application. Option C is correct because the gcloud logging tail command streams log entries live to the terminal by using the Cloud Logging API, allowing real-time viewing of logs from Compute Engine resources. Option B is not appropriate as a general Cloud Logging method because journalctl -f only works if the application logs to systemd's journal on that specific VM and requires SSH access, rather than streaming logs through Cloud Logging. Option D is incorrect because Cloud Monitoring's metrics explorer displays time-series metrics, not log entries. Option E is incorrect because gcloud app logs tail streams logs for App Engine applications, not Compute Engine instances.

Answer

Using gcloud compute ssh and running journalctl -f

Answer

Using Cloud Monitoring's metrics explorer

Answer

Using gcloud app logs tail

Question 5

A company is building a real-time analytics application on Google Cloud that ingests data from thousands of IoT devices. The data must be processed with sub-second latency and stored in a time-series database for querying. Which combination of services provides the best scalability and availability?

Accepted Answer

Cloud Pub/Sub, Cloud Dataflow, Cloud Bigtable. Cloud Bigtable is a fully managed, scalable NoSQL database designed for large analytical and operational workloads, offering sub-10ms latency for time-series data. Combined with Cloud Pub/Sub for ingesting high-throughput IoT data and Cloud Dataflow for stream processing, this combination provides the best scalability and availability for real-time analytics with sub-second latency requirements.

Answer

Cloud Pub/Sub, Cloud Dataflow, Cloud Datastore

Answer

Cloud Pub/Sub, Cloud Functions, Cloud SQL

Answer

Cloud Pub/Sub, Cloud Dataflow, Cloud Storage

Question 6

A company is deploying a microservices architecture on Google Kubernetes Engine (GKE). They need to monitor inter-service latency and error rates. Which set of Google Cloud services should they use to collect and visualize these metrics?

Accepted Answer

Cloud Monitoring, Cloud Logging, and Cloud Trace. Cloud Monitoring collects metrics like latency and error rates, Cloud Logging aggregates logs for deeper analysis, and Cloud Trace provides distributed tracing to track requests across microservices. Together, they enable end-to-end observability of inter-service performance in GKE, with Cloud Monitoring visualizing the data via dashboards and alerts.

Answer

Cloud Trace, Cloud Debugger, and Cloud Profiler

Answer

Cloud Logging, Cloud Run, and Cloud Build

Answer

Cloud Monitoring, Cloud Functions, and Cloud Pub/Sub

Question 7

A company runs a stateful application on Compute Engine with local SSDs. They want high durability. Which approach should they use?

Accepted Answer

Use persistent disks instead of local SSDs for automatic replication. Local SSDs are ephemeral and data is lost when the VM is stopped or terminated. Persistent disks, by contrast, automatically replicate data within the same zone (or across zones if using regional persistent disks), providing high durability. Option D correctly identifies that switching to persistent disks is the appropriate approach for durability, as local SSDs lack built-in redundancy.

Answer

Replicate data to another zone using synchronous replication

Answer

Use a RAID 1 array across multiple local SSDs

Answer

Take regular snapshots of local SSDs

Question 8

A data engineer needs to perform complex transformations on streaming data as it moves from Cloud Pub/Sub to Cloud Bigtable. Which service should they use for this purpose?

Accepted Answer

Cloud Dataflow. Dataflow (Apache Beam) is the recommended service for stream processing with complex transformations. It can read from Pub/Sub, apply transformations, and write to Bigtable. Cloud Functions are simpler and not suited for complex ETL.

Answer

Dataproc

Answer

Cloud Run

Answer

Cloud Functions

Question 9

A team wants to monitor a web application's uptime from multiple locations. Which THREE Google Cloud monitoring features should they use?

Accepted Answer

Uptime checks. Uptime checks (B) are the correct feature for probing a web application's availability from multiple global locations, since a single uptime check can be configured with multiple regions and validates HTTP/HTTPS/TCP responses. Dashboards (D) are correct because they let the team visualize uptime check results and related metrics in one place for ongoing monitoring. Alerting policies (E) are correct because they trigger notifications when uptime checks fail or when metrics breach thresholds, which is essential for proactive uptime monitoring. Log-based metrics (A) are not the right fit here because they derive metrics from log entries rather than actively probing endpoint availability. Error Reporting (C) is also not appropriate because it aggregates and groups application exceptions and errors, not external uptime or availability from multiple locations.

Answer

Log-based metrics

Answer

Error Reporting

Question 10

You are migrating a PostgreSQL database to Cloud SQL. The database currently uses custom functions and PostGIS extensions. The target must support these extensions. Which database version and tier should you choose?

Accepted Answer

Cloud SQL for PostgreSQL 15 with standard machine type. Cloud SQL for PostgreSQL supports PostGIS and custom functions. The machine type depends on performance needs, but any tier works as long as PostgreSQL is chosen.

Answer

Cloud SQL for MySQL 8.0 with highmem machine type

Answer

Cloud Spanner with PostgreSQL dialect

Answer

Cloud SQL for SQL Server 2019 with lightweight machine type

Question 11

Which TWO statements about deploying applications on Google Kubernetes Engine (GKE) are correct?

Accepted Answer

HorizontalPodAutoscaler can use custom metrics from Cloud Monitoring.. Option A is correct because GKE's HorizontalPodAutoscaler can scale workloads based on custom metrics exported to Cloud Monitoring (via the custom metrics adapter / Stackdriver adapter), not just CPU or memory. Option D is correct because a PodDisruptionBudget defines the minimum available or maximum unavailable pods, which constrains voluntary disruptions such as node drains during node repair or upgrades, helping maintain availability. Option B is not correct as stated because Kubernetes Secrets are only base64-encoded by default; encryption at rest requires configuring a KMS key (application-layer encryption) on GKE. Option C is not correct because a zonal GKE cluster uses zonal persistent disks by default; regional PDs are used with regional clusters for higher availability. Option E is not correct because a Deployment can be exposed externally via a Service of type LoadBalancer or NodePort, and an Ingress is only one optional approach, not a requirement.

Answer

Kubernetes Secrets are encrypted at rest by default.

Answer

A zonal GKE cluster automatically uses regional persistent disks for high availability.

Answer

To expose a Deployment externally, you must create an Ingress resource.

Question 12

A company is deploying a new web application on Google Cloud and needs a managed relational database with PostgreSQL compatibility. The database must support automatic failover within 60 seconds in case of a zone outage. Which database deployment option should they choose?

Accepted Answer

Cloud SQL regional HA configuration. Cloud SQL regional HA configuration provisions a primary and standby instance in different zones within the same region, with automatic failover typically completing in under 60 seconds. It supports PostgreSQL and provides the managed relational database with zone-outage resilience the company requires. This is the standard Google Cloud answer for regional HA on a managed relational database.

Answer

Cloud SQL single zone with automatic storage increase

Answer

Spanner multi-region configuration

Answer

AlloyDB with a read pool of 2 instances

Question 13

You are designing a Cloud Spanner schema with a parent table 'Customers' and a child table 'Orders'. To optimize read performance for retrieving a customer and their orders in a single split, you should use:

Accepted Answer

Interleaved tables with Customers as the parent. Interleaved tables store child rows with the parent row in the same split, enabling efficient joins and low-latency reads.

Answer

A secondary index on Orders.customer_id

Answer

A foreign key constraint from Orders to Customers

Answer

Use a single table with repeated fields

Question 14

You need a caching layer for a web application that handles high read traffic. The cache must support pub/sub messaging for cache invalidation across multiple servers. Which service should you use?

Accepted Answer

Memorystore for Redis. Memorystore for Redis is the correct choice because Redis natively supports pub/sub messaging, which enables cache invalidation notifications across multiple application servers. This allows each server to subscribe to channels and receive real-time invalidation messages, ensuring cache consistency in a distributed environment. Memcached, in contrast, lacks pub/sub capabilities, making it unsuitable for this requirement.

Answer

Cloud Bigtable

Answer

Memorystore for Memcached

Answer

Firestore

Question 15

An organization needs a globally distributed relational database with strong consistency across regions and the ability to scale horizontally without manual sharding. Which Google Cloud database should they choose?

Accepted Answer

Cloud Spanner. Cloud Spanner is Google Cloud's globally distributed, horizontally scalable relational database that provides external consistency (strong consistency) across regions and automatic sharding without manual intervention. It is the only option that combines relational semantics, global distribution, strong consistency, and transparent horizontal scaling.

Answer

Firestore

Answer

Cloud SQL for MySQL

Answer

Cloud Bigtable