Google Professional Cloud Architect (PR000213) Exam Questions Instant Access

Question 1

You are developing a retail application on Vertex AI that uses a large language model to give customers real-time product recommendations. The app uses text, code, images, audio, and video prompts to communicate with customers. You need to minimize the application's latency to ensure a responsive and delightful user experience. What should you do?

AEnable streaming responses from the model to process the output in real time.

BIncrease the temperature parameter.

CAvoid using the max_output_tokens parameter to prevent overly long responses.

DAvoid using system instructions to prevent overly long responses.

Answer : A

According to Google Cloud Vertex AI documentation on Generative AI, Streaming is the recommended technique to reduce perceived latency in user-facing applications. When using large language models (LLMs), generating the full response can take several seconds depending on the complexity and length. By enabling streaming, the model sends the output in chunks (tokens) as they are generated, rather than waiting for the entire sequence to complete. This allows the application UI to display text to the customer progressively, creating a 'real-time' feel and a more responsive experience.

Options B, C, and D do not directly address latency in a reliable or architectural way. Increasing temperature (Option B) affects the randomness and creativity of the output but not the speed of generation. While setting a lower max_output_tokens (Option C) might technically result in shorter (and thus faster) responses, 'avoiding' the parameter altogether gives the model more freedom to produce long, slow responses, which is counterproductive. Similarly, avoiding system instructions (Option D) sacrifices the quality and safety of the model's behavior without providing a predictable latency benefit. For a high-quality 'conversational commerce' experience as defined in the Cymbal Retail case study, streaming is the standard architectural pattern for performance.

Question 2

You are designing me observability strategy for a new microservices application running on Google Kubernetes Engine (GKE) The application consists of multiple services (e.g.. frontend, orders, payments). During load testing, you observe an error in the frontend service's logs, but you cannot find the corresponding logs in the downstream services to investigate the root cause because the logs are not correlated. You need to implement a solution that allows you to follow a single user request across all microservices involved in the transaction. The solution must not require developers to manually add correlation logic to their application code What should you do?

ARequire developers to generate a unique correlation-id at the frontend. and manually add it as a field to every log message in all services.

BConfigure all containers to write logs to STDOUTSTDERR. and then filter logs by pod name and timestamp m the Logs Explorer

CImplement Cloud Trace by ensuring the traceparent header is propagated between microservice calls to link logs to a single trace

DCreate custom metrics in Cloud Monitoring for error counts in each service and correlate incident spikes using a shared dashboard

Answer : C

To follow a user request through a maze of microservices, you need Distributed Tracing.

Cloud Trace: By ensuring the traceparent (W3C standard) header is propagated (Option C), Google Cloud can automatically link logs from the Frontend, Orders, and Payments services.

Zero Manual Effort: Modern libraries (like OpenTelemetry) handle the 'heavy lifting' of extracting and injecting these headers into outgoing requests, satisfying the requirement to avoid manual correlation logic.

Question 3

You need to ensure reliability for your application and operations by supporting reliable task a scheduling for compute on GCP. Leveraging Google best practices, what should you do?

AUsing the Cron service provided by App Engine, publishing messages directly to a message-processing utility service running on Compute Engine instances.

BUsing the Cron service provided by App Engine, publish messages to a Cloud Pub/Sub topic. Subscribe to that topic using a message-processing utility service running on Compute Engine instances.

CUsing the Cron service provided by Google Kubernetes Engine (GKE), publish messages directly to a
message-processing utility service running on Compute Engine instances.

DUsing the Cron service provided by GKE, publish messages to a Cloud Pub/Sub topic. Subscribe to that topic using a message-processing utility service running on Compute Engine instances.

Answer : B

https://cloud.google.com/solutions/reliable-task-scheduling-compute-engine

Question 4

You write a Python script to connect to Google BigQuery from a Google Compute Engine virtual machine. The script is printing errors that it cannot connect to BigQuery. What should you do to fix the script?

AInstall the latest BigQuery API client library for Python

BRun your script on a new virtual machine with the BigQuery access scope enabled

CCreate a new service account with BigQuery access and execute your script with that user

DInstall the bq component for gccloud with the command gcloud components install bq.

Answer : B

The error is most like caused by the access scope issue. When create new instance, you have the default Compute engine default service account but most serves access including BigQuery is not enable. Create an instance Most access are not enabled by default You have default service account but don't have the permission (scope) you can stop the instance, edit, change scope and restart it to enable the scope access. Of course, if you Run your script on a new virtual machine with the BigQuery access scope enabled, it also works

https://cloud.google.com/compute/docs/access/service-accounts

Question 5

Your company is forecasting a sharp increase in the number and size of Apache Spark and Hadoop jobs being run on your local datacenter You want to utilize the cloud to help you scale this upcoming demand with the least amount of operations work and code change. Which product should you use?

AGoogle Cloud Dataflow

BGoogle Cloud Dataproc

CGoogle Compute Engine

DGoogle Container Engine

Answer : B

Google Cloud Dataproc is a fast, easy-to-use, low-cost and fully managed service that lets you run the Apache Spark and Apache Hadoop ecosystem on Google Cloud Platform. Cloud Dataproc provisions big or small clusters rapidly, supports many popular job types, and is integrated with other Google Cloud Platform services, such as Google Cloud Storage and Stackdriver Logging, thus helping you reduce TCO.

Question 6

To reduce costs, the Director of Engineering has required all developers to move their development infrastructure resources from on-premises virtual machines (VMs) to Google Cloud Platform. These resources go through multiple start/stop events during the day and require state to persist. You have been asked to design the process of running a development environment in Google Cloud while providing cost visibility to the finance department. Which two steps should you take? Choose 2 answers

AUse the --no-auto-delete flag on all persistent disks and stop the VM.

BUse the -auto-delete flag on all persistent disks and terminate the VM.

CApply VM CPU utilization label and include it in the BigQuery billing export.

DUse Google BigQuery billing export and labels to associate cost to groups.

EStore all state into local SSD, snapshot the persistent disks, and terminate the VM.

FStore all state in Google Cloud Storage, snapshot the persistent disks, and terminate the VM.

Answer : A, D

https://cloud.google.com/billing/docs/how-to/export-data-bigquery

Question 7

Your company has successfully migrated to the cloud and wants to analyze their data stream to optimize operations. They do not have any existing code for this analysis, so they are exploring all their options. These options include a mix of batch and stream processing, as they are running some hourly jobs and live-processing some data as it comes in. Which technology should they use for this?

AGoogle Cloud Dataproc

BGoogle Cloud Dataflow

CGoogle Container Engine with Bigtable

DGoogle Compute Engine with Google BigQuery

Answer : B

Dataflow is for processing both the Batch and Stream.

Cloud Dataflow is a fully-managed service for transforming and enriching data in stream (real time) and batch (historical) modes with equal reliability and expressiveness -- no more complex workarounds or compromises needed.

Google Cloud Architect Professional Professional Cloud Architect (PR000213) Exam Questions