Google Google Cloud Architect Professional Exam Prep
Google Cloud Certified - Professional Cloud Architect (Page 5 )

Updated On: 15-Sep-2026

You have been asked to select the storage system for the click-data of your company's large portfolio of websites. This data is streamed in from a custom website analytics package at a typical rate of 6,000 clicks per minute. With bursts of up to 8,500 clicks per second. It must have been stored for future analysis by your data science and user experience teams.
Which storage infrastructure should you choose?

  1. Google Cloud SQL
  2. Google Cloud Bigtable
  3. Google Cloud Storage
  4. Google Cloud Datastore

Answer(s): B

Explanation:

The correct answer is B. Google Cloud Bigtable . Here's why:
The scenario describes a high-throughput, write-heavy data ingestion requirement with bursts exceeding 8,500 clicks per second. This volume and velocity of data make traditional relational databases like Google Cloud SQL (option A) unsuitable, as they are not designed for such high write rates.
Google Cloud Storage (option C), while excellent for storing large files, isn't ideal for rapidly ingesting structured data like clickstreams, as it doesn't offer the low-latency access required for real-time analysis. Google Cloud Datastore (option D) is a NoSQL document database, suitable for transactional data, but it wouldn't perform optimally with the extreme throughput mentioned.
Cloud Bigtable is a fully managed, scalable NoSQL database service built for handling massive datasets with low-latency, high-throughput reads and writes. It excels at time-series data and stream processing scenarios like this. It's designed to handle the high ingest rate and burst capacity requirements specified. Its columnar structure and key-value pairing is well-suited for clickstream data where you are often querying for clicks in specific periods or for specific elements, providing highly efficient queries. It’s optimized for throughput at large scale compared to other NoSQL solutions.
The key is the "bursts of up to 8,500 clicks per second", which is a clear indicator that a highly scalable solution optimized for write performance like Bigtable is necessary. Bigtable is also well integrated with other GCP services for data analysis such as BigQuery.
Authoritative Links:
Google Cloud Bigtable Overview: https://cloud.google.com/bigtable/docs/overview Bigtable Use Cases: https://cloud.google.com/bigtable/docs/use-cases Choosing a Storage Option: https://cloud.google.com/storage/docs/choosing-storage-option



You are creating a solution to remove backup files older than 90 days from your backup Cloud Storage bucket. You want to optimize ongoing Cloud Storage spend.
What should you do?

  1. Write a lifecycle management rule in XML and push it to the bucket with gsutil
  2. Write a lifecycle management rule in JSON and push it to the bucket with gsutil
  3. Schedule a cron script using gsutil ls "lr gs://backups/** to find and remove items older than 90 days
  4. Schedule a cron script using gsutil ls "l gs://backups/** to find and remove items older than 90 days and schedule it with cron

Answer(s): B

Explanation:

The most efficient and cost-effective approach to managing object lifecycle in Google Cloud Storage is by utilizing lifecycle management policies. Option B, which involves creating a lifecycle rule in JSON format and applying it to the bucket using gsutil , is the correct solution. Cloud Storage lifecycle management allows you to automatically delete objects or transition them to different storage classes (like Nearline or Coldline) based on age or other criteria, directly within the storage service. This eliminates the need for custom scripts, reducing operational overhead and potential errors. JSON is the preferred format for defining these rules due to its structured and readable nature. gsutil provides the necessary command-line interface to implement these policies. In contrast, option A proposes using XML, which is a less common format for lifecycle rules in Google Cloud Storage. Options C and D involve creating custom cron scripts to identify and delete files, which is a less optimal solution compared to lifecycle management. Cron jobs require server management and could be inefficient for large storage buckets. Additionally, gsutil ls -lr and gsutil ls -l do not directly support filtering by age, making implementation complex and prone to errors. Lifecycle management within Cloud Storage is more efficient because it's server-side and doesn't require object listing for deletion, resulting in cost savings and faster processing.
Authoritative Links:
Google Cloud Storage Lifecycle Management: https://cloud.google.com/storage/docs/lifecycle gsutil lifecycle command: https://cloud.google.com/storage/docs/gsutil/commands/lifecycle JSON for lifecycle rules: https://cloud.google.com/storage/docs/json/v1/objects/delete (Example is object deletion but the principles of using JSON for rule definition applies.)



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?

  1. Google Cloud Dataflow
  2. Google Cloud Dataproc
  3. Google Compute Engine
  4. Google Kubernetes Engine

Answer(s): B

Explanation:

Google Cloud Dataproc is the most suitable solution because it's a fully managed, highly scalable service for running Apache Spark and Hadoop ecosystems jobs. It simplifies cluster creation, management, and scaling, significantly reducing operational overhead. You can quickly spin up Dataproc clusters to handle the increased workload and scale them down when demand decreases, optimizing cost efficiency.
Dataflow, while excellent for data pipelines, requires rewriting your Spark/Hadoop jobs to fit its programming model, conflicting with the requirement of minimal code change. Compute Engine would necessitate manual cluster configuration and management, significantly increasing operational burden. Kubernetes Engine can orchestrate containers running Spark/Hadoop, but it also demands more operational expertise and configuration than Dataproc, as you'll need to handle the individual Spark/Hadoop components yourself.
Dataproc offers seamless integration with other Google Cloud services, like Cloud Storage and BigQuery. It's designed to minimize code changes, allowing you to migrate your existing Spark and Hadoop jobs with minimal modifications, addressing the prompt's core constraint. This feature allows for faster migration and reduced risk of introducing errors during the cloud adoption process. Dataproc also automates administrative tasks like cluster configuration, monitoring, and security patching.
Therefore, Dataproc directly addresses the need for scaling Spark/Hadoop jobs with the least operational work and code change, making it the optimal choice.
Further research:
Google Cloud Dataproc Documentation: https://cloud.google.com/dataproc/docs Dataproc vs. Dataflow: https://cloud.google.com/dataproc/docs/concepts/choosing-data-processing-service



The database administration team has asked you to help them improve the performance of their new database server running on Google Compute Engine. The database is for importing and normalizing their performance statistics and is built with MySQL running on Debian Linux. They have an n1-standard-8 virtual machine with 80 GB of SSD persistent disk.
What should they change to get better performance from this system?

  1. Increase the virtual machine's memory to 64 GB
  2. Create a new virtual machine running PostgreSQL
  3. Dynamically resize the SSD persistent disk to 500 GB
  4. Migrate their performance metrics warehouse to BigQuery
  5. Modify all of their batch jobs to use bulk inserts into the database

Answer(s): C

Explanation:

The correct answer is C. Dynamically resize the SSD persistent disk to 500 GB.
Here's the justification:
The question focuses on improving database performance on a Compute Engine VM.
While other options might offer benefits in different scenarios, the most direct and impactful change for the described situation is to increase the SSD persistent disk size. Database performance, especially for write-heavy operations like data imports and normalization, is heavily dependent on disk I/O. A larger SSD disk not only provides more storage space but also improves I/O performance due to the way storage devices handle data blocks internally. Specifically, increasing the disk size often provides an increase in available I/O operations per second (IOPS), which directly influences how quickly the database can write new data and thus speeds up the import and normalization tasks. The initial 80GB disk size might be a bottleneck given the import and processing workload, therefore resizing to a 500 GB disk can alleviate this performance constraint. It is also a practical solution since the other choices introduce more drastic changes like switching the database system or completely migrating the workload.
While adding memory (option A) can help with in-memory caching, it's less crucial if the bottleneck is disk I/O. PostgreSQL (option B) may be a suitable replacement but is a larger change, and BigQuery (option D) is for analytics, not the current transactional use case. Finally, bulk inserts (option E) are a best practice but do not solve the immediate performance issue related to disk limitations. Persistent disks can be dynamically resized without downtime, making this a quick and non-disruptive way to significantly improve the performance of this database.
Authoritative Links:
Google Cloud Persistent Disk Performance: https://cloud.google.com/compute/docs/disks/performance -This document discusses factors influencing persistent disk performance, emphasizing the impact of disk size on IOPS and throughput. Resizing Persistent Disks: https://cloud.google.com/compute/docs/disks/resize-persistent-disk - This documentation provides details on dynamically resizing persistent disks, a key factor for efficient operations.



You want to optimize the performance of an accurate, real-time, weather-charting application. The data comes from 50,000 sensors sending 10 readings a second, in the format of a timestamp and sensor reading.
Where should you store the data?

  1. Google BigQuery
  2. Google Cloud SQL
  3. Google Cloud Bigtable
  4. Google Cloud Storage

Answer(s): C

Explanation:

The correct answer is C. Google Cloud Bigtable . Here's why:
Bigtable is a fully managed, wide-column NoSQL database service ideal for large-scale, low-latency applications that require high read and write throughput, making it suitable for real-time data ingestion and retrieval. The weather-charting application described requires ingesting a massive stream of data (50,000 sensors * 10 readings/second = 500,000 readings/second) and demands quick access for charting. Bigtable's design, optimized for such scenarios, efficiently handles this high volume and velocity of data. Its schema-less nature allows for flexible handling of time-series data, which is a core characteristic of sensor readings.
While BigQuery (A) excels at analytical queries over large datasets, it isn't optimized for real-time ingestion and low-latency queries; loading such a high frequency of data would be cumbersome. Cloud SQL (B) is suitable for relational data and not the ideal choice for such high velocity and scale, especially with time series data. Cloud Storage (D) is a durable object storage and not designed for low-latency access and high read-write traffic like a database.
Bigtable's distributed architecture, automated scaling, and built-in support for time-series data make it the most fitting option for the given use case. Its low-latency read and write capabilities provide the performance required for real-time charting.
For further information, refer to the following resources:
Google Cloud Bigtable Documentation: https://cloud.google.com/bigtable/docs When to Use Bigtable: https://cloud.google.com/bigtable/docs/choosing-a-storage-option Bigtable Use Cases: https://cloud.google.com/bigtable/docs/use-cases



Viewing page 5 of 98
Viewing questions 21 - 25 out of 480 questions


Post your Comments and Discuss Google Google Cloud Architect Professional exam prep with other Community members:

AI Tutor AI Tutor 👋 I’m here to help!