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| Certification Vendor: | |
|---|---|
| Exam Name: | Google Cloud Certified Professional - Cloud Architect Exam |
| Exam Number: | Professional-Cloud-Architect |
| Passing Score: | Not officially disclosed |
| Exam Price: | USD 200 (plus tax where applicable) |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 50-60 |
| Exam Duration: | 120 minutes |
| Available Languages: | English, Japanese |
| Exam Format: | Multiple choice, Multiple select, Case study-based questions |
| Related Certifications: | Google Cloud Certified Professional Cloud Security Engineer Google Cloud Certified Associate Cloud Engineer Google Cloud Certified Professional Cloud Developer Google Cloud Certified Professional Cloud Network Engineer |
| Recommended Training: | Google Cloud Professional Cloud Architect Learning Path Google Cloud Architect Exam Guide |
| Exam Registration: | Google Cloud Certification Registration |
| Sample Questions: | Google Professional-Cloud-Architect Sample Questions |
| Exam Way: | Online proctored or onsite proctored at authorized test centers |
| Pre Condition: | No mandatory prerequisites; recommended 3+ years industry experience, including 1+ year designing and managing Google Cloud solutions |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/cloud-architect |
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NEW QUESTION # 294
Your company wants to track whether someone is present in a meeting room reserved for a scheduled meeting. There are 1000 meeting rooms across 5 offices on 3 continents. Each room is equipped with a motion sensor that reports its status every second. The data from the motion detector includes only a sensor ID and several different discrete items of information. Analysts will use this data, together with information about account owners and office locations. Which database type should you use?
Answer: C
Explanation:
Relational databases were not designed to cope with the scale and agility challenges that face modern applications, nor were they built to take advantage of the commodity storage and processing power available today.
NoSQL fits well for:
Developers are working with applications that create massive volumes of new, rapidly changing
data types - structured, semi-structured, unstructured and polymorphic data.
Incorrect Answers:
D: The Blobstore API allows your application to serve data objects, called blobs, that are much larger than the size allowed for objects in the Datastore service. Blobs are useful for serving large files, such as video or image files, and for allowing users to upload large data files.
References: https://www.mongodb.com/nosql-explained
NEW QUESTION # 295
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?
Answer: C
Explanation:
Reference:
https://cloud.google.com/storage/docs/gsutil/commands/lifecycle
NEW QUESTION # 296
You need to reduce the number of unplanned rollbacks of erroneous production deployments in your
company's web hosting platform. Improvement to the QA/Test processes accomplished an 80% reduction.
Which additional two approaches can you take to further reduce the rollbacks? Choose 2 answers.
Answer: C,D
NEW QUESTION # 297
You are developing your microservices application on Google Kubernetes Engine. During testing, you want to validate the behavior of your application in case a specific microservice should suddenly crash. What should you do?
Answer: C
NEW QUESTION # 298
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?
Answer: D
Explanation:
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.
NEW QUESTION # 299
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