Q: 1
An organization recently launched a virtual customer support agent, generating vast amounts of text
and speech data.
Why should they use a cloud data warehouse to interpret this data?
Options
Discussion
Probably B here, since the question is about analyzing both structured and unstructured text/speech data at scale. A is tempting but visualization isn’t the main point. Anyone see a reason to pick A over B?
B tbh
Its B, but does the question mean by "interpret" the analysis or only storing data? If focus was on security, maybe C.
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Q: 2
Your organization is moving an application to Google Cloud. As part of that effort, it needs to migrate
the application’s working database from another cloud provider to Cloud SQL. The database runs on
the MySQL engine. The migration must cause minimal disruption to users. Data must be secured
while in transit.
Which should your organization use?
Options
Discussion
Option C is the go-to here. Database Migration Service is built for MySQL migrations and supports minimal downtime plus encrypts data in transit. Seen similar advice in official guides, almost always points to DMS for this use case. Anyone see this handled differently?
Database Migration Service (C) really fits here since it's designed for MySQL migrations with minimal user impact and secures the connection. Batch insert and BQ transfer can’t meet those downtime or security needs. I'm pretty sure DMS is what Google recommends for this, but open to correction if someone’s done it differently.
Does "minimal disruption" mean zero downtime, or is a short maintenance window acceptable? That could change if Database Migration Service (C) is the clear choice, since it supports continuous replication for near-zero downtime migration.
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Q: 3
Your organization needs to allow a production job to have access to a BigQuery dataset. The
production job is running on a Compute Engine instance that is part of an instance group.
What should be included in the IAM Policy on the BigQuery dataset?
Options
Discussion
B tbh, since the project owns the instance and inherits a lot of permissions by default. I always thought project-level roles covered resources in cases like this. Not 100% on it but seems logical, yeah?
Option C pops up on a ton of GCP questions. You grant access to the service account since that's what actually runs on the instance and hits BigQuery, not the instance or group directly. I think that's the usual approach but let me know if anyone's seen it asked some other way.
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Q: 4
You are currently managing workloads running on Windows Server for which your company owns the
licenses. Your workloads are only needed during working hours, which allows you to shut down the
instances during the weekend. Your Windows Server licenses are up for renewal in a month, and you
want to optimize your license cost.
What should you do?
Options
Discussion
Its C, had something like this in a mock exam.
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Q: 5
A Customer has their current SAP systems using Microsoft SQL Server as the Database. They are
migrating to Google Cloud and also preparing to later migrate to the latest ver-sion of SAP. The entire
IT team is being directed to focus on the migration to the new ver-sion of SAP. The new version of
SAP does not use Microsoft SQL Server as the Database, Any but the most critical IT management
tasks are being deprioritized, How should they migrate their current database to Google Cloud?
Options
Discussion
D imo. It’s managed and supports SQL Server, so less overhead while they prep for the SAP upgrade.
Nah, BigQuery (C) is a trap, it's not the right fit for transactional SQL Server workloads. D makes more sense here given the managed aspect. Pretty sure about that but open to other views.
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Q: 6
Your organization needs to categorize objects in a large group of static images using machine
learning. Which Google Cloud product or service should your organization use?
Options
Discussion
C
I think it's Cloud Vision API since that's the one for image object detection. BigQuery ML and AutoML Tables are more for structured data, not images. Not totally sure if AutoML Video could do images too but pretty sure C fits best here. Agree?
I think it's Cloud Vision API since that's the one for image object detection. BigQuery ML and AutoML Tables are more for structured data, not images. Not totally sure if AutoML Video could do images too but pretty sure C fits best here. Agree?
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Q: 7
You are a program manager within a Software as a Service (SaaS) company that offers rendering
software for animation studios. Your team needs the ability to allow scenes to be scheduled at will
and to be interrupted at any time to restart later. Any individual scene rendering takes less than 12
hours to complete, and there is no service-level agreement (SLA) for the completion time for all
scenes. Results will be stored in a global Cloud Storage bucket. The compute resources are not bound
to any single geographical location. This software needs to run on Google Cloud in a cost-optimized
way.
What should you do?
Options
Discussion
I think A makes more sense here, since preemptible instances are much cheaper and work well when your jobs can handle interruptions. The workload isn't tied to SLAs and each scene is under 12 hours, so preemptibles fit perfectly. Not totally sure though if D would ever beat A unless there's a strict job concurrency need. Agree?
D imo. Starting more instances with fewer vCPUs should let you parallelize the scenes and keep jobs flexible for interruption, which seems to fit. A similar scenario popped up in some practice tests, but official docs recommend preemptibles more often. Anyone else stick with D after reviewing guides?
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Q: 8
Your Coustomer's Organization has decided to move to the cloud. They currently run VMs on-
promise but their goal on Google cloud is to run containers, primarily on Google Kuber-nete's Engine.
They have a lease for their private data center for another year that they have already paid for. What
could be strategy they could adopt in migrating?
Options
Discussion
Probably D, not B.
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Q: 9
Your organization wants to be sure that is expenditures on cloud services are in line with the budget.
Which two Google Cloud cost management features help your organization gain greater visibility into
its cloud resource costs? (Choose two.)
Options
Discussion
B A and B are the best picks here. Billing dashboards show actual spend and resource labels let you break down costs by team or project. D is tempting but more about policy than visibility, right? Pretty sure these are what Google expects.
D imo A and B fit best here. Billing dashboards (A) give you a real-time overview of where your spend is going, so you can spot overages or unexpected charges quickly. Resource labels (B) let you tag resources to break down costs by project or team in reports-super useful for tracking. Not 100% sure though if there's a tricky option hiding, but A and B match what I've seen in similar practice sets.
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Q: 10
Your organization wants to predict the behavior of visitors to its public website. To do that, you have
decided to build a machine learning model. Your team has database-related skills but only basic
machine learning skills, and would like to use those database skills.
Which Google Cloud product or feature should your organization choose?
Options
Discussion
Option A Saw a similar question in practice sets, pretty sure BigQuery ML lets you run ML models using SQL so database folks can stick to what they know.
Its D, since Cloud SQL uses standard SQL for queries and should fit a database team's skills.
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