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Question 250
A small firm wants generative writing in existing office apps today. Which benefit is more direct than building its own service?
- Enterprise security and governance
- Integrated AI ecosystem
- Prebuilt application capabilities
- Scalable cloud infrastructure
Explanation
Correct Answer
C:Prebuilt experiences avoid much custom implementation. Prebuilt AI features can shorten time to value without building every model or workflow from scratch.
Incorrect Answers
A:Enterprise controls address access, privacy, compliance and safe deployment at organizational scale. This does not directly meet the case requirement to start AI-assisted work with existing software.
B:Google connects models, infrastructure, platform services and productivity or customer-facing applications. This does not directly meet the case requirement to start AI-assisted work with existing software.
D:Managed cloud capacity can support model workloads across changing traffic and compute requirements. This does not directly meet the case requirement to start AI-assisted work with existing software.
Question 251
An infrastructure lead examines Google's purpose-built silicon for large neural workloads. Which accelerator type is being considered?
- Cloud data centers
- Managed model inference
- Autoscaling compute
- Tensor Processing Units (TPUs)
Explanation
Correct Answer
D:Custom TPUs accelerate suitable ML computations. TPUs are Google's custom-designed accelerators optimized for machine-learning tensor workloads.
Incorrect Answers
A:Data centers provide physical sites, power, networking and hardware supporting regional cloud services. This does not directly meet the case requirement to select Google's purpose-built ML accelerators.
B:Managed inference lets a provider operate model-serving infrastructure on behalf of applications. This does not directly meet the case requirement to select Google's purpose-built ML accelerators.
C:Autoscaling adjusts resources in response to usage demand, subject to quotas and design. This does not directly meet the case requirement to select Google's purpose-built ML accelerators.
Question 252
A team needs familiar parallel accelerators for models supported by common GPU software stacks. Which hardware class is relevant?
- Autoscaling compute
- Tensor Processing Units (TPUs)
- Cloud data centers
- Graphics Processing Units (GPUs)
Explanation
Correct Answer
D:GPU compute supports many established AI frameworks. GPUs provide broadly supported parallel processing for training and serving many AI models.
Incorrect Answers
A:Autoscaling adjusts resources in response to usage demand, subject to quotas and design. This does not directly meet the case requirement to run GPU-compatible training or inference workloads.
B:TPUs are Google's custom-designed accelerators optimized for machine-learning tensor workloads. This does not directly meet the case requirement to run GPU-compatible training or inference workloads.
C:Data centers provide physical sites, power, networking and hardware supporting regional cloud services. This does not directly meet the case requirement to run GPU-compatible training or inference workloads.
Question 253
An architect wants a coordinated AI infrastructure system rather than buying isolated chips. Which Google concept encompasses compute, fabric and storage?
- Tensor Processing Units (TPUs)
- AI Hypercomputer architecture
- Autoscaling compute
- Graphics Processing Units (GPUs)
Explanation
Correct Answer
B:AI Hypercomputer combines complementary infrastructure components. AI Hypercomputer integrates performance-oriented compute, networking, storage and software for AI workloads.
Incorrect Answers
A:TPUs are Google's custom-designed accelerators optimized for machine-learning tensor workloads. This does not directly meet the case requirement to optimize the entire AI computing system.
C:Autoscaling adjusts resources in response to usage demand, subject to quotas and design. This does not directly meet the case requirement to optimize the entire AI computing system.
D:GPUs provide broadly supported parallel processing for training and serving many AI models. This does not directly meet the case requirement to optimize the entire AI computing system.
Question 254
A compliance review asks which physical facilities host cloud AI infrastructure within permitted regions. What layer is relevant?
- Cloud data centers
- Managed model inference
- Tensor Processing Units (TPUs)
- Graphics Processing Units (GPUs)
Explanation
Correct Answer
A:Cloud data centers provide underlying physical hosting. Data centers provide physical sites, power, networking and hardware supporting regional cloud services.
Incorrect Answers
B:Managed inference lets a provider operate model-serving infrastructure on behalf of applications. This does not directly meet the case requirement to identify physical locations of cloud infrastructure.
C:TPUs are Google's custom-designed accelerators optimized for machine-learning tensor workloads. This does not directly meet the case requirement to identify physical locations of cloud infrastructure.
D:GPUs provide broadly supported parallel processing for training and serving many AI models. This does not directly meet the case requirement to identify physical locations of cloud infrastructure.
Question 255
An API's traffic doubles every holiday and resources should adjust without manual provisioning. Which operational capability helps?
- Autoscaling compute
- Tensor Processing Units (TPUs)
- Managed model inference
- Graphics Processing Units (GPUs)
Explanation
Correct Answer
A:Autoscaling responds to load within configured constraints. Autoscaling adjusts resources in response to usage demand, subject to quotas and design.
Incorrect Answers
B:TPUs are Google's custom-designed accelerators optimized for machine-learning tensor workloads. This does not directly meet the case requirement to change serving capacity based on demand.
C:Managed inference lets a provider operate model-serving infrastructure on behalf of applications. This does not directly meet the case requirement to change serving capacity based on demand.
D:GPUs provide broadly supported parallel processing for training and serving many AI models. This does not directly meet the case requirement to change serving capacity based on demand.
Question 256
A startup wants a model endpoint without maintaining accelerator drivers and server images. Which service model is suitable?
- Autoscaling compute
- Managed model inference
- Tensor Processing Units (TPUs)
- AI Hypercomputer architecture
Explanation
Correct Answer
B:Managed serving reduces hardware operations required from customers. Managed inference lets a provider operate model-serving infrastructure on behalf of applications.
Incorrect Answers
A:Autoscaling adjusts resources in response to usage demand, subject to quotas and design. This does not directly meet the case requirement to consume hosted inference instead of operating servers.
C:TPUs are Google's custom-designed accelerators optimized for machine-learning tensor workloads. This does not directly meet the case requirement to consume hosted inference instead of operating servers.
D:AI Hypercomputer integrates performance-oriented compute, networking, storage and software for AI workloads. This does not directly meet the case requirement to consume hosted inference instead of operating servers.
Question 257
A Google Cloud proposal highlights accelerators designed by Google itself. Which hardware category is that?
- Tensor Processing Units (TPUs)
- Managed model inference
- Graphics Processing Units (GPUs)
- Autoscaling compute
Explanation
Correct Answer
A:TPUs are Google's own ML-focused processors. TPUs are Google's custom-designed accelerators optimized for machine-learning tensor workloads.
Incorrect Answers
B:Managed inference lets a provider operate model-serving infrastructure on behalf of applications. This does not directly meet the case requirement to name a Google-designed neural accelerator.
C:GPUs provide broadly supported parallel processing for training and serving many AI models. This does not directly meet the case requirement to name a Google-designed neural accelerator.
D:Autoscaling adjusts resources in response to usage demand, subject to quotas and design. This does not directly meet the case requirement to name a Google-designed neural accelerator.
Question 258
A research team needs NVIDIA-compatible accelerators for a supported open-source pipeline. Which compute category applies?
- Cloud data centers
- Managed model inference
- Graphics Processing Units (GPUs)
- Tensor Processing Units (TPUs)
Explanation
Correct Answer
C:GPU instances commonly support that software ecosystem. GPUs provide broadly supported parallel processing for training and serving many AI models.
Incorrect Answers
A:Data centers provide physical sites, power, networking and hardware supporting regional cloud services. This does not directly meet the case requirement to use parallel GPU hardware for AI.
B:Managed inference lets a provider operate model-serving infrastructure on behalf of applications. This does not directly meet the case requirement to use parallel GPU hardware for AI.
D:TPUs are Google's custom-designed accelerators optimized for machine-learning tensor workloads. This does not directly meet the case requirement to use parallel GPU hardware for AI.
Question 259
A performance review discusses GPU or TPU resources, low-latency networking and distributed storage as one system. Which architecture is being described?
- Tensor Processing Units (TPUs)
- Cloud data centers
- Autoscaling compute
- AI Hypercomputer architecture
Explanation
Correct Answer
D:AI Hypercomputer treats the AI stack as a coordinated design. AI Hypercomputer integrates performance-oriented compute, networking, storage and software for AI workloads.
Incorrect Answers
A:TPUs are Google's custom-designed accelerators optimized for machine-learning tensor workloads. This does not directly meet the case requirement to optimize distributed model workload infrastructure.
B:Data centers provide physical sites, power, networking and hardware supporting regional cloud services. This does not directly meet the case requirement to optimize distributed model workload infrastructure.
C:Autoscaling adjusts resources in response to usage demand, subject to quotas and design. This does not directly meet the case requirement to optimize distributed model workload infrastructure.
Question 260
A commerce assistant serves 100 queries per minute ordinarily but 500 at peak. What platform behavior should absorb the surge?
- Managed model inference
- AI Hypercomputer architecture
- Graphics Processing Units (GPUs)
- Autoscaling compute
Explanation
Correct Answer
D:Autoscaling can adjust provisioned capacity. Autoscaling adjusts resources in response to usage demand, subject to quotas and design.
Incorrect Answers
A:Managed inference lets a provider operate model-serving infrastructure on behalf of applications. This does not directly meet the case requirement to respond to changing request volumes.
B:AI Hypercomputer integrates performance-oriented compute, networking, storage and software for AI workloads. This does not directly meet the case requirement to respond to changing request volumes.
C:GPUs provide broadly supported parallel processing for training and serving many AI models. This does not directly meet the case requirement to respond to changing request volumes.
Generative AI Leader Practice Tests
