easyMultiple Select
PDE Practice Question: A data engineering team is operationalizing a…
A data engineering team is operationalizing a machine learning model for real-time fraud detection. The model must process transactions with sub-100ms latency and be highly available. Which TWO strategies should the team implement?
⚠ Common exam trap
Google Cloud often tests the misconception that single-zone deployment minimizes latency, but the real trade-off is between availability and negligible intra-region latency, making multi-region deployment the correct choice for high availability.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Deploy the model to multiple Google Cloud regions for failover.
Option A is correct because deploying the model to multiple Google Cloud regions provides geographic redundancy and failover, which directly supports the high-availability requirement for a real-time fraud detection service. Option D is correct because pruning or quantizing the model reduces its size and computational cost, which helps achieve the sub-100ms latency target for real-time inference. Option B is incorrect because a single zone creates a single point of failure and does not meet the high-availability requirement, even if it reduces cross-zone latency. Option C is incorrect because Cloud Batch is designed for asynchronous, batch-oriented workloads, not real-time sub-100ms predictions. Option E is incorrect because loading the model from Cloud Storage on every request adds significant network and I/O latency, making the sub-100ms target impractical.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Deploy the model to multiple Google Cloud regions for failover.
Why this is correct
Deploying across multiple Google Cloud regions directly satisfies the high-availability constraint by eliminating single-region failure as a single point of outage. Regional redundancy lets traffic fail over when one region degrades, preserving continuous fraud scoring. It does not by itself guarantee sub-100ms latency, which the second strategy must address.
- ✗
Deploy the model to a single zone to minimize cross-zone latency.
Why it's wrong here
A single-zone deployment removes redundancy, so a zone outage takes the fraud-detection service offline, violating the high-availability requirement. It is tempting to shave cross-zone latency, but multi-zone deployment with a load balancer is needed for resilience.
- ✗
Use Cloud Batch for asynchronous prediction.
Why it's wrong here
Cloud Batch queues jobs for asynchronous execution, so predictions return after scheduling and processing delays, breaching the sub-100ms requirement. It suits overnight or throughput-oriented workloads, not synchronous per-transaction inference that an always-on endpoint must serve.
- ✓
Optimize the model by pruning or quantizing to reduce size.
Why this is correct
Pruning and quantising shrink the model, cutting inference compute and memory so each prediction returns faster. This directly serves the sub-100ms latency constraint, since smaller weights reduce the time spent on matrix operations during online fraud scoring.
- ✗
Store the model in Cloud Storage and load it on each request.
Why it's wrong here
Loading the model from Cloud Storage on every request adds object-download latency and network dependency to each prediction, exceeding sub-100ms. It is tempting for centralised model storage, but that pattern suits infrequent batch scoring, not real-time serving where the model stays resident in memory.
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