Plan and manage an Azure AI solution →hardMultiple Select

AI-102 Plan and manage an Azure AI solution Practice Question

Which TWO of the following are best practices for managing Azure AI services costs?

⚠ Common exam trap

Many exam-takers confuse cost-saving strategies (like using the Free tier or batching) with best practices for managing costs in production, overlooking that the Free tier is not for production and that batching may not be applicable or effective for all services.

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

✓

Use the S0 pricing tier for production workloads

Option A is correct because the S0 (Standard) tier is the production-grade pricing tier for Azure AI services, offering higher throughput, SLA-backed availability, and pay-as-you-go billing that lets you match capacity to actual usage rather than being capped by Free-tier limits. Option E is correct because configuring budget alerts in Azure Cost Management (Microsoft Cost Management + Billing) proactively notifies you when spending approaches or exceeds defined thresholds, enabling early corrective action before costs escalate. Option B is incorrect because the Free tier (F0) has strict transaction and rate limits and is intended only for trials and evaluation, not production workloads. Option C is incorrect because scaling up partitions increases provisioned capacity and therefore cost, and it is a performance/scalability action rather than a cost-management best practice. Option D is incorrect because increasing batch size is a throughput optimization for supported batch APIs, not a general cost-control practice, and it does not reduce charges for services billed per transaction in the way implied.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
Use the S0 pricing tier for production workloads
Why this is correct
The S0 standard tier provides the throughput, SLA and feature set required for production workloads, avoiding the rate limits and lack of SLA that constrain the free F0 tier. This satisfies the production workload requirement.
✗
Always use the Free tier to avoid charges
Why it's wrong here
Free tiers cap transaction volume and features, so production workloads exceed them and incur charges or fail; they suit prototyping and evaluation only. Cost management instead relies on selecting the pricing tier matching throughput and monitoring usage.
✗
Scale up partitions to improve performance
Why it's wrong here
Partitions affect throughput and concurrency, not billing; scaling them up raises provisioned capacity and therefore cost. It is tempting because performance tuning and cost tuning are conflated, but the scenario asks for cost reduction, which capacity trimming achieves.
✗
Increase batch size to reduce number of API calls
Why it's wrong here
Batching reduces per-call overhead but does not lower billed cost, since Azure AI services charge per transaction or per record regardless of grouping. It is tempting as an efficiency measure, yet cost control requires tier selection, caching, and usage monitoring.
✓
Set up budget alerts in Azure Cost Management
Why this is correct
Budget alerts in Azure Cost Management track actual and forecast spend against a defined threshold, triggering notifications before overruns occur. This satisfies the stem's cost-management constraint by enabling proactive intervention across Azure AI services, rather than reactive review after billing. Alerts alone do not cap spending, but they surface anomalies early enough to adjust provisioned throughput or model deployments.

Go deeper

AI-102 Plan and manage an Azure AI solution Practice Question

Why each option matters

Option-by-option breakdown

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