AI-102 Exam Dumps - Designing and Implementing a Microsoft Azure AI Solution

The requirement:

You are using Azure AI Anomaly Detector.

You need to deploy it on Server1, which has intermittent internet access.

This means you need to run the service locally using containers (Docker).

Step 1 – Install Docker Engine

Before pulling or running any containers, Docker must be installed on Server1.

Without Docker Engine, no container can be deployed.

Step 2 – Pull the container image

Use docker pull to download the Anomaly Detector container image from Microsoft Container Registry.

This requires internet access, so it should be done when connectivity is available.

Step 3 – Run the container

After pulling the image, use docker run to start the Anomaly Detector container.

This exposes a local REST API endpoint on Server1.

Step 4 – Query the local endpoint

Once the container is running, you can query the prediction endpoint on Server1.

This allows local inference without requiring continuous internet access.

Correct Sequence:

Install the Docker Engine on Server1.

From Server1, run the docker pull command.

From Server1, run the docker run command.

Query the prediction endpoint on Server1.

Microsoft References

Use Azure AI Anomaly Detector on containers

Deploy Azure AI services in Docker containers

Anomaly Detector container setup

For a social media messaging app that needs to determine the language of short text in real time, the most appropriate service is Azure AI Translator. Translator provides a low-latency language detection operation for text, returning the detected language code and a confidence score, and it’s optimized for interactive scenarios like chat and messaging. You can use detection on its own or feed the result directly into translation if needed.

Why not the others:

Azure AI Speech is for audio (speech-to-text, text-to-speech, speech translation), not text-only language detection.

Azure AI Language has language detection within its Text Analytics features, but Translator’s detect endpoint is the recommended, streamlined choice for real-time translation pipelines and chat apps.

Azure AI Foundry Content Safety is for moderation (toxicity, harassment, hate), not language identification.

Microsoft References

Azure AI Translator — Text Translation overview (includes language detection).

https://learn.microsoft.com/azure/ai-services/translator/

Text Translation REST API v3.0 — Detect operation.

https://learn.microsoft.com/azure/ai-services/translator/reference/v3-0-detect

Quickstart: Use the Translator text client libraries (shows detect + translate flow).

https://learn.microsoft.com/azure/ai-services/translator/quickstart-text-sdk

- enable active learning

- validate the utterances

- train and republish

https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-how-to-review-endpoint-utterances#log-user-queries-to-enable-active-learning

https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-concept-prebuilt-model

For an Azure AI Language Custom Question Answering project (QA1), you can import Q & A pairs directly from Excel (.xls/.xlsx) or TSV files in Language Studio. Microsoft’s guidance explicitly states that when importing question–answer sources into a project, you can choose Excel or TSV. JSON, LU, and CSV are not supported as import file types for Q & A pairs in Language Studio. Microsoft Learn

Additional Microsoft guidance notes that structured TXT, TSV, and XLS files can be uploaded to create or augment a project, and clarifies import/export behavior for TSV and XLS. This reinforces that Excel and TSV are the supported formats here. Microsoft Learn

Microsoft References:

Move projects and Q & A pairs – “Import question and answers… option to import either an Excel or TSV file.” Microsoft Learn

Format guidelines for Custom Question Answering – structured TXT, TSV, and XLS supported; import/export notes for TSV and XLS. Microsoft Learn

To minimize development effort in Azure AI Content Understanding, you should start from a video analyzer template that already targets large libraries and general-purpose metadata extraction. The Media asset management template is designed exactly for this scenario—organizing and extracting structured information from large collections of videos (news, marketing, broadcast, TV episodes, film archives). Templates are fully customizable, so you can add a field such as sentiment per video or per segment without building the schema from scratch. This approach yields the required sentiment analysis while keeping effort low.

“Video shot analysis” is not a Content Understanding template (shot detection is a capability inside the video analyzer), and the Advertising template is tuned for ad-specific fields (brand presence, ad categories), which is unnecessary for a generic video library. Using Media asset management aligns with the library use case and lets you add a sentiment field to the template’s schema.

Microsoft References

Analyzer templates (Video) — lists the available video templates including Media asset management and Advertising and notes that templates are customizable. Microsoft Learn

Video solutions overview — explains prebuilt video analyzer, shot detection (as a capability), and shows how to customize fields (example includes extracting fields such as sentiment per scene ). Microsoft Learn

To extend Video Indexer with industry-specific terms, you upload a plain text (TXT) file containing the vocabulary.

PDF, XML, XLS are not supported for custom vocabulary upload.

Correct Answer: C

Comprehensive Detailed Explanation

You have an Azure Cognitive Search service. Query volume has increased, and some search requests are now being throttled. This means your current tier is no longer sufficient to handle the query traffic.

Why does migrating to a higher tier work?

Each Azure Cognitive Search pricing tier (Basic, Standard S1/S2/S3, Storage Optimized, etc.) provides different resource limits for queries per second (QPS), indexing throughput, and storage.

Throttling occurs when query traffic exceeds the capacity limits of the current tier.

Moving to a higher tier increases:

The allowed query units (QUs),

Maximum queries per second (QPS),

and available compute resources.

Therefore, migrating to a higher tier reduces the likelihood of throttling and supports the increased query volume.

Why not other solutions?

Simply adding replicas can help scale out query workloads, but the question specifically asks whether moving to a higher tier meets the goal—and it does.

Using indexer scaling or adjusting query patterns might help, but they are not direct answers to the throttling caused by insufficient service tier capacity.

Correct Answer: A. Yes

Microsoft References

Azure Cognitive Search service limits by tier

Scale resources in Azure Cognitive Search

Azure Cognitive Search pricing tiers