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| Section | Objectives |
|---|---|
| Connect to and consume Azure services | - Integrate Azure services
|
| Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
| Develop containerized solutions on Azure | - Implement containerized applications
|
| Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
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NEW QUESTION # 128
You deploy a Linux container image to App Service.
The container requires the following environment variables at runtime:
* A non-sensitive configuration value named MODEL_VERSION
* A database password that must remain secure
You need to configure App Service to provide these environment variables at runtime.
Which configurations should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 129
You publish custom AI inference events to an Event Grid topic.
Downstream services must receive events only if the confidenceScore value is greater than 0.80.
You need to filter events based on a numeric value in the event payload.
Which filter should you configure?
Answer: B
Explanation:
An Advanced Filter using the NumberGreaterThan operator configured with a key path pointing to the payload value (e.g., data.confidenceScore) and a comparison value of 0.80 should be set up.
To filter custom payload data based on a numeric threshold in Azure Event Grid, configure the event subscription with these properties:
Key: data.confidenceScore (adjust the path depending on your exact JSON payload structure) Operator: NumberGreaterThanValue: 0.80 Reference:
https://learn.microsoft.com/en-us/azure/event-grid/event-filtering
NEW QUESTION # 130
Hotspot Question
You are developing a Retrieval-Augmented Generation (RAG) solution for a company.
AI responses and embedding vectors are cached in Redis.
The solution must meet the following requirements:
- AI responses must expire exactly 24 hours after they are cached.
- Cached embeddings must always reflect the current source data.
You need to configure Redis to meet the requirements.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Set a Time for Live (TTL) on each key.
To configure Redis to expire your AI responses exactly 24 hours after caching, you must apply a Time-To-Live (TTL) of 86,400 seconds to the specific keys holding those responses.
Box 2: Delete related keys when the document changes
You must invalidate or update those cached keys when source data changes to prevent your RAG system from retrieving stale information.
Incorrect:
Configuring allkeys-lru (Least Recently Used) is not recommended to ensure your cached embeddings or AI responses reflect current source data.allkeys-lru is an eviction policy. It only deletes data when Redis reaches its maximum memory limit, and it targets keys that haven't been accessed recently. It does not verify if the underlying source data has changed, meaning users will get dangerously outdated (hallucinated) AI responses if the source document is updated but the cached response remains in memory.
Reference:
https://zenvanriel.com/ai-engineer-blog/ai-caching-strategies/
NEW QUESTION # 131
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.
You are using Azure Monitor Application Insights to investigate a production API. You open the Logs blade and set the time range to Last 24 hours.
An engineer recommends the following query to count requests by result code and sort the results from most frequent to least frequent:
requests
| summarize request_count = count() by resultCode
| order by request_count desc
You need to determine whether the query returns the number of requests grouped by result code and sorted from most to least frequent.
Solution: The query lists every individual request along with its result code.
Does the solution meet the goal?
Answer: A
Explanation:
Correct:
* The query returns one row per unique resultCode value with the number of requests in each group.
The Kusto Query Language (KQL) query uses the summarize operator, which acts as a grouping and aggregation mechanism.
summarize request_count = count() by resultCode
This groups all the individual rows in the requests table by their unique resultCode. It then counts the total number of logs within each group and places that value into a new column called request_count.
order by request_count desc: This sorts those aggregated rows so that the resultCode with the highest number of requests appears at the top.
Incorrect:
* The result codes are sorted alphabetically.
* The query lists every individual request along with its result code.
Reference:
https://learn.microsoft.com/en-us/azure/azure-monitor/reference/tables/apprequests
NEW QUESTION # 132
You are choosing an embedding strategy for a RAG solution. Documents range from 2 to 200 pages. You need to preserve semantic coherence while staying within embedding model token limits. What should you do?
Answer: D
Explanation:
Embedding models have token limits, and embedding an entire long document as one vector dilutes semantic meaning. Chunking into smaller, slightly overlapping segments preserves context at chunk boundaries and keeps each chunk within model limits while improving retrieval relevance.
NEW QUESTION # 133
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