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| Section | Objectives |
|---|---|
| Plan and manage Azure AI solutions | - Select appropriate Azure AI services - Plan security and compliance requirements - Monitor and optimize AI solutions |
| Implement Azure AI solutions | - Implement knowledge mining with Azure AI Search - Implement generative AI solutions using Azure OpenAI - Implement computer vision solutions - Implement natural language processing solutions |
| Implement and monitor AI workloads | - Deploy AI models and services - Monitor performance and troubleshoot issues |
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NEW QUESTION # 135
Hotspot Question
You have an Azure Service Bus namespace that contains a topic named Topic1.
You plan to create a subscription named Sub1 to Topic1. In Sub1, you plan to filter messages from Topic1 based on their system properties and apply an action that will annotate each filtered message.
You need to configure the filtering.
How should you configure the filtering? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Use the SQL type.
To achieve this goal, you must use a SQL filter (also known as a SqlFilter) type Box 2: Copy a message and annotate its metadata.
The correct filtering action to use is to copy a message and annotate its metadata.
When an action is applied (such as setting a new property or modifying an existing one via SQL filter actions), Azure Service Bus creates a copy of the inbound message for the subscription and annotates the copy's metadata.
Reference:
https://turbo360.com/blog/azure-service-bus-topic-filter
NEW QUESTION # 136
You are using Azure Monitor Application Insights to collect dependency data.
You must be able to:
* Correlate failed requests with dependency calls during the last hour.
* Calculate the average dependency duration per operation.
You need to construct the KQL query by using the minimum number of statements.
Which three operators should you use? Each correct answer presents part of the solution.
Answer: A,B,D
Explanation:
Use where , join , and summarize .
The where operator filters telemetry to the required scope, including records from the last hour and failed requests. For example, a predicate can restrict timestamp > ago(1h) and request success status in the same filtering expression.
Next, use join to correlate request telemetry with dependency telemetry. Application Insights assigns related telemetry a common operation_Id , and Microsoft specifically demonstrates joining the dependencies and requests tables on operation_Id to identify dependency calls associated with requests.
Finally, use summarize with avg(duration) grouped by the appropriate operation identifier or operation name.
summarize performs aggregation over groups and is therefore the required operator for calculating average dependency duration. Microsoft documents dependency duration as supporting an Avg aggregation.
extend is unnecessary because no derived column is required, while distinct merely removes duplicate rows and cannot calculate averages or correlate two telemetry tables.
A representative structure is:
requests
| where timestamp > ago(1h) and success == false
| join (dependencies | where timestamp > ago(1h)) on operation_Id
| summarize avg_dependency_duration = avg(duration1) by operation_Name
Study Guide references: Azure Monitor Application Insights; KQL where; telemetry correlation using operation_Id; join; summarize; dependency duration analysis.
NEW QUESTION # 137
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.
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 result codes are sorted alphabetically.
Does the solution meet the goal?
Answer: B
Explanation:
The solution does not meet the goal because the query does not sort by resultCode. The summarize operator groups request records by each distinct resultCode value and calculates the number of requests in each group by using count().
The resulting table contains a resultCode column and a calculated request_count column. The next statement:
| order by request_count desc
sorts the output by request_count in descending order , which places the most frequently occurring result code first and the least frequent last.
Therefore, the output is sorted by frequency , not alphabetically or numerically by the resultCode field.
If alphabetical sorting by result code were required, the query would instead use a statement such as:
| order by resultCode asc
That is not what the provided query does.
Because the Logs blade is already scoped to the Last 24 hours , the query operates over that selected time range unless additional time filtering is explicitly added.
Study Guide references: Azure Monitor Logs; Application Insights requests table; KQL summarize; count() aggregation; order by; descending sort semantics.
NEW QUESTION # 138
Drag and Drop Question
A recommendation system stores 3 million embeddings in Azure Database for PostgreSQL.
During peak hours, P95 similarity-query latency increases, and the cache hit ratio drops significantly.
You suspect that the vector index working set no longer fits in memory, resulting in more disk reads.
You need to scale server resources and validate the outcome.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Step 1: Before peak hours, run a fixed workload to capture baseline P95 latency and capture EXPLAIN ANALYZE for the vector query Establishing a baseline is crucial for performance tuning. Running EXPLAIN ANALYZE provides deep insight into whether the query is actually using the vector index (like HNSW or IVFFlat) or falling back to a slow sequential scan.
Step 2: Scale to a tier with more memory per vCore
Since the problem states that the vector index working set no longer fits in memory, scaling to a tier with higher memory (such as Memory-Optimized tiers) directly solves the root cause by allowing the RAM to fully cache the vector index, eliminating slow disk reads.
Step 3: Re-run the same benchmark and compare P95 and P99 latency
To validate the outcome of your scaling operations, you must run the identical benchmark workload and compare high-percentile latencies (P95, P99) against your baseline to prove the bottleneck was resolved.
Step 4: Review memory pressure and cache hit ratio during peak hours and compare them to the baseline This step confirms the success of the resource scaling. A successful remediation will show a significantly increased cache hit ratio and reduced memory pressure/disk I/O compared to the baseline metrics.
Reference:
https://www.pgedge.com/blog/ai-features-in-pgadmin-ai-insights-for-explain-plans
NEW QUESTION # 139
You are implementing the expiration requirement for AI-generated summaries in the Redis cache.
You need to purge the AI summaries five minutes after creation, regardless of how often they are accessed.
What should you do?
Answer: C
Explanation:
TTL (Time-To-Live) with a fixed duration of five minutes is the exact Redis feature you need to purge AI-generated summaries regardless of access frequency.
Redis TTL and Expiration Features
Core Expiration Feature
EXPIRE command: Sets a timeout in seconds on a key. Use EXPIRE key 300 to remove the summary after 300 seconds (five minutes).
PX command: Sets a timeout in milliseconds. Use PX key 300000 for precise millisecond control.
EXPIREAT / PEXPIREAT: Sets an absolute Unix timestamp for expiration instead of a relative duration.
Reference:
https://dev.to/truongpx396/30-common-redis-interview-questions-1ai
NEW QUESTION # 140
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