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| Section | Objectives |
|---|---|
| Topic 1: Implement Azure AI solutions | - Implement knowledge mining with Azure AI Search - Implement natural language processing solutions - Implement computer vision solutions - Implement generative AI solutions using Azure OpenAI |
| Topic 2: Plan and manage Azure AI solutions | - Plan security and compliance requirements - Monitor and optimize AI solutions - Select appropriate Azure AI services |
| Topic 3: Implement and monitor AI workloads | - Monitor performance and troubleshoot issues - Deploy AI models and services |
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NEW QUESTION # 107
You are developing a solution for a hospital to support the following use cases:
*The most recent patient status details must be retrieved even if multiple users in different locations have updated the patient record.
*Patient health monitoring data retrieved must be the current version or the prior version.
*After a patient is discharged and all charges have been assessed, the patient billing record contains the final charges.
You provision a Cosmos DB NoSQL database and set the default consistency level for the database account to Strong. You set the value for Indexing Mode to Consistent.
You need to minimize latency and any impact to the availability of the solution. You must override the default consistency level at the query level to meet the required consistency guarantees for the scenarios.
Which consistency levels should you implement? To answer, drag the appropriate consistency levels to the correct requirements. Each consistency level may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Strong consistency is required for the patient-status scenario because it provides linearizability : reads return the latest committed value. This is the appropriate guarantee when updates can originate from different locations and the application must never return an older patient status. Microsoft defines Strong as the highest Azure Cosmos DB consistency level and notes that it provides the strongest freshness guarantee, at the cost of greater latency and reduced availability compared with weaker models.
For health-monitoring data, Bounded Staleness is the intended consistency level. It explicitly limits how far reads can lag behind writes using a maximum number of versions (K) or a time interval (T). Conceptually, this matches a requirement that data may be current or only a limited number of versions behind.
For finalized billing data, Eventual consistency provides the lowest latency and highest availability. Once discharge processing is complete and no further billing writes occur , replicas eventually converge on the same final value. Microsoft specifically notes that, in the absence of further writes, weaker consistency levels ultimately yield the same converged data.
Azure Cosmos DB also permits requests to relax consistency below an account ' s Strong default , which is exactly the optimization pattern described here.
Study Guide references: Azure Cosmos DB for NoSQL # Strong consistency; Bounded Staleness; Eventual consistency; per-request consistency override; latency/availability trade-offs.
NEW QUESTION # 108
You are training a Language Understanding model for a user support system.
You create the first intent named GetContactDetails and add 200 examples.
You need to decrease the likelihood of a false positive.
What should you do?
Answer: D
Explanation:
To reduce false-positive intent predictions , add representative utterances to the None intent . The None intent exists specifically for user utterances that should not map to any defined business intent. Microsoft recommends adding examples that resemble potential false positives so that the model learns a stronger decision boundary between valid intent utterances and unrelated or ambiguous input.
For example, if the GetContactDetails intent contains requests such as "give me the customer phone number," the None intent should contain similar-looking but semantically unrelated phrases that could otherwise be incorrectly classified as GetContactDetails. During prediction, an utterance can be classified as None when it resembles None-training examples or when the highest intent score falls below the configured None threshold. Microsoft specifically advises adding false-positive examples to the None intent to improve intent discrimination.
Adding more examples to GetContactDetails alone generally strengthens recognition of that intent but does not provide the model with enough negative examples. A machine-learned entity addresses entity extraction rather than intent classification. Active learning helps identify uncertain utterances for review, but it is not the direct corrective action requested.
Study Guide references: Azure AI Language # Conversational Language Understanding; intents; None intent; intent classification; reducing false positives.
NEW QUESTION # 109
You are building o back end pipeline that receives Al inference requests. The pipeline must perform the following actions:
* Publish a message so that multiple independent consumers receive it.
* Process massages in order through a single consumer entity.
* isolate any failed messages
You need to configure the appropriate Service Bus entities.
How should you configure the service bus entities? To answer, move the appropriate configurations to the correct requirements. You may use each configuration once..
Answer:
Explanation:
Explanation:
NEW QUESTION # 110
You are designing an Azure Database for PostgreSQL table for semantic search. Queries frequently filter on the created_at column. The schema must support vector similarity search and reliable date filtering You need to ensure that the table meets the requirements.
Which two actions should you perform? Each correct answer presents part of the solution. Choose two NOTE: Each correct selection is worth one point.
Answer: A,B
Explanation:
Detailed Explanation: A semantic-search schema should use the pgvector vector type for embeddings so PostgreSQL can apply vector distance operators and vector indexes. The creation timestamp should use an actual timestamp data type so comparisons, range predicates, ordering, and indexes use date semantics rather than string ordering. Storing vectors or dates as free-form character data would sacrifice type validation and make similarity or date filtering less efficient and less reliable.
Study Guide Alignment: AI data-management workloads: Cosmos DB, PostgreSQL, caching, vector storage, vector retrieval, consistency, and connection optimization.
Official Microsoft Learn References: AI-200 Study Guide | Vector similarity search with Azure PostgreSQL
NEW QUESTION # 111
An application deployed to AKS depends on an internal API hosted in the same cluster. All pods are healthy and report Ready status, but requests between the services time out.
You need to determine whether the issue is related to Kubernetes service configuration or application code.
What should you do first?
Answer: B
NEW QUESTION # 112
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