Microsoft AI-200 Practice Test Questions ( Updated) – Real Exam Questions & Dumps PDF
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Microsoft AI-200 Sample Questions – Free Practice Test & Real Exam Prep
Question #1
A solution must fan a single 'DocumentIngested' event out to three independent consumers (indexing,
notification, and audit), each able to fail and retry independently without affecting the others. Which design is
most appropriate?
One Service Bus queue read by all three consumers competing for messages
Event Grid topic with three subscriptions, or a Service Bus topic with three subscriptions, so each
consumer gets its own copy
A single function that calls all three synchronously
A Redis list shared by all consumers
Answer: B Explanation. Publish/subscribe via an Event Grid topic (three subscriptions) or a Service Bus topic (three subscriptions)
delivers an independent copy to each consumer, so each can retry or fail in isolation. A single queue with competing
consumers delivers each message to only one consumer, a synchronous fan-out couples the consumers' failures, and a
shared Redis list does not provide independent per-consumer delivery and retrie
Question #2
A Service Bus-triggered function's messages keep reappearing and are eventually dead-lettered with reason
'MaxDeliveryCountExceeded', even though processing seems to complete. Logs show processing sometimes
takes longer than the lock duration. What is the best fix?
A. Disable dead-lettering
B. Renew the message lock during long processing (or increase lock duration) so the lock does not expire
before completion
C. Lower MaxDeliveryCount to 1
D. Switch to Event Grid
Answer: B Explanation. If processing exceeds the lock duration, the lock expires, the message is redelivered, and repeated redelivery
eventually dead-letters it. Renewing the lock (or increasing lock duration to exceed processing time) lets the handler
AI-200 Practice Exam | Page 30
complete before the lock lapses. Lowering MaxDeliveryCount dead-letters sooner, Event Grid does not fix the lock issue,
and disabling dead-lettering hides poison messages
Question #3
An order-processing pipeline must guarantee that duplicate submissions (same OrderId sent twice within 10
minutes) are not processed twice, and that messages for the same OrderId are handled in order. Which Service
Bus configuration meets both needs?
A. Use ReceiveAndDelete mode with prefetch
B. Enable partitioning and set a large max size
C. Enable duplicate detection with a 10-minute window and use sessions keyed by OrderId
D. Use Event Grid with retries
Answer: C Explanation. Duplicate detection with a 10-minute window discards repeated MessageIds within that window, and
sessions keyed by OrderId enforce ordered, single-consumer processing per order—together satisfying both
requirements. Partitioning/max size addresses scale, ReceiveAndDelete risks loss and gives no ordering, and Event Grid
provides neither dedupe windows nor per-key ordering
Question #4
A RAG pipeline returns answers that are fluent but sometimes unsupported by the retrieved context
(hallucinations). Retrieval quality metrics show low groundedness. Which combination of changes most directly
improves groundedness?
Switch the vector store from Cosmos DB to Redis
Improve chunking/retrieval relevance, add hybrid + re-ranking, and instruct the model to answer only
from provided context (and cite sources)
Remove the system prompt entirely
Increase temperature and max_tokens
Answer: B Explanation. Groundedness improves when the model receives relevant, well-scoped context and is constrained to use
only that context—better chunking, hybrid retrieval with re-ranking, and explicit grounding instructions with citations
address the root cause. Higher temperature increases variability/hallucination, changing the vector store technology does
not fix relevance, and removing the system prompt loses the grounding constraints.
Question #5
After adding an IVFFlat index to a growing pgvector table, recall (result quality) has degraded even though
queries are faster. What is the most likely cause and remedy?
A. The embeddings must be stored as JSON
B. The IVFFlat 'lists'/'probes' settings are not tuned for the current data size; increase probes (or rebuild withmore lists) to improve recall
C. The table needs a B-tree index instead
D. pgvector does not support similarity search
Answer: B Explanation. IVFFlat is an approximate index whose recall depends on the number of lists and how many probes are
searched; too few probes/lists for the data volume lowers recall. Increasing probes at query time (or rebuilding with an appropriate lists value) restores recall. A B-tree does not serve vector search, pgvector clearly supports similarity search,
and JSON storage would break it.
Question #6
A financial RAG assistant must never mix data between customers, must audit every retrieval, and must
minimize the risk of one customer's vectors surfacing for another. Using Cosmos DB for NoSQL, which design
most reduces cross-tenant leakage risk?
A. A single global index with client-side filtering after retrieval
B. Store all tenants in one item to simplify queries
C. One shared container with customerId only used as a query filter and no partitioning by tenant
D. Partition by tenant (customerId) and always scope vector queries to that partition key, with retrieval
logging
Answer: D Explanation. Partitioning by tenant and constraining every vector query to that partition key both isolates data and
makes queries efficient and single-partition, while retrieval logging satisfies auditing. Relying only on a query filter
without tenant partitioning, post-retrieval client-side filtering, or lumping tenants together all raise the risk of returning
another customer's data.
Question #7
You must lower the cost and latency of pgvector similarity search on a large, growing table while keeping recall
acceptable. Which two actions are appropriate? (Choose two.)
A. Build an HNSW index and tune its parameters (for example, m and ef_search)
B. Force sequential scans for every query
C. Remove the vector column and store embeddings in TEXT
D. Right-size compute/memory so the index and working set fit in RAM
Answer: A,D Explanation. An HNSW index with tuned parameters balances recall and speed, and provisioning enough memory so the
index/working set stays in RAM avoids disk I/O that inflates latency. Storing embeddings as TEXT disables vector search,
and forcing sequential scans is the opposite of optimization.
Question #8
A retrieval service using Azure Managed Redis must implement a semantic cache: if a new question is highly
similar to a previously answered one, return the cached answer; otherwise call the model. Which Redis
capability enables the 'highly similar' lookup?
A. Simple string GET on the exact question text
B. Redis Streams consumer groups
C. Redis vector indexing/similarity search over stored question embeddings with a similarity threshold
D. Redis pub/sub channels
Answer: C Explanation. A semantic cache needs approximate matching, which Redis provides through vector indexing and similarity
search over embedded questions, returning a hit when similarity passes a threshold. An exact string GET only matches
identical text, while pub/sub and Streams are messaging primitives, not similarity search.
Question #9
An enterprise RAG system must refresh its vector index within seconds whenever source documents in Cosmos
DB change, without a nightly batch job. Which architecture is best?
A. A change feed processor that, on each changed item, re-chunks/re-embeds only the affected document and upserts its vectors
B. A Timer function that re-embeds all documents every night
B. A Timer function that re-embeds all documents every night
D. Manual re-indexing triggered by an operator
Answer: A Explanation. A change feed processor reacts to individual item changes in near real time and re-embeds/upserts only the
affected document, keeping the index fresh efficiently. A nightly re-embed is too slow and wasteful, manual re-indexing
does not meet the seconds requirement, and full-container polling is expensive and still laggy compared with the change
feed
Question #10
A Cosmos DB for NoSQL container shows one physical partition consuming most RU/s and frequent 429s, while
others are nearly idle. Point reads by 'id' are fine, but throughput cannot be raised cost-effectively. What is the
best remediation?
A. Redesign the partition key to one with high cardinality and even access (for example, a synthetic/composite key) and migrate data
B. Switch to eventual consistency
C. Increase provisioned RU/s until 429s stop
D. Enable the analytical store
Answer: A Explanation. A hot partition indicates a low-cardinality or skewed partition key; the durable fix is to choose a highercardinality/composite key that distributes load evenly and migrate the data. Simply raising RU/s pays for skew without
fixing it, changing consistency does not rebalance partitions, and the analytical store addresses analytics, not the hotpartition throughput problem.
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