Microsoft Building AI Applications with Azure Cosmos DB (DP-420) Certification Sample Questions

Building AI Applications with Azure Cosmos DB Dumps, DP-420 Dumps, DP-420 PDF, Building AI Applications with Azure Cosmos DB VCE, Microsoft DP-420 VCE, Microsoft MCA Azure Cosmos DB AI Developer PDFThe purpose of this Sample Question Set is to provide you with information about the Microsoft Building AI Applications with Azure Cosmos DB exam. These sample questions will make you very familiar with both the type and the difficulty level of the questions on the DP-420 certification test. To get familiar with real exam environment, we suggest you try our Sample Microsoft Building AI Applications with Azure Cosmos DB Certification Practice Exam. This sample practice exam gives you the feeling of reality and is a clue to the questions asked in the actual Microsoft Certified - Azure Cosmos DB AI Developer Associate certification exam.

These sample questions are simple and basic questions that represent likeness to the real Microsoft DP-420 exam questions. To assess your readiness and performance with real time scenario based questions, we suggest you prepare with our Premium Microsoft Building AI Applications with Azure Cosmos DB Certification Practice Exam. When you solve real time scenario based questions practically, you come across many difficulties that give you an opportunity to improve.

Microsoft DP-420 Sample Questions:

You need to create resortdb and its containers. The solution must meet the technical requirements.
Which two actions should you perform?
Each correct answer presents part of the solution.
a) Provision throughput on the resortdb database so that its containers share it.
b) Create liftscans in resortdb with dedicated throughput of its own.
c) Provision dedicated throughput on each reference container.
d) Create liftscans as a serverless container in resortdb.
e) Create liftscans without throughput of its own, and size the database throughput for its peak.
 
You need to make the order data available in fc-finance. The solution must meet the technical requirements.
What should you do first?
a) Enable analytical store on orders, and read it with a Data Factory pipeline.
b) Assign a data-plane role on fc-ops to the managed identity of fc-finance.
c) Change the backup policy of fc-ops to continuous backup.
d) Create a mirrored Azure Cosmos DB database for fc-ops in fc-finance.
 
03. Wingtip Toys stores product items in a container named catalog in an Azure Cosmos DB for NoSQL account named account1. The container has a full-text policy and a full-text index on /title, and each item has a brand property.
When a shopper searches for robot, a search page in an app named app1 must show a brand panel that lists each brand together with the number of matching products for that brand. Only products whose title contains the search term must be counted, and the panel must be built by using a single query.
Which query should app1 run to build the brand panel?
a) SELECT DISTINCT c.brand FROM c WHERE FullTextContains(c.title, "robot")
b) SELECT c.brand AS facetKey, COUNT(1) AS facetCount FROM c WHERE FullTextContains(c.title, "robot") GROUP BY c.brand
c) SELECT c.brand AS facetKey, COUNT(1) AS facetCount FROM c WHERE FullTextContains(c.title, {"term": "robot", "distance": 2}) GROUP BY c.brand
d) SELECT c.brand AS facetKey, COUNT(1) AS facetCount FROM c GROUP BY c.brand
 
04. Contoso, Ltd. is building app1, a multitenant contract assistant. You are creating a container named container1 in an Azure Cosmos DB for NoSQL account named account1 that has vector search enabled. Each item stores a tenantId property and a 1,536-dimension embedding in /embedding. The container vector policy declares /embedding, and every app1 search query already filters with WHERE c.tenantId = @tenantId.
All tenants must share container1, and each tenant's vector searches must run against a DiskANN index that holds only that tenant's vectors.
You need to declare the shard key for the vector index of container1.
What should you configure?
a) In vectorIndexes, add a diskANN entry for /embedding with "vectorIndexShardKey": ["/tenantId"]
b) In the vector policy entry for /embedding, add "vectorIndexShardKey": ["/tenantId"]
c) In vectorIndexes, add a quantizedFlat entry with "vectorIndexShardKey": ["/tenantId"]
d) In vectorIndexes, add a diskANN entry for /embedding with "vectorIndexShardKey": "/tenantId"
 
05. Tailwind Traders created a container named products in an Azure Cosmos DB for NoSQL account named account1. products has a vector policy for /embedding (1,536 dimensions, float32, cosine), a diskANN vector index on /embedding, and /embedding/* in excludedPaths. An app named app1 has loaded 600 items so far. The full catalog of about 200,000 items will be loaded over the next month.
Vector queries such as SELECT TOP 10 c.name FROM c ORDER BY VectorDistance(c.embedding, @qv) already return sensible results, but their request unit (RU) charge is much higher than the team expected. Vector search must keep low latency when it runs over the full catalog.
What should you do?
a) Re-create products with a flat vector index on /embedding so that searches use exact brute-force scans
b) Re-create products with a quantizedFlat vector index on /embedding and reload the 600 items
c) Remove /embedding/* from excludedPaths so that the range index also covers the vector path
d) Keep the diskANN index; the charge falls once at least 1,000 vectors are indexed
 
You need to provide the operations team with the request unit consumption and cost data for fc-loyalty and fc-ops. The solution must meet the technical requirements.
What should you use?
a) Azure Monitor metrics for both accounts in Metrics Explorer
b) Fabric mirroring of fc-loyalty and fc-ops into one Fabric workspace
c) Fleet analytics for the fleet, with a Fabric workspace as its destination
d) Diagnostic settings that send both accounts' resource logs to one Log Analytics workspace
 
07. Tailwind Traders runs a multitenant app that has 60 Azure Cosmos DB for NoSQL accounts, one per tenant. The accounts are spread across three subscriptions and several resource groups, and all of them use single-region writes in the same Azure region. The accounts must remain in their current subscriptions and resource groups.
You need all 60 accounts to draw extra RU/s from one shared throughput pool when their dedicated throughput is exceeded.
What should you do?
a) Move the accounts into one subscription, and then add them to a fleetspace in a fleet
b) Create a fleet, put all 60 accounts in one fleetspace, and give it a pool
c) Create a fleet, and configure a pool on each account in it
d) Create a fleet in each subscription, and configure a pool in each of the three fleets
 
08. Proseware, Inc. stores troubleshooting articles in a container named articles in an Azure Cosmos DB for NoSQL account. Each item has title, content, status and viewCount properties and an embedding of the title and content in /embedding. The embeddings are produced by a custom embedding model that Proseware hosts itself.
Editors update article content throughout the day, and vector search keeps returning results based on the old text. Most writes to articles change only status or viewCount.
You need to keep the embeddings synchronized with article content while minimizing calls to the embedding model.
What should you do?
a) Add an embeddingSource for /title and /content to the vector policy entry for /embedding so that Azure Cosmos DB generates the embeddings.
b) Schedule a nightly job that regenerates the embedding of every article and upserts each item.
c) Use an Azure Functions Cosmos DB trigger that regenerates the embedding for every changed article that the trigger delivers.
d) Use an Azure Functions Cosmos DB trigger that re-embeds an article only when a hash of its title and content has changed.
 
You need to configure the masking policy of conversations. The support analysts will query conversations by using their Microsoft Entra ID accounts, which will be assigned the Cosmos DB Built-in Data Reader role. Properties that HelpPilot adds to conversation items in later releases must also remain readable unmasked. The solution must meet the security requirements.
Which masking policy should you configure?
a) includedPaths: /, /customer/email (Email), and /customer/phone (Default); excludedPaths: none
b) includedPaths: /, /customer/email (Email), and /customer/phone (Default); excludedPaths: every other property path that conversation items use today
c) includedPaths: /customer/email (Email) and /customer/phone (Default); excludedPaths: none
d) includedPaths: /; excludedPaths: /customer/email and /customer/phone
 
10. Contoso, Ltd. is creating a container named container1 in an Azure Cosmos DB for NoSQL account named account1 to store about 40 million product descriptions. Each item will hold a 1,536-dimension embedding in /embedding that an embedding deployment named embed1 produces as float32 values.
You need to reduce the storage used by the vectors in container1. The solution must keep using embed1, and a small reduction in search accuracy is acceptable.
What should you do?
a) Set dataType to float16 for /embedding in the vector policy of container1
b) Switch embed1 to a model that returns fewer dimensions
c) Set dimensions to 768 for /embedding in the vector policy
d) Set distanceFunction to dotproduct for /embedding in the vector policy

Answers:

Question: 01
Answer: a, b		 Question: 02
Answer: c		 Question: 03
Answer: b		 Question: 04
Answer: a		 Question: 05
Answer: d
Question: 06
Answer: c		 Question: 07
Answer: b		 Question: 08
Answer: d		 Question: 09
Answer: c		 Question: 10
Answer: a

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