You configure Azure Cognitive Search to index a container in an Azure Cosmos DB Core (SQL) API account as shown in the following exhibit.

Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation

Box 1: country
The country field is filterable.
Note: filterable: Indicates whether to enable the field to be referenced in $filter queries. Filterable differs from searchable in how strings are handled. Fields of type Edm.String or Collection(Edm.String) that are filterable do not undergo lexical analysis, so comparisons are for exact matches only.
Box 2: name
The name field is not Retrievable.
Retrievable: Indicates whether the field can be returned in a search result. Set this attribute to false if you want to use a field (for example, margin) as a filter, sorting, or scoring mechanism but do not want the field to be visible to the end user.
Note: searchable: Indicates whether the field is full-text searchable and can be referenced in search queries.
Reference: https://docs.microsoft.com/en-us/rest/api/searchservice/create-index

Comment: *

Name: *

Email: *

Verification: *

The following is a sample of a document in orders.

The orders container uses customer as the partition key.
You need to provide a report of the total items ordered per month by item type. The solution must meet the following requirements:
Ensure that the report can run as quickly as possible.
Minimize the consumption of request units (RUs).
What should you do?

• A.Configure the report to query orders by using a SQL query.
• B.Configure the report to query a new aggregate container. Populate the aggregates by using the change feed.
• C.Configure the report to query orders by using a SQL query through a dedicated gateway.
• D.Configure the report to query a new aggregate container. Populate the aggregates by using SQL queries that run daily.

Comment: *

Name: *

Email: *

Verification: *

You have a container in an Azure Cosmos DB Core (SQL) API account. The container stores telemetry data from IoT devices. The container uses telemetryId as the partition key and has a throughput of 1,000 request units per second (RU/s). Approximately 5,000 IoT devices submit data every five minutes by using the same telemetryId value.
You have an application that performs analytics on the data and frequently reads telemetry data for a single IoT device to perform trend analysis.
The following is a sample of a document in the container.

You need to reduce the amount of request units (RUs) consumed by the analytics application.
What should you do?

• A.Decrease the offerThroughput value for the container.
• B.Increase the offerThroughput value for the container.
• C.Move the data to a new container that has a partition key of deviceId.
• D.Move the data to a new container that uses a partition key of date.

Comment: *

Name: *

Email: *

Verification: *

You plan to deploy two Azure Cosmos DB Core (SQL) API accounts that will each contain a single database.
The accounts will be configured as shown in the following table.

How should you provision the containers within each account to minimize costs? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation

Box 1: Serverless capacity mode
Azure Cosmos DB serverless best fits scenarios where you expect intermittent and unpredictable traffic with long idle times. Because provisioning capacity in such situations isn't required and may be cost-prohibitive, Azure Cosmos DB serverless should be considered in the following use-cases:
Getting started with Azure Cosmos DB
Running applications with bursty, intermittent traffic that is hard to forecast, or low (<10%) average-to-peak traffic ratio Developing, testing, prototyping and running in production new applications where the traffic pattern is unknown Integrating with serverless compute services like Azure Functions Box 2: Provisioned throughput capacity mode and autoscale throughput The use cases of autoscale include:
Variable or unpredictable workloads: When your workloads have variable or unpredictable spikes in usage, autoscale helps by automatically scaling up and down based on usage. Examples include retail websites that have different traffic patterns depending on seasonality; IOT workloads that have spikes at various times during the day; line of business applications that see peak usage a few times a month or year, and more. With autoscale, you no longer need to manually provision for peak or average capacity.
Reference:
https://docs.microsoft.com/en-us/azure/cosmos-db/serverless
https://docs.microsoft.com/en-us/azure/cosmos-db/provision-throughput-autoscale#use-cases-of-autoscale

Comment: *

Name: *

Email: *

Verification: *

You have an application named App1 that reads the data in an Azure Cosmos DB Core (SQL) API account.
App1 runs the same read queries every minute. The default consistency level for the account is set to eventual.
You discover that every query consumes request units (RUs) instead of using the cache.
You verify the IntegratedCacheiteItemHitRate metric and the IntegratedCacheQueryHitRate metric. Both metrics have values of 0.
You verify that the dedicated gateway cluster is provisioned and used in the connection string.
You need to ensure that App1 uses the Azure Cosmos DB integrated cache.
What should you configure?

• A.the indexing policy of the Azure Cosmos DB container
• B.the consistency level of the requests from App1
• C.the connectivity mode of the App1 CosmosClient
• D.the default consistency level of the Azure Cosmos DB account

Comment: *

Name: *

Email: *

Verification: *