Microsoft DP-420 시험

Question No : 1

HOTSPOT
You plan to implement con-iot1 and con-iot2.
You need to configure the default Time to Live setting for each container. The solution must meet the loT telemetry requirements.
What should you configure? To answer, select the appropriate options in the answer NOTE: Each correct selection is worth one point.

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Explanation:
Box 1 = On (no default)
For con-iot1, you need to configure the default TTL setting to On (no default), which means that items in this container do not expire by default, but you can override the TTL value on a per-item basis. This meets the requirement of retaining all telemetry data unless overridden1. Box 2 = On (3600 seconds)
For con-iot2, you need to configure the default TTL setting to On (3600 seconds), which means that items in this container will expire 3600 seconds (one hour) after their last modified time. This meets the requirement of deleting all telemetry data older than one hour1.

Question No : 2

You have an Azure Cosmos DB Core (SQL) API account that is used by 10 web apps.
You need to analyze the data stored in the account by using Apache Spark to create machine learning models. The solution must NOT affect the performance of the web apps.
Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

• A.In an Apache Spark pool in Azure Synapse, create a table that uses cosmos.olap as the data source.
• B.Create a private endpoint connection to the account.
• C.In an Azure Synapse Analytics serverless SQL pool, create a view that uses OPENROWSET and the CosmosDB provider.
• D.Enable Azure Synapse Link for the account and Analytical store on the container.
• E.In an Apache Spark pool in Azure Synapse, create a table that uses cosmos.oltp as the data source.

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Explanation:
Reference: https://github.com/microsoft/MCW-Cosmos-DB-Real-Time-Advanced-Analytics/blob/main/Hands-on%20lab/HOL%20step-by%20step%20-%20Cosmos%20DB%20real-time%20advanced%20analytics.md

Question No : 3

HOTSPOT
You have an Azure Cosmos DB Core (SQL) API account named account1.
In account1, you run the following query in a container that contains 100GB of data.
SELECT *
FROM c
WHERE LOWER(c.categoryid) = "hockey"
You view the following metrics while performing the query.



For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

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Explanation:
Box 1: No
Each physical partition should have its own index, but since no index is used, the query is not cross-partition.
Box 2: No
Index utilization is 0% and Index Look up time is also zero.
Box 3: Yes
A partition key index will be created, and the query will perform across the partitions.
Reference: https://docs.microsoft.com/en-us/azure/cosmos-db/sql/how-to-query-container

Question No : 4

HOTSPOT
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.

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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

Question No : 5

You have a database in an Azure Cosmos DB Core (SQL) API account.
You need to create an Azure function that will access the database to retrieve records based on a variable named accountnumber. The solution must protect against SQL injection attacks.
How should you define the command statement in the function?

• A.cmd = "SELECT * FROM Persons p WHERE p.accountnumber = 'accountnumber'"
• B.cmd = "SELECT * FROM Persons p WHERE p.accountnumber = LIKE @accountnumber"
• C.cmd = "SELECT * FROM Persons p WHERE p.accountnumber = @accountnumber"
• D.cmd = "SELECT * FROM Persons p WHERE p.accountnumber = '" + accountnumber + "'"

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Explanation:
Azure Cosmos DB supports queries with parameters expressed by the familiar @ notation. Parameterized SQL provides robust handling and escaping of user input, and prevents accidental exposure of data through SQL injection.
For example, you can write a query that takes lastName and address.state as parameters, and execute it for various values of lastName and address.state based on user input. SELECT *
FROM Families f
WHERE f.lastName = @lastName AND f.address.state = @addressState
Reference: https://docs.microsoft.com/en-us/azure/cosmos-db/sql/sql-query-parameterized-queries

Question No : 6

HOTSPOT
You have an Azure Cosmos DB Core (SQL) API account used by an application named App1.
You open the Insights pane for the account and see the following chart.



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

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Explanation:
Box 1: incorrect connection URLs
400 Bad Request: Returned when there is an error in the request URI, headers, or body. The response body will contain an error message explaining what the specific problem is.
The HyperText Transfer Protocol (HTTP) 400 Bad Request response status code indicates that the server cannot or will not process the request due to something that is perceived to be a client error (for example, malformed request syntax, invalid request message framing, or deceptive request routing).
Box 2: 6 thousand
201 Created: Success on PUT or POST. Object created or updated successfully.
Note:
200 OK: Success on GET, PUT, or POST. Returned for a successful response.
404 Not Found: Returned when a resource does not exist on the server. If you are managing or querying an index, check the syntax and verify the index name is specified correctly.
Reference: https://docs.microsoft.com/en-us/rest/api/searchservice/http-status-codes

Question No : 7

You have a database in an Azure Cosmos DB Core (SQL) API account. The database is backed up every two hours.
You need to implement a solution that supports point-in-time restore.
What should you do first?

• A.Enable Continuous Backup for the account.
• B.Configure the Backup & Restore settings for the account.
• C.Create a new account that has a periodic backup policy.
• D.Configure the Point in Time Restore settings for the account.

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Explanation:
Reference: https://docs.microsoft.com/en-us/azure/cosmos-db/provision-account-continuous-backup

Question No : 8

You have an Azure Cosmos DB Core (SQL) API account.
You configure the diagnostic settings to send all log information to a Log Analytics workspace.
You need to identify when the provisioned request units per second (RU/s) for resources within the account were modified.
You write the following query.
AzureDiagnostics
| where Category == "ControlPlaneRequests"
What should you include in the query?

• A.| where OperationName startswith "AccountUpdateStart"
• B.| where OperationName startswith "SqlContainersDelete"
• C.| where OperationName startswith "MongoCollectionsThroughputUpdate"
• D.| where OperationName startswith "SqlContainersThroughputUpdate"

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Explanation:
The following are the operation names in diagnostic logs for different operations:
RegionAddStart, RegionAddComplete
RegionRemoveStart, RegionRemoveComplete
AccountDeleteStart, AccountDeleteComplete
RegionFailoverStart, RegionFailoverComplete AccountCreateStart, AccountCreateComplete *AccountUpdateStart*, AccountUpdateComplete VirtualNetworkDeleteStart, VirtualNetworkDeleteComplete DiagnosticLogUpdateStart, DiagnosticLogUpdateComplete
Reference: https://docs.microsoft.com/en-us/azure/cosmos-db/audit-control-plane-logs

Question No : 9

You have a container named container1 in an Azure Cosmos DB Core (SQL) API account.
You need to provide a user named User1 with the ability to insert items into container1 by using role-based access control (RBAC). The solution must use the principle of least privilege.
Which roles should you assign to User1?

• A.CosmosDB Operator only
• B.DocumentDB Account Contributor and Cosmos DB Built-in Data Contributor
• C.DocumentDB Account Contributor only
• D.Cosmos DB Built-in Data Contributor only

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Explanation:
Cosmos DB Operator: Can provision Azure Cosmos accounts, databases, and containers. Cannot
access any data or use Data Explorer.
Incorrect Answers:
B: DocumentDB Account Contributor can manage Azure Cosmos DB accounts. Azure Cosmos DB is formerly known as DocumentDB.
C: DocumentDB Account Contributor: Can manage Azure Cosmos DB accounts.
Reference: https://docs.microsoft.com/en-us/azure/cosmos-db/role-based-access-control

Question No : 10

You are implementing an Azure Data Factory data flow that will use an Azure Cosmos DB (SQL API) sink to write a dataset. The data flow will use 2,000 Apache Spark partitions.
You need to ensure that the ingestion from each Spark partition is balanced to optimize throughput.
Which sink setting should you configure?

• A.Throughput
• B.Write throughput budget
• C.Batch size
• D.Collection action

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정답:
Explanation:
Batch size: An integer that represents how many objects are being written to Cosmos DB collection in each batch. Usually, starting with the default batch size is sufficient. To further tune this value, note: Cosmos DB limits single request's size to 2MB. The formula is "Request Size = Single Document Size * Batch Size". If you hit error saying "Request size is too large", reduce the batch size value.
The larger the batch size, the better throughput the service can achieve, while make sure you allocate enough RUs to empower your workload. Incorrect Answers:
A: Throughput: Set an optional value for the number of RUs you'd like to apply to your CosmosDB collection for each execution of this data flow. Minimum is 400.
B: Write throughput budget: An integer that represents the RUs you want to allocate for this Data Flow write operation, out of the total throughput allocated to the collection.
D: Collection action: Determines whether to recreate the destination collection prior to writing. None: No action will be done to the collection.
Recreate: The collection will get dropped and recreated
Reference: https://docs.microsoft.com/en-us/azure/data-factory/connector-azure-cosmos-db

Question No : 11

You need to configure an Apache Kafka instance to ingest data from an Azure Cosmos DB Core (SQL)
API account. The data from a container named telemetry must be added to a Kafka topic named iot.
The solution must store the data in a compact binary format.
Which three configuration items should you include in the solution? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

• A."connector.class": "com.azure.cosmos.kafka.connect.source.CosmosDBSourceConnector"
• B."key.converter": "org.apache.kafka.connect.json.JsonConverter"
• C."key.converter": "io.confluent.connect.avro.AvroConverter"
• D."connect.cosmos.containers.topicmap": "iot#telemetry"
• E."connect.cosmos.containers.topicmap": "iot"
• F."connector.class": "com.azure.cosmos.kafka.connect.source.CosmosDBSinkConnector"

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Explanation:
C: Avro is binary format, while JSON is text.
F: Kafka Connect for Azure Cosmos DB is a connector to read from and write data to Azure Cosmos DB. The Azure Cosmos DB sink connector allows you to export data from Apache Kafka topics to an Azure Cosmos DB database. The connector polls data from Kafka to write to containers in the database based on the topics subscription.
D: Create the Azure Cosmos DB sink connector in Kafka Connect. The following JSON body defines config for the sink connector.
Extract:
"connector.class": "com.azure.cosmos.kafka.connect.sink.CosmosDBSinkConnector",
"key.converter": "org.apache.kafka.connect.json.AvroConverter"
"connect.cosmos.containers.topicmap": "hotels#kafka" Incorrect Answers:
B: JSON is plain text. Note, full example:
{
"name": "cosmosdb-sink-connector",
"config": {
"connector.class": "com.azure.cosmos.kafka.connect.sink.CosmosDBSinkConnector",
"tasks.max": "1",
"topics": [ "hotels" ],
"value.converter": "org.apache.kafka.connect.json.AvroConverter",
"value.converter.schemas.enable": "false",
"key.converter": "org.apache.kafka.connect.json.AvroConverter",
"key.converter.schemas.enable": "false",
"connect.cosmos.connection.endpoint": "https://<cosmosinstance-name>.documents.azure.com:443/",
"connect.cosmos.master.key": "<cosmosdbprimarykey>",
"connect.cosmos.databasename": "kafkaconnect",
"connect.cosmos.containers.topicmap": "hotels#kafka"
}
}
Reference:
https://docs.microsoft.com/en-us/azure/cosmos-db/sql/kafka-connector-sink
https://www.confluent.io/blog/kafka-connect-deep-dive-converters-serialization-explained/

Question No : 12

You plan to create an Azure Cosmos DB Core (SQL) API account that will use customer-managed keys stored in Azure Key Vault.
You need to configure an access policy in Key Vault to allow Azure Cosmos DB access to the keys.
Which three permissions should you enable in the access policy? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

• A.Wrap Key
• B.Get
• C.List
• D.Update
• E.Sign
• F.Verify
• G.Unwrap Key

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Explanation:
Reference: https://docs.microsoft.com/en-us/azure/cosmos-db/how-to-setup-cmk

Question No : 13

HOTSPOT
You have an Azure Cosmos DB Core (SQL) API account named account1.
You have the Azure virtual networks and subnets shown in the following table.



The vnet1 and vnet2 networks are connected by using a virtual network peer.
The Firewall and virtual network settings for account1 are configured as shown in the exhibit.



For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

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Explanation:
Box 1: Yes
VM1 is on vnet1.subnet1 which has the Endpoint Status enabled.
Box 2: No
Only virtual network and their subnets added to Azure Cosmos account have access. Their peered VNets cannot access the account until the subnets within peered virtual networks are added to the account.
Box 3: No
Only virtual network and their subnets added to Azure Cosmos account have access.
Reference: https://docs.microsoft.com/en-us/azure/cosmos-db/how-to-configure-vnet-service-endpoint

Question No : 14

HOTSPOT
You have a database in an Azure Cosmos DB SQL API Core (SQL) account that is used for development.
The database is modified once per day in a batch process.
You need to ensure that you can restore the database if the last batch process fails. The solution must minimize costs.
How should you configure the backup settings? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

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Question No : 15

You have the following query.
SELECT * FROM с
WHERE c.sensor = "TEMP1"
AND c.value < 22
AND c.timestamp >= 1619146031231
You need to recommend a composite index strategy that will minimize the request units (RUs) consumed by the query.
What should you recommend?

• A.a composite index for (sensor ASC, value ASC) and a composite index for (sensor ASC, timestamp ASC)
• B.a composite index for (sensor ASC, value ASC, timestamp ASC) and a composite index for (sensor DESC, value DESC, timestamp DESC)
• C.a composite index for (value ASC, sensor ASC) and a composite index for (timestamp ASC, sensor ASC)
• D.a composite index for (sensor ASC, value ASC, timestamp ASC)

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Explanation:
If a query has a filter with two or more properties, adding a composite index will improve performance.
Consider the following query:
SELECT * FROM c WHERE c.name = “Tim” and c.age > 18
In the absence of a composite index on (name ASC, and age ASC), we will utilize a range index for this query. We can improve the efficiency of this query by creating a composite index for name and age. Queries with multiple equality filters and a maximum of one range filter (such as >,<, <=, >=, !=) will utilize the composite index.
Reference: https://azure.microsoft.com/en-us/blog/three-ways-to-leverage-composite-indexes-in-azure-cosmos-db/