Microsoft DP-420 Dumps

The cost of a point read for a 1 KB item is 1 RU. The cost of other operations depends on factors such as item size, indexing policy, consistency level, and query complexity1. To minimize the consumption of RUs, you can optimize these factors according to your application needs.

For your scenario, one possible way to minimize the consumption of RUs associated to the reads by App1 is to change the consistency level of read requests to consistent prefix. Consistent prefix is a lower consistency level than session, which is the default consistency level for Azure Cosmos DB. Lower consistency levels consume fewer RUs than higher consistency levels2. Consistent prefix guarantees that reads never see out-of-order writes and that monotonic reads are preserved1. This may be suitable for your application if you can tolerate some eventual consistency.

Box 1: CompanyID

After you create a container with a unique key policy, the creation of a new or an update of an existing item resulting in a duplicate within a logical partition is prevented, as specified by the unique key constraint. The partition key combined with the unique key guarantees the uniqueness of an item within the scope of the container.

For example, consider an Azure Cosmos container with Email address as the unique key constraint and CompanyID as the partition key. When you configure the user's email address with a unique key, each item has a unique email address within a given CompanyID. Two items can't be created with duplicate email addresses and with the same partition key value.

Box 2: emailAddress

When DefaultTimeToLive is -1 then your Time to Live setting is On (No default)

Time to Live on a container, if present and the value is set to "-1", it is equal to infinity, and items don’t expire by default.

Time to Live on an item:

This Property is applicable only if DefaultTimeToLive is present and it is not set to null for the parent container.

If present, it overrides the DefaultTimeToLive value of the parent container.

 Azure Cosmos DB offers two different capacity modes: provisioned throughput and serverless1. Provisioned throughput mode allows you to configure a certain amount of throughput (expressed in Request Units per second or RU/s) that is provisioned on your databases and containers. You get billed for the amount of throughput you’ve provisioned, regardless of how many RUs were consumed1. Serverless mode allows you to run your database operations without having to configure any previously provisioned capacity. You get billed for the number of RUs that were consumed by your database operations and the storage consumed by your data1.

To create a database that minimizes costs, you should consider the following factors:

• The read and write volumes of your containers
• The predictability and variability of your traffic
• The latency and throughput requirements of your application
• The geo-distribution and availability needs of your data

Based on these factors, one possible option that you could choose is B. Create a provisioned throughput account. Set the throughput for coll1 to Autoscale. Set the throughput for coll2 and coll3 to Manual.

This option has the following advantages:

• It allows you to handle unpredictable read and write volumes for coll1 by using Autoscale, which automatically adjusts the provisioned throughput based on the current load1.
• It allows you to handle predictable read and write volumes for coll2 and coll3 by using Manual, which lets you specify a fixed amount of provisioned throughput that meets your performance needs1.
• It allows you to optimize your costs by paying only for the throughput you need for each container1.
• It allows you to enable geo-distribution for your account if you need to replicate your data across multiple regions1.

This option also has some limitations, such as:

• It may not be suitable for scenarios where all containers have intermittent or bursty traffic that is hard to forecast or has a low average-to-peak ratio1.
• It may not be optimal for scenarios where all containers have low or sporadic traffic that does not justify provisioned capacity1.
• It may not support availability zones or multi-master replication for your account1.

Depending on your specific use case and requirements, you may need to choose a different option. For example, you could use a serverless account if all containers have low or sporadic traffic that does not require predictable performance or geo-distribution1. Alternatively, you could use a provisioned throughput account with Manual for all containers if all containers have stable and consistent traffic that requires predictable performance or geo-distribution1.

Azure Monitor is a service that provides comprehensive monitoring for Azure resources, including Azure Cosmos DB. You can use Azure Monitor to collect, analyze, and alert on metrics and logs from your Azure Cosmos DB account. You can create alerts for Azure Cosmos DB using Azure Monitor based on the metrics, activity log events, or Log Analytics logs on your account1.

For your scenario, if you want to receive an alert when requests that target the database exceed the available request units per second (RU/s), you should use the Document Quota metric. This metric measures the percentage of RU/s consumed by your account or container. You can create an alert rule on this metric from the Azure portal by following these steps2:

• In the Azure portal, select the Azure Cosmos DB account you want to monitor.
• Under the Monitoring section of the sidebar, select Alerts, and then select New alert rule.
• In the Create alert rule pane, fill out the Scope section by selecting your subscription name and resource type (Azure Cosmos DB accounts).
• In the Condition section, select Add condition and choose Document Quota from the list of signals.
• In the Configure signal logic pane, specify the threshold value and operator for your alert condition. For example, you can choose Greater than or equal to 90 as the threshold value and operator to receive an alert when your RU/s consumption reaches 90% or more of your provisioned throughput.
• In the Alert rule details section, specify a name and description for your alert rule.
• In the Actions section, select Add action group and choose how you want to receive notifications for your alert. For example, you can choose Email/SMS/Push/Voice as an action type and enter your email address or phone number as a receiver.
• Review your alert rule settings and select Create alert rule to save it.

The indexing policy has an includedPaths array that contains only one path: /product/brand/? . This means that only the properties under /product/brand will be indexed. The ? symbol indicates that only scalar values will be indexed, not arrays or objects1.

The excludedPaths array contains a single path: /* . This means that all other properties will be excluded from indexing. The * symbol indicates a wildcard that matches any property name1.

Therefore, the paths /product/category , /product/[ ]/category , and /product/brand/tailspin will not be indexed.

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.

Box 1: No

Four containers with 1000 RU/s each.

Box 2: No

Max 8000 RU/s for db2. 8 containers, so 1000 RU/s for each container.

Box 3: Yes

Max 8000 RU/s for db2. 8 containers, so 1000 RU/s for each container. Can very well add an additional container.

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"

Box 1 = Randomized

Box 2 = Deterministic

Always Encrypted for Azure Cosmos DB supports two types of encryption: deterministic and randomized1. Deterministic encryption always produces the same encrypted value for any given plain text value. Randomized encryption produces a different encrypted value for the same plain text value.

For dateOfBirth, randomized encryption is the strongest type of encryption because it provides better protection against statistical analysis and brute-force attacks. Deterministic encryption would not be suitable for dateOfBirth because it could reveal patterns or allow equality comparisons1.

For healthStatus, deterministic encryption is the strongest type of encryption because it allows queries to perform equality comparisons and filters on the encrypted property. Randomized encryption would not be suitable for healthStatus because it would prevent any queries on the encrypted property1.

Box 1: Resource tokens

Resource tokens provide access to the application resources within a database. Resource tokens:

Provide access to specific containers, partition keys, documents, attachments, stored procedures, triggers, and UDFs.

Box 2: Azure Resource Manager API

You can use Azure Resource Manager to help deploy and manage your Azure Cosmos DB accounts, databases, and containers.

Box 1: No

Azure Cosmos DB's change feed is a great choice as a central data store in event sourcing architectures where all data ingestion is modeled as writes (no updates or deletes).

Note: The change feed does not capture deletes. If you delete an item from your container, it is also removed from the change feed. The most common method of handling this is adding a soft marker on the items that are being deleted. You can add a property called "deleted" and set it to "true" at the time of deletion. This document update will show up in the change feed. You can set a TTL on this item so that it can be automatically deleted later.

Box 2: No

The _etag format is internal and you should not take dependency on it, because it can change anytime.

Box 3: Yes

Change feed support in Azure Cosmos DB works by listening to an Azure Cosmos container for any changes.

Box 1: ConsistencyLevel

The ItemRequestOptions Class ConsistencyLevel property gets or sets the consistency level required for the request in the Azure Cosmos DB service.

Azure Cosmos DB offers 5 different consistency levels. Strong, Bounded Staleness, Session, Consistent Prefix and Eventual - in order of strongest to weakest consistency.

Box 2: _etag

The ItemRequestOptions class helped us implement optimistic concurrency by specifying that we wanted the SDK to use the If-Match header to allow the server to decide whether a resource should be updated. The If-Match value is the ETag value to be checked against. If the ETag value matches the server ETag value, the resource is updated.

The partition key is what will determine how data is routed in the various partitions by Cosmos DB and needs to make sense in the context of your specific scenario. The IoT Device ID is generally the "natural" partition key for IoT applications.

Scenario: The iotdb database will contain two containers named con-iot1 and con-iot2.

Ensure that Azure Cosmos DB costs for IoT-related processing are predictable.

Bounded staleness is frequently chosen by globally distributed applications that expect low write latencies but require total global order guarantee. Bounded staleness is great for applications featuring group collaboration and sharing, stock ticker, publish-subscribe/queueing etc.

Scenario: Application updates in con-product frequently cause HTTP status code 429 "Too many requests". You discover that the 429 status code relates to excessive request unit (RU) consumption during the updates.

leaseCollectionPrefix: when set, the value is added as a prefix to the leases created in the Lease collection for this Function. Using a prefix allows two separate Azure Functions to share the same Lease collection by using different prefixes.

Scenario: Use Azure Functions to send notifications about product updates to different recipients.

Trigger the execution of two Azure functions following every update to any document in the con-product container.

Scenario: Develop an app named App2 that will run from the retail stores and query the data in account2. App2 must be limited to a single DNS endpoint when accessing account2.

By using Azure Private Link, you can connect to an Azure Cosmos account via a private endpoint. The private endpoint is a set of private IP addresses in a subnet within your virtual network.

When you're using Private Link with an Azure Cosmos account through a direct mode connection, you can use only the TCP protocol. The HTTP protocol is not currently supported.

App1 queries the con-product and con-productVendor containers.

Note: Request unit is a performance currency abstracting the system resources such as CPU, IOPS, and memory that are required to perform the database operations supported by Azure Cosmos DB.

Scenario:

Develop an app named App1 that will run from all locations and query the data in account1.

Once multi-region writes are configured, maximize the performance of App1 queries against the data in account1.

Whenever there are multiple solutions for a requirement, select the solution that provides the best performance, as long as there are no additional costs associated.