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| Section | Weight | Objectives |
|---|---|---|
| Maintain an Azure Cosmos DB solution | 25-30% | - DevOps and automation
|
| Design and implement data distribution | 5-10% | - Design replication strategy
|
| Design and implement data models | 35-40% | - Design partitioning strategy
|
| Integrate an Azure Cosmos DB solution | 5-10% | - Integrate with Azure services
|
| Optimize an Azure Cosmos DB solution | 15-20% | - Work with change feed
|
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55. Frage
Hotspot Question
You configure Azure Cognitive Search to index a container in an Azure Cosmos DB for NoSQL 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.
Antwort:
Begründung:
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
56. Frage
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?
Antwort: B
Begründung:
You can facilitate aggregate data by using Change Feed and Azure Functions, and then use it for reporting.
57. Frage
Hotspot Question
You have an Azure subscription that contains an Azure Cosmos DB for NoSQL account.
You run the following 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.
Antwort:
Begründung:
58. Frage
You are designing an Azure Cosmos DB for NoSQL solution.
You plan to create a new container named Container1 that will store product data for automotive repair pans as shown in the following table.
The data will be stored in Container! as shown in the following table
You need to recommend a data partitioning solution for the database. The solution must spread the workload to minimize I/O contention.
Which column should you recommend for the physical partition key and the logical partition key? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
59. Frage
You need to create a data store for a directory of small and medium-sized businesses (SMBs). The data store must meet the following requirements:
* Store companies and the users employed by them. Each company will have less than 1,000 users.
* Some users have data that is greater than 2 KB.
* Associate each user to only one company.
* Provide the ability to browse by company.
* Provide the ability to browse the users by company.
* Whenever a company or user profile is selected, show a details page for the company and all the related users.
* Be optimized for reading data.
Which design should you implement to optimize the data store for reading data?
Antwort: C
Begründung:
Explanation
Azure Cosmos DB is a multi-model database that supports various data models, such as documents, key-value, graph, and column-family3. The core content-model of Cosmos DB's database engine is based on atom-record-sequence (ARS), which allows it to store and query different types of data in a flexible and efficient way3.
To optimize the data store for reading data, you should consider the following factors:
The size and shape of your data
The frequency and complexity of your queries
The latency and throughput requirements of your application
The trade-offs between storage efficiency and query performance
Based on these factors, one possible design that you could implement is B. In a company container, create a document for each company. Embed the users into company documents. Use the company ID as the partition key.
This design has the following advantages:
It stores companies and users as self-contained documents that can be easily retrieved by company ID1.
It avoids storing redundant data or creating additional containers for users1.
It allows you to browse by company and browse the users by company with simple queries1.
It shows a details page for the company and all the related users by fetching a single document1.
It leverages the benefits of embedding data, such as reducing the number of requests, improving query performance, and simplifying data consistency2.
This design also has some limitations, such as:
It may not be suitable for some users who have data that is greater than 2 KB, as it could exceed the maximum document size limit of 2 MB2.
It may not be optimal for scenarios where users need to be associated with more than one company or queried independently from companies2.
It may not be scalable for companies that have more than 1,000 users, as it could result in hot partitions or throttling2.
Depending on your specific use case and requirements, you may need to adjust this design or choose a different one. For example, you could use a hybrid data model that combines embedding and referencing data2
, or you could use a graph data model that expresses entities and relationships as vertices and edges.
60. Frage
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