DP-900 Describe considerations for working with non-relational data on Azure Practice Test 3 — 15 Questions

Question 1

Refer to the exhibit. The exhibit shows a query used in Azure Cosmos DB. What is the correct interpretation of this query?

Accepted Answer

It queries an Azure Cosmos DB container for items where the age property exceeds 25.. In Azure Cosmos DB SQL API, 'c' is an alias for each item (document) in the container. The query retrieves all items where the 'age' property is greater than 25. Option A is wrong because while the syntax is SQL-like, it is specific to Azure Cosmos DB's SQL API, not SQL Server. Option C is wrong because the query is a standard SELECT statement, not a command to execute a stored procedure. Option D is wrong because 'c' is an alias for items within a container, not a database, and the WHERE clause ensures only items matching the condition are retrieved, not all documents.

Answer

It is a SQL Server query that selects from a table named 'c'.

Answer

It executes a stored procedure in Azure Cosmos DB.

Answer

It retrieves all documents from the Azure Cosmos DB database named 'c'.

Question 2

A company uses Azure Cosmos DB with the MongoDB API for a customer profile service. The service handles 10,000 writes per second and 50,000 reads per second. The data is 1 KB per document. The company needs to reduce read latency for frequently accessed customers and minimize RU consumption. Currently, the service reads the entire document for every request. They decide to implement a materialized view pattern using Azure Cosmos DB change feed and a separate container. Which additional step should they take to optimize read performance and cost?

Accepted Answer

Create a materialized view container with a partition key optimized for the read queries.. Creating a materialized view container with a partition key optimized for read queries (option A) reduces RU consumption and latency by pre-aggregating or projecting only the needed fields and using a partition key that matches the read pattern. Option B (stored procedures) is for transactional logic, not read optimization. Option C (increasing RU) does not address the root cause of reading full documents. Option D (TTL) would delete data, not improve reads.

Answer

Use stored procedures to aggregate data on read.

Answer

Increase the provisioned RU/s on the source container.

Answer

Enable Time-to-Live (TTL) on the source container to automatically expire old data.

Question 3

A company stores log files in Azure Blob Storage. Each log file is a CSV file ranging from 100 MB to 1 GB. They need to query the logs using SQL queries and plan to use Azure Synapse Serverless SQL. The logs are stored in a container named 'logs' with a folder structure like 'year=2023/month=01/day=01/logfile.csv'. They want to optimize query performance and reduce data scanned. Which partitioning strategy should they implement?

Accepted Answer

Use the existing folder structure with year/month/day partitions.. Partitioning by year, month, and day using the existing folder structure (option B) allows Synapse Serverless SQL to perform partition elimination, reducing the amount of data scanned. Option A (converting to Parquet in a single folder) improves columnar storage but does not provide partition pruning. Option C (consolidating into a single large CSV file) eliminates all partitioning and increases data scanned. Option D (random file names) prevents partition elimination.

Answer

Convert logs to Parquet format and store in a single folder.

Answer

Consolidate all logs into a single large CSV file.

Answer

Use random file names to distribute load.

Question 4

A company uses Azure Table Storage to store customer session data. Each session has a PartitionKey (CustomerId) and RowKey (SessionId). They need to retrieve all sessions for a specific customer quickly. Which query design will be most efficient?

Accepted Answer

Query with PartitionKey equal to the specific CustomerId.. The most efficient query design is to filter by PartitionKey (CustomerId) as shown in option D. This allows Azure Table Storage to directly access the specific partition containing the customer's sessions, avoiding a full table scan. Option A (selecting all and filtering client-side) requires scanning the entire table and transferring unnecessary data. Option B (creating a secondary index on RowKey) is not supported in standard Azure Table Storage. Option C (querying by RowKey alone) would need to scan all partitions because the RowKey is only unique within a partition.

Answer

Select all entities and filter client-side.

Answer

Create a secondary index on RowKey.

Answer

Query with RowKey equal to a specific SessionId.

Question 5

A company uses Azure Cosmos DB with the Cassandra API for a time-series telemetry application. The data model uses a composite primary key (device_id, timestamp). The application queries telemetry for a specific device within a time range. Recently, query performance has degraded as data volume grows. The RU consumption per query is high. Which action should they take to improve performance and reduce RU cost?

Accepted Answer

Change the clustering order to descending on timestamp.. Changing the clustering order to descending on timestamp (option D) allows the most recent data to be retrieved without scanning all rows, reducing RU consumption. Option A (changing the partition key to timestamp) would scatter device data across partitions, hurting queries by device. Option B (increasing provisioned RU/s) does not improve efficiency of the query itself. Option C (adding a secondary index on timestamp) would increase RU costs and is less efficient than clustering order.

Answer

Change the partition key to timestamp.

Answer

Increase the provisioned RU/s on the container.

Answer

Add a secondary index on the timestamp column.

Question 6

A company stores user profile data in Azure Blob Storage as JSON files. Each file represents one user. They need to provide real-time search capabilities on user attributes like name, email, and location. The search must support partial matches and return results within 500 ms. The data volume is 10 TB and grows by 1 GB daily. They have a limited budget and want to minimize operational overhead. Which Azure solution should they choose?

Accepted Answer

Use Azure Cognitive Search to index the JSON files and provide search capabilities.. Azure Cognitive Search (option C) is a managed search service that indexes JSON data from Blob Storage and supports partial matches with low latency. Option A (Azure Cosmos DB) requires data migration and may not support partial match efficiently. Option B (Azure Data Lake Analytics) is for batch processing, not real-time search. Option D (Azure SQL Database) requires importing data and schema changes, adding operational overhead.

Answer

Import the JSON files into Azure Cosmos DB and use the SQL API to search.

Answer

Use Azure Data Lake Analytics with U-SQL to query the files.

Answer

Load the data into Azure SQL Database and create full-text indexes.

Question 7

A social media application stores user profile data as JSON documents. Each user's document has a different structure, with fields that vary based on user activity. The application needs to query these documents efficiently using SQL-like syntax and support high write throughput. Which Azure data store is most appropriate for this workload?

Accepted Answer

Azure Cosmos DB. Azure Cosmos DB is the most appropriate choice because it natively supports storing and querying JSON documents with varying schemas, offers SQL-like query syntax via its core (SQL) API, and provides guaranteed low-latency reads/writes at any scale with automatic indexing of all fields. Its multi-model nature and configurable consistency levels make it ideal for high-throughput workloads like a social media application.

Answer

Azure SQL Database

Answer

Azure Blob Storage

Answer

Azure Table Storage

Question 8

A ride-sharing application needs to store real-time GPS location updates from drivers and passengers. The data is ingested as key-value pairs where the key is the user ID and the value is a timestamped location. The application requires low-latency reads and writes for millions of concurrent users, and the data model is simple with no need for complex queries or joins. Which Azure NoSQL database API should be used for this workload?

Accepted Answer

Azure Cosmos DB Table API. Azure Cosmos DB Table API is the correct choice because it provides a key-value store with low-latency reads and writes, ideal for high-throughput scenarios like real-time GPS updates. It supports a simple schema-less data model where each item is a key-value pair, and it offers single-millisecond latency at the 99th percentile for both reads and writes, meeting the requirement for millions of concurrent users without complex queries or joins.

Answer

Azure Cosmos DB SQL (Core) API

Answer

Azure Cosmos DB for MongoDB API

Answer

Azure Cosmos DB for Apache Gremlin API

Question 9

A global social media platform stores user profile images (JPEG) and activity logs in JSON format. The logs have varying structures based on the type of activity. The application requires low-latency reads of images from any region and the ability to query logs using SQL-like syntax. Which Azure data storage solution should they use for each data type?

Accepted Answer

Azure Blob Storage with a CDN for images and Azure Cosmos DB (SQL API) for logs. Azure Blob Storage is optimized for storing large binary objects like JPEG images, and integrating it with Azure CDN ensures low-latency reads globally by caching content at edge nodes. Azure Cosmos DB with the SQL API provides native support for querying JSON documents with varying schemas using SQL-like syntax, making it ideal for the activity logs.

Answer

Azure Table Storage for images and Azure Cosmos DB (Table API) for logs

Answer

Azure Files for images and Azure SQL Database for logs

Answer

Azure Disk Storage for images and Azure Cosmos DB (MongoDB API) for logs

Question 10

A retail company stores product catalog data as JSON documents. Each product has a different set of attributes depending on its category (e.g., electronics have 'voltage', clothing has 'size'). The application needs to query products by category and price range efficiently. Which Azure data store is most appropriate for this workload?

Accepted Answer

Azure Cosmos DB. Azure Cosmos DB is the most appropriate choice because it natively supports JSON documents with flexible schemas, enabling each product to have a different set of attributes per category. Its indexing policies can be configured to efficiently support queries filtering by category and price range, and it offers low-latency, high-throughput access ideal for retail catalog workloads.

Answer

Azure SQL Database

Answer

Azure Blob Storage

Answer

Azure Table Storage

Question 11

A media company stores large video files and associated metadata (title, duration, tags) as JSON documents. The application requires low-latency streaming of videos to users worldwide and the ability to quickly query metadata by tag. Which combination of Azure services should the company use?

Accepted Answer

Azure Blob Storage for videos and Azure Cosmos DB for metadata. Azure Blob Storage is optimized for storing large binary objects like video files, offering high-throughput streaming via HTTP/HTTPS and integration with CDN for low-latency global delivery. Azure Cosmos DB provides single-digit millisecond read and write latencies with automatic indexing, making it ideal for quickly querying JSON metadata by tag using SQL or MongoDB API. This combination separates storage concerns (blobs for raw video, document DB for structured metadata) to meet both streaming and query performance requirements.

Answer

Azure Blob Storage for both videos and metadata

Answer

Azure Cosmos DB for videos and Azure Table Storage for metadata

Answer

Azure Files for videos and Azure SQL Database for metadata

Question 12

A social media application stores user profiles as JSON documents. Each user profile can have different attributes (e.g., some have 'education', others have 'work experience'). The application needs to query profiles by any attribute with low latency. Which Azure data store is most appropriate?

Accepted Answer

Azure Cosmos DB (SQL API). Azure Cosmos DB with the SQL API is the correct choice because it natively supports schema-agnostic JSON documents, allowing each user profile to have varying attributes without requiring a fixed schema. Its indexing policies enable low-latency queries on any attribute, and it provides single-digit millisecond response times for point reads and queries, which is essential for a social media application.

Answer

Azure Blob Storage

Answer

Azure Table Storage

Answer

Azure SQL Database

Question 13

A healthcare application stores patient medical records as JSON documents. Each document contains a variable set of fields depending on the patient's conditions. The application needs to query records by any field and support high write throughput. Which Azure data store is most appropriate?

Accepted Answer

Azure Cosmos DB with SQL API. Azure Cosmos DB with SQL API is the most appropriate choice because it natively supports storing and querying JSON documents with variable schemas, enabling efficient queries on any field. Its multi-model architecture and configurable indexing policies allow high write throughput while maintaining low-latency queries, which is critical for healthcare applications with dynamic patient records.

Answer

Azure Blob Storage

Answer

Azure Synapse Analytics

Answer

Azure Table Storage

Question 14

A manufacturing company collects sensor readings from thousands of IoT devices. Each reading consists of a device ID, a timestamp, and a numeric value. The data is stored as key-value pairs and must support low-latency reads and writes at a global scale. The company also needs to query the data by device ID and time range. Which Azure Cosmos DB API should they choose?

Accepted Answer

Table API. The Table API is the correct choice because it is designed for key-value workloads with a schema-less design, supporting low-latency reads and writes at global scale. It allows querying by partition key (device ID) and row key (timestamp) to efficiently retrieve data by device ID and time range, matching the IoT sensor data requirements.

Answer

Core (SQL) API

Answer

MongoDB API

Answer

Gremlin API

Question 15

A manufacturing company stores IoT sensor data as JSON documents in Azure Cosmos DB. Each document contains a device ID, a timestamp, and a varying set of sensor readings. The application frequently queries data by device ID and a time range to retrieve all readings for a specific device over a period. The development team wants to use an API that supports SQL-like queries on this JSON data. Which Azure Cosmos DB API should they choose?

Accepted Answer

Azure Cosmos DB Core (SQL) API. The Azure Cosmos DB Core (SQL) API is the correct choice because it natively supports querying JSON documents using SQL-like syntax, which aligns with the requirement to run SQL-like queries on JSON data. This API provides a rich query language for filtering by device ID and timestamp ranges, making it ideal for the described IoT scenario where documents have varying sensor readings.

Answer

Azure Cosmos DB MongoDB API

Answer

Azure Cosmos DB Cassandra API

Answer

Azure Cosmos DB Gremlin API