Microsoft Azure Data Fundamentals (DP-900)

B is correct.

Explanation: Azure Blob Storage stores massive amounts of unstructured data as binary large objects, or blobs, in the cloud. This matches the requirement for unstructured data stored as blobs.

A is incorrect: Azure Table Storage is a NoSQL solution that uses tables containing key/value data items. It does not store data as binary large objects.

C is incorrect: Azure Files creates cloud-based network shares for documents and other files. Use it when multiple users need access to a share, not when the requirement is blob storage.

D is incorrect: Microsoft OneLake is a data lake provisioned by Microsoft Fabric. The requirement is for nonrelational, unstructured data stored as blobs.

D is correct.

Explanation: Azure Data Lake Storage Gen2 is a cloud-scale data lake solution built into Azure Storage. This matches the requirement.

A is incorrect: Azure Files provides cloud-based network shares for multiple users. It is not described as a data lake solution.

B is incorrect: Azure Table Storage is a NoSQL solution that stores key/value data items in tables. It is not a data lake solution.

C is incorrect: Microsoft OneLake is provisioned by Microsoft Fabric and is built upon Azure Data Lake Gen 2. It is not the data lake solution built into Azure Storage itself.

A is correct.

Explanation: Microsoft Fabric automatically provisions OneLake, and OneLake is built upon Azure Data Lake Gen 2. This matches both parts of the requirement.

B is incorrect: OneLake is built upon Azure Data Lake Storage Gen2, but Azure Data Lake Storage Gen2 is not the item that Fabric automatically provisions.

C is incorrect: Azure Blob Storage stores unstructured data as blobs. It is not the data lake that Fabric automatically provisions.

D is incorrect: Azure Files provides cloud-based network shares. It is not the data lake that Fabric automatically provisions.

C is correct.

Explanation: Azure Files creates cloud-based network shares, like those found in on-premises organizations. These shares make documents and other files available to multiple users.

A is incorrect: Azure Blob Storage stores unstructured data as binary large objects. It is not described as a network share service.

B is incorrect: Azure Data Lake Storage Gen2 is a cloud-scale data lake solution built into Azure Storage. The requirement is for a network share, not a data lake.

D is incorrect: Azure Table Storage stores key/value data items in tables. It does not provide network shares for documents and files.

B is correct.

Explanation: Azure Table Storage is a NoSQL storage solution that uses tables containing key/value data items. This matches the requirement.

A is incorrect: Azure Files creates cloud-based network shares for multiple users. It does not store key/value data items in tables.

C is incorrect: Azure Blob Storage stores unstructured data as binary large objects. The requirement is for tables of key/value items.

D is incorrect: Azure Data Lake Storage Gen2 is a cloud-scale data lake solution built into Azure Storage. It is not described as a table-based key/value store.

B is correct.

Explanation: With autoscale, you set a maximum RU/s. Azure Cosmos DB then adjusts capacity automatically within that range based on actual demand.

A is incorrect: Dedicated throughput is reserved exclusively for a single container. It does not describe automatic adjustment within a maximum RU/s range.

C is incorrect: Shared throughput is provisioned at the database level and shared across up to 25 containers. It does not describe automatic adjustment based on demand.

D is incorrect: Serverless has no capacity to provision upfront, and you pay per request. You do not set a maximum RU/s, and serverless accounts are limited to a single Azure region.

C is correct.

Explanation: A well-chosen partition key has many distinct values and spreads data evenly across those values. A customer ID with similar order counts per customer meets both conditions. This helps keep throughput balanced as the database grows.

A is incorrect: A single shared value puts all items in one logical partition. Each logical partition can hold up to 20 GB of data.

B is incorrect: Three values do not provide many distinct values. Data is distributed across only three logical partitions.

D is incorrect: Most items share one value, so the data is not spread evenly. This does not keep throughput balanced.

D is correct.

Explanation: If an application depends on complex multi-table joins, Azure SQL Database is better suited than Azure Cosmos DB.

A is incorrect: Azure Cosmos DB for NoSQL stores JSON documents. It is a good fit for flexible schema, global reach, and low latency, not for complex multi-table joins.

B is incorrect: Azure Cosmos DB for Table stores data as key-value pairs. It does not address the requirement for complex multi-table joins.

C is incorrect: Azure Synapse Analytics is an option for large-scale historical analytics. The scenario describes an application that depends on joins, not historical analytics.

B and D are correct.

Explanation: Azure Cosmos DB for MongoDB is compatible with MongoDB drivers and client libraries, so existing MongoDB applications can connect without significant code changes. Azure Cosmos DB for Table uses the same programming model as Azure Table Storage, so existing Table Storage applications can connect with minimal code changes.

A is incorrect: Azure Cosmos DB for Apache Gremlin is designed for graph data and uses the Gremlin query language. Neither application uses a graph model.

C is incorrect: Azure Cosmos DB for NoSQL is the native API. It stores JSON documents and uses a SQL-like syntax. It does not match the MongoDB or Table Storage programming models.

A is correct.

Explanation: Azure Cosmos DB for Apache Gremlin is designed for graph data. Entities are vertices and relationships are edges. Gremlin is the query language used to traverse and manipulate graph data. Fraud detection is a listed use case for graph databases.

B is incorrect: Azure Cosmos DB for Apache Cassandra uses a column-family storage model and CQL. It is a good fit for migrating an Apache Cassandra workload, not for modeling relationships as edges.

C is incorrect: Azure Cosmos DB for Table stores data as key-value pairs in tables. Each row is identified by a PartitionKey and RowKey combination, which does not represent vertices and edges.

D is incorrect: Azure Cosmos DB for MongoDB stores data in BSON format and uses MQL. It is a good choice for teams with MongoDB expertise or MongoDB workloads, not for graph data.