DP-900 Practice Test 46 — 15 Questions

Question 1

A company stores customer contact information in a table with columns for CustomerID, Name, Email, and Phone. They also store customer support chat transcripts as plain text files. Which of the following correctly classifies these data types?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Customer contact information is structured; chat transcripts are unstructured. Customer contact information stored in a table with columns like CustomerID, Name, Email, and Phone is structured data because it has a fixed schema with rows and columns. Chat transcripts stored as plain text files have no predefined schema or organization, making them unstructured data. Therefore, option C correctly classifies the contact info as structured and the chat transcripts as unstructured.

Answer

Both are structured data

Answer

Customer contact information is structured; chat transcripts are semi-structured

Answer

Both are semi-structured

Question 2

A logistics company collects sensor data from delivery trucks. Each sensor sends a JSON message that includes a fixed set of core fields (truck ID, timestamp) but also includes optional fields such as temperature, humidity, and engine diagnostics depending on the sensor type. The JSON structure varies between messages. How should this data be classified?

Accepted Answer

Semi-structured data. The JSON messages contain a fixed set of core fields (truck ID, timestamp) but also include optional fields that vary per message, meaning the data has a flexible schema. This mixture of structured fields and variable attributes is the defining characteristic of semi-structured data, which does not require a rigid schema like a relational table but still has organizational properties (e.g., key-value pairs). In Azure, this type of data is commonly stored in services like Azure Cosmos DB or Azure Blob Storage with JSON format.

Answer

Structured data

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Unstructured data

Answer

Relational data

Question 3

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

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Azure Cosmos DB Cassandra API

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Azure Cosmos DB Gremlin API

Question 4

Your application uses Azure Table storage to store user preferences. You need to retrieve all preferences for a specific user quickly. Which key should you use as the partition key?

Accepted Answer

UserID. In Azure Table Storage, the partition key determines which partition stores the entity, and entities with the same partition key are stored together and can be retrieved with a single partition scan. Using UserID as the partition key groups all preferences for a user into one partition, enabling fast point queries by PartitionKey + RowKey.

Answer

Region

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Random GUID

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Timestamp

Question 5

An organization stores sensitive customer data in Azure SQL Database. They need to encrypt the data at rest and ensure that only authorized applications can decrypt it. Which combination of features should they implement?

Accepted Answer

Transparent Data Encryption (TDE) and Always Encrypted. Transparent Data Encryption (TDE) encrypts the database, backups, and logs at rest at the storage layer, protecting against physical media theft. Always Encrypted goes further by encrypting specific columns client-side so that even DBAs and the SQL engine cannot see plaintext — only authorized client applications holding the column encryption keys can decrypt. Together they satisfy both 'encrypt at rest' and 'only authorized applications can decrypt'.

Answer

Row-Level Security (RLS) and Transparent Data Encryption (TDE)

Answer

Azure SQL Database Auditing and Transparent Data Encryption (TDE)

Answer

Dynamic Data Masking and Always Encrypted

Question 6

A data engineering team is building a batch analytics pipeline. Raw clickstream data is stored as Parquet files in Azure Data Lake Storage Gen2. The team needs to transform the data using Apache Spark (Python code) and then load the results into Azure Synapse Analytics for high-performance reporting. They want to use a serverless compute option for Spark to avoid managing clusters. Which combination of Azure services should they use for the transformation and loading?

Accepted Answer

Use Azure Synapse Analytics serverless Spark pools for transformations and load into the Synapse dedicated SQL pool.. Azure Synapse Analytics serverless Spark pools provide a serverless compute option for running Apache Spark transformations without managing clusters, and the transformed data can be directly loaded into the Synapse dedicated SQL pool for high-performance reporting. This combination meets all requirements: serverless Spark for transformations, and Synapse dedicated SQL pool for optimized analytics workloads.

Answer

Use Azure Databricks with a serverless cluster for transformations and load into Azure SQL Database.

Answer

Use Azure Data Factory with a Spark activity to run transformations and load into Azure Synapse Analytics.

Answer

Use Azure HDInsight with Apache Spark for transformations and load into Azure Blob Storage.

Question 7

A company uses Azure Synapse Analytics dedicated SQL pool to store a large fact table with billions of rows. The table is distributed using hash distribution on the SaleDate column. Queries that join this fact table with a small dimension table (Product) on ProductID are slow because the join requires shuffling data across distributions. Which design change would most improve the performance of these join queries?

Accepted Answer

Replicate the Product dimension table to all distributions.. Replicating the Product dimension table to all distributions eliminates the need to shuffle data across distributions during the join. In Azure Synapse dedicated SQL pool, hash distribution distributes rows across 60 distributions based on the hash of the distribution column (SaleDate). When joining on ProductID, which is not the distribution column, data must be moved between distributions. Replicating the small dimension table ensures each distribution has a local copy, allowing the join to be performed without data movement, significantly improving performance.

Answer

Change the distribution of the fact table to round-robin.

Answer

Partition the fact table by SaleDate.

Answer

Create a nonclustered index on ProductID in the fact table.

Question 8

A company stores customer data in a SQL table with fixed columns (CustomerID, Name, Email, SignupDate). They also store product images as JPEG files and application logs as JSON documents. Which of the following correctly classifies each data type?

Accepted Answer

SQL table: structured, JPEG: unstructured, JSON: semi-structured. A SQL table with fixed columns enforces a rigid schema, making it structured data. JPEG files are binary blobs with no internal schema, classifying them as unstructured. JSON documents use key-value pairs with flexible schemas, which is the definition of semi-structured data.

Answer

SQL table: structured, JPEG: semi-structured, JSON: unstructured

Answer

SQL table: semi-structured, JPEG: unstructured, JSON: structured

Answer

SQL table: unstructured, JPEG: structured, JSON: semi-structured

Question 9

A development team is designing an application that stores user session data in Azure Cosmos DB. Each session document contains a sessionId (unique), userId, timestamp, and a JSON field 'metadata' that can include various optional properties. The application frequently queries by userId to retrieve all sessions for a particular user. Which property should be chosen as the partition key to optimize query performance and ensure even data distribution?

Accepted Answer

userId. The partition key should be the property most frequently used in queries and that provides high cardinality for even distribution. Since the application frequently queries by userId to retrieve all sessions for a user, choosing userId as the partition key ensures that all session documents for a given user are stored in the same logical partition, making these queries efficient and fast. Additionally, userId typically has a large number of distinct values, which promotes even data distribution across physical partitions.

Answer

sessionId

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timestamp

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metadata

Question 10

Your team needs to store JSON documents that require schema flexibility and global distribution. Which Azure data store should you choose?

Accepted Answer

Azure Cosmos DB. Azure Cosmos DB is a globally distributed, multi-model database service that natively supports JSON documents with schema flexibility. It offers turnkey global distribution, multi-master replication, and low-latency access worldwide, making it the ideal choice for JSON documents requiring global distribution.

Answer

Azure Table Storage

Answer

Azure SQL Database

Answer

Azure Blob Storage

Question 11

A mobile gaming company is building a new feature that stores player profiles and game settings as key-value pairs. The development team is most familiar with SQL queries and wants to minimize the learning curve. They require low-latency reads and writes, and the data does not require complex joins. Which Azure Cosmos DB API should they choose?

Accepted Answer

A. Core (SQL) API. The Core (SQL) API is the correct choice because it provides native support for SQL queries, which aligns with the development team's familiarity with SQL and minimizes the learning curve. It stores data in JSON documents with key-value pairs, supports low-latency reads and writes, and does not require complex joins, making it ideal for player profiles and game settings.

Answer

B. Azure Cosmos DB for Table API

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C. Azure Cosmos DB for MongoDB API

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D. Azure Cosmos DB for Cassandra API

Question 12

A social media company stores user posts in Azure Cosmos DB. Each post document contains fields like postId, userId, content, timestamp, and an array of comments. The comments array can grow large (hundreds per post), and the application frequently retrieves a post without its comments to display in a feed. To optimize read performance and minimize request units (RU) consumption, which data modeling approach should the company adopt?

Accepted Answer

B. Store comments as separate documents and reference them from the post document via a comments array of IDs.. Storing comments as separate documents and referencing them via an array of IDs in the post document allows the application to retrieve the post without comments in a single point read, consuming minimal request units (RUs). This avoids loading the large comments array when only the post metadata is needed for the feed, significantly reducing RU consumption and improving read performance in Azure Cosmos DB.

Answer

A. Store comments in a separate container to isolate the data.

Answer

C. Use a vertical partition within the same document to separate the comments array.

Answer

D. Migrate the data to Azure SQL Database to use normalized tables and indexes.

Question 13

Your organization has a large dataset of customer transactions stored in Azure Blob Storage as CSV files. You need to run ad-hoc SQL queries on this data without loading it into a database. Which Azure service should you use?

Accepted Answer

Azure Synapse Serverless SQL pool. Azure Synapse Serverless SQL pool allows you to query data directly from files in Azure Blob Storage using standard T-SQL syntax, without needing to load or move the data into a database. It uses a pay-per-query model and supports CSV, Parquet, and JSON formats, making it ideal for ad-hoc analytical queries over large datasets stored in data lakes.

Answer

Azure Data Factory

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Azure SQL Database

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Azure Analysis Services

Question 14

A company wants to analyze customer feedback from surveys and social media. The data includes both structured (ratings) and unstructured (comments) text. They plan to use Azure Cognitive Services for sentiment analysis. Which service should they use for text analytics?

Accepted Answer

Azure AI Language. Azure AI Language (formerly part of Azure Cognitive Services) provides pre-built text analytics capabilities, including sentiment analysis, key phrase extraction, and language detection. This service is specifically designed to process unstructured text data like survey comments and social media posts, making it the correct choice for analyzing customer feedback.

Answer

Azure Synapse Analytics

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Azure Cosmos DB

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Azure Machine Learning

Question 15

A company stores customer data in a relational database with columns like CustomerID, Name, and Email. They also store product images as JPEG files in Azure Blob Storage, and customer feedback as JSON documents that contain varying fields such as rating, comments, and optional metadata. Which of the following correctly orders these data types from most structured to least structured?

Accepted Answer

Relational data, JSON, images. Relational data (CustomerID, Name, Email) is the most structured because it enforces a fixed schema with defined data types and constraints. JSON documents (customer feedback) are semi-structured: they have a flexible schema with optional fields like metadata, but still use key-value pairs. Images (JPEG files) are unstructured binary data with no inherent schema. Option C correctly orders them from most structured (relational) to least structured (images).

Answer

Relational data, images, JSON

Answer

Images, JSON, relational data

Answer

JSON, relational data, images