DP-900 Practice Test 34 — 15 Questions

Question 1

Which THREE are characteristics of structured data? (Choose three.)

Accepted Answer

Has a predefined schema. Structured data has a predefined schema, meaning the data types, relationships, and constraints are defined before data is entered. This schema ensures consistency and enables efficient querying, which is why relational databases enforce a fixed schema through table definitions and constraints like primary keys and foreign keys.

Answer

Consists of audio and video files

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Uses JSON or XML format

Question 2

Which TWO Azure services are primarily used for data integration and orchestration?

Accepted Answer

Azure Logic Apps. Azure Logic Apps is correct because it is a serverless workflow service that integrates apps, data, and services using connectors and triggers, making it ideal for data integration and orchestration. Azure Data Factory is correct because it is a cloud-based ETL and data integration service that orchestrates and automates data movement and transformation across various data stores.

Answer

Azure Synapse Analytics

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Azure Stream Analytics

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Azure Analysis Services

Question 3

Which TWO of the following are characteristics of structured data?

Accepted Answer

It has a predefined schema. Structured data adheres to a predefined schema, meaning its fields, data types, and relationships are defined in advance, typically enforced by a database management system. This schema ensures consistency and enables efficient querying using SQL. Relational databases are the primary storage system for structured data, organizing it into tables with rows and columns that follow the schema.

Answer

It is stored in Azure Blob Storage as objects

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It can contain images and videos

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It uses tags to describe the data

Question 4

Which TWO of the following are correct descriptions of data processing workloads in Azure?

Accepted Answer

Streaming processing is used to process data in real time as it arrives.. Streaming processing in Azure (e.g., Azure Stream Analytics, Event Hubs, or Kafka on HDInsight) is designed to ingest, analyze, and act on data in near real-time as it arrives, often with sub-second latency. This is fundamentally different from batch processing, which handles data at rest.

Answer

Streaming processing is used for interactive queries on historical data.

Answer

Streaming processing is used to process data at rest.

Answer

Batch processing is used to process data in real time as it arrives.

Question 5

The exhibit shows a Kusto Query Language (KQL) query run in Azure Data Explorer. What is the output of this query?

Accepted Answer

The top 5 event types in Texas by total property damage. The query uses `summarize sum(PropertyDamage) by EventType` to aggregate total property damage per event type, then `top 5 by TotalPropertyDamage` to return the five event types with the highest totals. The `where State == 'TEXAS'` filter ensures only Texas storms are considered. This directly yields the top 5 event types in Texas by total property damage.

Answer

All storm events in Texas with property damage

Answer

The total property damage for all event types in Texas

Answer

A list of the top 5 property damage amounts in Texas

Question 6

You are reviewing a Data Factory mapping data flow definition. What is the primary purpose of this data flow?

Accepted Answer

Remove duplicate OrderIDs by counting them. The mapping data flow includes an Aggregate transformation configured with a group by on OrderID and a count aggregation. This removes duplicate OrderIDs by collapsing multiple rows with the same OrderID into a single row and counting the occurrences, which is the primary purpose of the data flow.

Answer

Pivot the data by OrderID

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Filter rows where OrderID is null

Answer

Merge two data sources

Question 7

Which THREE of the following are valid Azure data storage services? (Choose three.)

Accepted Answer

Azure Files. Azure Files provides fully managed file shares in the cloud that can be accessed via the Server Message Block (SMB) protocol or the Network File System (NFS) protocol. It is a valid Azure data storage service because it stores data as files in a hierarchical structure, making it suitable for lift-and-shift scenarios for on-premises file servers.

Answer

Azure Redis Cache

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Azure Service Bus

Question 8

A company stores customer transaction data in Azure Blob Storage. They need to query the data using SQL-based tools without moving the data. Which Azure service should they use?

Accepted Answer

Azure Synapse Serverless SQL pool. Azure Synapse Serverless SQL pool allows you to query data directly from Azure Blob Storage using T-SQL without moving or copying the data. It uses a pay-per-query model and supports reading common file formats like Parquet, CSV, and JSON, making it ideal for ad-hoc querying over data lakes.

Answer

Azure SQL Database

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Azure Analysis Services

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Azure Cosmos DB

Question 9

Your organization uses Microsoft Fabric to build a data lakehouse. Data engineers need to transform data using Spark and store results in Delta Lake format. Which Fabric component should they use?

Accepted Answer

Notebooks. Notebooks in Microsoft Fabric provide an interactive environment for writing and executing Spark code, which is required for transforming data using Spark. The results can be directly written to Delta Lake format, making Notebooks the correct component for this task.

Answer

Dataflows Gen2

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Pipelines

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Semantic models

Question 10

A company has an Azure SQL Database and needs to run a weekly data aggregation job that takes several hours. They want to minimize cost and avoid impacting production workload. Which approach should they use?

Accepted Answer

Create a read-only replica and run the aggregation on the replica. Creating a read-only replica allows the weekly aggregation job to run against a separate copy of the database without affecting the production workload. Since the replica is read-only, it incurs additional compute costs only during the aggregation window, and you can scale it down or stop it when not in use, minimizing overall cost.

Answer

Migrate the database to the Hyperscale service tier

Answer

Use Azure Elastic Jobs to run the aggregation during off-peak hours

Answer

Increase the DTU or vCore size of the database to handle the load

Question 11

You are a data architect at a global retail company. The company has an Azure Data Lake Storage Gen2 account that stores petabytes of clickstream data. They need to provide near real-time analytics dashboards for regional managers. The data arrives in batches every 5 minutes. Currently, they use Azure Databricks to transform the data and load it into Azure Synapse Analytics, but the dashboards show data that is 30 minutes old. The business requires dashboards to reflect data within 10 minutes of ingestion. You propose a new solution. Which approach should you recommend?

Accepted Answer

Ingest data into Azure Event Hubs, use Azure Stream Analytics to process and output to Power BI for real-time dashboards.. It uses Azure Event Hubs for low-latency ingestion and Azure Stream Analytics for real-time processing, enabling near real-time dashboards in Power BI with sub-minute latency. This architecture bypasses the batch-oriented pipeline that causes the current 30-minute delay, meeting the 10-minute requirement.

Answer

Keep current pipeline but replace Synapse with Azure Analysis Services for faster query performance.

Answer

Use Azure Data Factory with tumbling window triggers every 5 minutes to load data from Data Lake to Synapse.

Answer

Increase the number of Databricks clusters and use Auto Loader to speed up transformations.

Question 12

Refer to the exhibit. You are reviewing an ARM template for an Azure SQL Database deployment. What is the maximum size (in GB) of the database?

Accepted Answer

250 GB. The ARM template specifies the 'maxSizeBytes' property as 268,435,456,000 bytes. Converting this to gigabytes (divide by 1024^3) yields exactly 250 GB. This is the maximum size of the database as defined in the template.

Answer

256 GB

Answer

500 GB

Answer

268 GB

Question 13

Which TWO scenarios are appropriate for using Azure Blob Storage? (Choose two.)

Accepted Answer

Storing backup files and archival data.. Azure Blob Storage is designed for storing large amounts of unstructured data, such as binary files and text. Backup files and archival data are ideal use cases because Blob Storage supports hot, cool, and cold access tiers optimized for long-term retention and cost-effective storage. Additionally, storing images and videos for a website leverages Blob Storage's ability to serve static assets directly via HTTP/HTTPS, with built-in CDN integration for fast global delivery.

Answer

Storing key-value pairs with partition and row keys.

Answer

Running SQL queries on structured data.

Answer

Storing JavaScript functions for server-side logic.

Question 14

A data scientist needs to perform exploratory data analysis on a large dataset stored in Azure Data Lake Storage Gen2 using Python notebooks. The solution must minimize infrastructure management. Which Azure service should the data scientist use?

Accepted Answer

Azure Databricks with collaborative notebooks.. Azure Databricks provides a fully managed, collaborative notebook environment optimized for big data analytics and machine learning. It integrates natively with Azure Data Lake Storage Gen2, allowing the data scientist to perform exploratory data analysis (EDA) using Python notebooks without managing any underlying infrastructure. This minimizes operational overhead while providing autoscaling clusters and built-in Spark capabilities.

Answer

Azure Machine Learning compute instances.

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Power BI with dataflows.

Answer

Azure HDInsight with Jupyter notebooks.

Question 15

Which TWO of the following are benefits of using a data lake architecture? (Choose two.)

Accepted Answer

Ability to store raw data in its native format. A data lake architecture is designed to store raw data in its native format without requiring schema-on-write transformations. This allows organizations to ingest data as-is from various sources, preserving the original structure and enabling schema-on-read flexibility for analytics.

Answer

ACID transactions for all operations

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Optimized for high-frequency OLTP workloads

Answer

Built-in data governance without additional tools