Microsoft DP-203 Practice Test Questions, Exam Dumps

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DP-203 Microsoft Practice Test Questions and Exam Dumps

Question No 1:

You need to alter the table to meet the following requirements:

Ensure that users can identify the current manager of employees.
Support creating an employee reporting hierarchy for your entire company.
Provide fast lookup of the managers' attributes such as name and job title.

Which column should you add to the table?

A. [ManagerEmployeeID] [smallint] NULL
B. [ManagerEmployeeKey] [smallint] NULL
C. [ManagerEmployeeKey] [int] NULL
D. NULL

Correct Answer: C

Explanation:

To meet the requirements provided, you need to establish a way to link employees to their managers, maintain a reporting hierarchy, and enable fast lookups for the manager's attributes like name and job title. Let’s evaluate each option based on these goals.

A. [ManagerEmployeeID] [smallint] NULL

This column name suggests that it might store the employee ID of the manager. While this is close to what is needed, the use of smallint (a smaller range for integer values) is likely too limited for larger organizations. Employee IDs in many organizations may exceed the smallint range, which typically covers values from -32,768 to 32,767.
Therefore, using a smallint could be problematic for organizations with a larger number of employees, limiting scalability.

B. [ManagerEmployeeKey] [smallint] NULL

This is similar to option A, but the term "EmployeeKey" seems to suggest a unique key that could link to the manager’s record in another table. However, the use of smallint here has the same limitation as in option A.
While the key may help in linking, the size of the column could still be too restrictive for larger organizations.

C. [ManagerEmployeeKey] [int] NULL

This column provides a more scalable option compared to the smallint because int can handle a much larger range of values (from -2,147,483,648 to 2,147,483,647), making it suitable for organizations of any size.
The term EmployeeKey is also fitting since it can uniquely identify the manager in the employee table, helping to create a reporting hierarchy. It supports linking employees to their managers, which is crucial for building an employee hierarchy and performing fast lookups of the manager’s attributes.

D. NULL

Storing the manager's name directly in the employee table would violate normalization principles. This could lead to redundancy issues, especially if multiple employees report to the same manager. Any changes to the manager’s name would require updates to multiple records, which is inefficient.
This column doesn't support the creation of a hierarchical structure, and querying the manager's details (such as job title) would require repetitive data entry.

The best option to meet all the requirements—linking employees to their managers, supporting an organizational hierarchy, and ensuring fast lookup of manager attributes—is C. [ManagerEmployeeKey] [int] NULL. This solution ensures scalability, normalization, and proper hierarchical structure, while also providing a way to easily link to additional manager details in other tables (such as job title).

Question No 2:

You have an Azure Synapse workspace named MyWorkspace that contains an Apache Spark database named mytestdb. You run the following command in an Azure Synapse Analytics Spark pool in MyWorkspace:

You then use Spark to insert a row into mytestdb.myParquetTable. The row contains the following data. One minute later, you execute the following query from a serverless SQL pool in MyWorkspace:

What will be returned by the query?

A. 24
B. an error
C. a null value

Correct Answer: B

Explanation:

In this scenario, there are several important points to consider regarding the use of Apache Spark and serverless SQL pools in Azure Synapse. Here's a breakdown of what happens:

Data in Apache Spark and Serverless SQL Pools:

Apache Spark in Azure Synapse is used for big data analytics and can interact with various data formats, such as Parquet.

A serverless SQL pool, on the other hand, provides an SQL interface for querying data stored in Azure Data Lake or Azure Blob Storage, including formats like Parquet.

Spark Table Creation:

The command CREATE TABLE mytestdb.myParquetTable USING Parquet creates a Parquet-formatted table in Apache Spark.

At this point, the table exists and can be populated with data, and the query operations on this table are executed within the Spark pool.

Data Consistency Across Pools:

Although Apache Spark can write to Parquet files, the serverless SQL pool doesn't directly access the data written by Spark unless it's in a common location in Azure Data Lake or Blob Storage. Even if the Parquet file exists, the serverless SQL pool may not have access to the data written by the Spark pool unless the necessary external table mappings are configured.

Query Execution from Serverless SQL Pool:

When the query SELECT EmployeeID FROM mytestdb.dbo.myParquetTable WHERE EmployeeName = 'Alice'; is run from the serverless SQL pool, it expects an SQL-based external table pointing to the data stored in Azure Data Lake or Blob Storage.

The dbo schema used in the query (mytestdb.dbo.myParquetTable) indicates a possible SQL Server-like structure, which doesn't directly correspond to the Apache Spark-created table (mytestdb.myParquetTable).

As a result, the query execution fails due to the absence of an external table or external data mapping between the serverless SQL pool and the data in Spark's Parquet table.
Since there is no external table or data link established between the Spark-managed Parquet table and the serverless SQL pool, the query will result in an error.

Therefore, the correct answer is B, as an error occurs when the query is executed from the serverless SQL pool due to the mismatch in data access.

Question No 3:

You create an external table named ExtTable that has LOCATION='/topfolder/'. When you query ExtTable by using an Azure Synapse Analytics serverless SQL pool, which files are returned?

A. File2.csv and File3.csv only
B. File1.csv and File4.csv only
C. File1.csv, File2.csv, File3.csv, and File4.csv
D. File1.csv only

Correct Answer: C

Explanation:

In Azure Synapse Analytics, when you create an external table that points to a directory location, such as /topfolder/, the query will return all the files in that directory that match the table’s structure. Azure Synapse will automatically detect and query all files within the specified location unless additional filters are applied or the file formats don't match the expected structure defined by the external table.

Here’s a breakdown of how the query works:

Location of the External Table: The location /topfolder/ points to a directory in the underlying storage. When an external table is created with this location, Synapse looks in that folder for all files that match the format and schema defined in the external table.
Files Matching the Format: Azure Synapse will return all files in the specified folder that adhere to the format expected by the external table. For example, if the external table is set up to handle CSV files, all CSV files within /topfolder/ will be included in the query result.

Since the external table is defined with a location pointing to /topfolder/, and the query is being run on an Azure Synapse Analytics serverless SQL pool, it will retrieve all files in that folder that match the defined format and schema of the table.

Thus, C (File1.csv, File2.csv, File3.csv, and File4.csv) would be the correct answer, as it suggests that all files in the location are returned in the query result. No filtering or restrictions were mentioned that would limit the returned files. Therefore, the external table will return all four files within the /topfolder/ directory.

To summarize, C is the most appropriate answer because it correctly accounts for all files within the directory that are compatible with the external table definition.

Question No 4:

You are designing the folder structure for an Azure Data Lake Storage Gen2 container. Users will query data by using a variety of services including Azure Databricks and Azure Synapse Analytics serverless SQL pools. The data will be secured by subject area. Most queries will include data from the current year or current month.

Which folder structure should you recommend to support fast queries and simplified folder security?

A. /{SubjectArea}/{DataSource}/{DD}/{MM}/{YYYY}/{FileData}{YYYY}{MM}{DD}.csv
B. /{DD}/{MM}/{YYYY}/{SubjectArea}/{DataSource}/{FileData}{YYYY}{MM}{DD}.csv
C. /{YYYY}/{MM}/{DD}/{SubjectArea}/{DataSource}/{FileData}{YYYY}{MM}{DD}.csv
D. /{SubjectArea}/{DataSource}/{YYYY}/{MM}/{DD}/{FileData}{YYYY}{MM}{DD}.csv

Correct Answer: D

Explanation:

Designing an effective folder structure for Azure Data Lake Storage Gen2 is crucial for optimizing query performance, simplifying data management, and implementing effective security practices. The folder structure needs to take into account factors like how the data will be queried, how often the data will be accessed, and how it will be secured.

Key Considerations:

Query Performance: For fast queries, data should be organized in a way that allows quick filtering by frequently accessed fields such as the year or month. Organizing data by YYYY (year) and MM (month) is optimal for queries that filter on time-based data, especially when most queries focus on the current year or month.
Security: You want to secure data by subject area. It’s best to structure the data such that subject areas are clearly separated at the highest levels of the folder hierarchy. This makes it easier to apply access control policies on a per-subject basis.
Data Access Patterns: Since the majority of queries are expected to focus on the current year or current month, it makes sense to organize the data by year and month to minimize the number of files that need to be scanned for queries that filter on these time-based dimensions.

Option Analysis:

A. /{SubjectArea}/{DataSource}/{DD}/{MM}/{YYYY}/{FileData}{YYYY}{MM}_{DD}.csv: This structure places the day (DD) as a higher level folder, which is unnecessary for most queries, as most queries are not likely to filter down to the day level. This can lead to inefficient querying, especially when users typically query by year or month. The inclusion of DD adds granularity that may not be needed for performance optimization.
B. /{DD}/{MM}/{YYYY}/{SubjectArea}/{DataSource}/{FileData}{YYYY}{MM}_{DD}.csv: This structure starts with DD, which is even more granular than option A. Queries that filter by year or month would need to scan across multiple day-level folders, which is inefficient for most data access scenarios. This structure does not align well with the common access patterns for time-based filtering.
C. /{YYYY}/{MM}/{DD}/{SubjectArea}/{DataSource}/{FileData}{YYYY}{MM}_{DD}.csv: While organizing data by year (YYYY) and month (MM) at the top level is a good choice for performance, this structure includes the day (DD) folder as part of the path. Since many queries will likely focus on year or month, having DD as part of the folder hierarchy introduces unnecessary complexity and could result in slower queries that don’t require daily-level granularity.
D. /{SubjectArea}/{DataSource}/{YYYY}/{MM}/{DD}/{FileData}{YYYY}{MM}_{DD}.csv: This is the best structure for several reasons:

SubjectArea is at the top level, which supports easy access control and security by subject area.

Organizing by year (YYYY) and month (MM) enables efficient queries that focus on these time periods.

Day (DD) is included but placed at a lower level, which allows users to access more granular data when needed, without impacting the performance of most queries that only require filtering by year or month.

Option D is the most effective choice because it strikes a balance between efficient querying, security, and simplicity. The structure allows for fast access to the most queried data (year and month) while maintaining the ability to dive deeper into daily data when needed. This is crucial for supporting fast queries and simplified folder security in Azure Data Lake Storage Gen2.

Question No 5:

Which of the following is the most appropriate method for handling real-time streaming data from IoT devices into Azure Data Lake Storage for further processing?

A) Azure Data Factory
B) Azure Stream Analytics
C) Azure Databricks
D) Azure Synapse Analytics

Correct Answer: B) Azure Stream Analytics

Explanation:

The DP-203: Data Engineering on Microsoft Azure certification exam requires proficiency in selecting and implementing the appropriate tools for handling data at various stages of processing, including real-time data ingestion, transformation, and storage. Among the options listed in this question, Azure Stream Analytics is the most appropriate method for handling real-time streaming data, such as that coming from Internet of Things (IoT) devices.

Azure Stream Analytics is a fully managed, real-time analytics service that is specifically designed for processing real-time data streams. It is highly effective when it comes to ingesting data from sources like IoT devices, sensors, and social media streams, and it can then route this data to destinations such as Azure Data Lake Storage. Stream Analytics allows for the quick ingestion of high-throughput data streams, real-time querying, and event-based processing. Its ability to process streams with low-latency ensures that organizations can make timely, data-driven decisions. For IoT scenarios, this service is optimal because it integrates easily with IoT Hub, making it ideal for scenarios where devices need to send data for real-time analytics.

Let’s break down the other options to understand why they are less suitable for this particular use case:

Azure Data Factory (Option A) is a powerful ETL (Extract, Transform, Load) service that is excellent for orchestrating batch data workflows. However, it is not optimized for real-time data processing. While it can be used for moving data from one storage service to another, it lacks the real-time data streaming capabilities that Azure Stream Analytics provides.
Azure Databricks (Option C) is a fast, easy, and collaborative Apache Spark-based analytics platform. It is great for large-scale data processing, machine learning, and advanced analytics on batch data. While Databricks is capable of processing streaming data, it is more complex to set up for this purpose compared to Stream Analytics, which is purpose-built for real-time stream processing.
Azure Synapse Analytics (Option D) is an integrated analytics platform that combines big data and data warehousing. It’s fantastic for batch analytics and complex queries on large data sets but not ideal for real-time data streaming from IoT devices.

In conclusion, Azure Stream Analytics is the correct choice for handling real-time streaming data from IoT devices into Azure Data Lake Storage due to its built-in real-time analytics capabilities, ease of integration with IoT Hub, and optimized stream processing features.

Question No 6:

Which of the following Azure services is most suitable for building a scalable and efficient solution for processing large amounts of unstructured data, such as log files or media files, from multiple sources?

A) Azure Blob Storage
B) Azure SQL Database
C) Azure Cosmos DB
D) Azure Table Storage

Correct Answer: A) Azure Blob Storage

Explanation:

The DP-203: Data Engineering on Microsoft Azure exam tests your ability to design and implement solutions for storing and processing data in Azure. One of the most important aspects of data engineering is choosing the correct storage solution for various types of data, including unstructured data, which can come from sources such as logs, media files, and documents. Among the options listed in this question, Azure Blob Storage is the most suitable solution for handling large amounts of unstructured data.

Azure Blob Storage is a highly scalable, cost-effective, and durable solution designed for storing large amounts of unstructured data. Unstructured data is data that doesn’t have a predefined schema, such as log files, media files (videos, images), and backups. Azure Blob Storage allows users to store these types of data as objects (blobs), and it offers different types of blobs (block blobs, append blobs, and page blobs) to accommodate different use cases. For instance, block blobs are ideal for large media files, while append blobs are optimized for scenarios like logging.

The service is highly scalable, meaning it can handle massive volumes of data from multiple sources. Additionally, it integrates well with other Azure services for processing and analyzing data, such as Azure Data Lake Analytics, Azure Databricks, and Azure Stream Analytics. It is often used in data engineering pipelines where large-scale data ingestion and processing are required.

Let’s now evaluate the other options:

Azure SQL Database (Option B) is a relational database-as-a-service (DBaaS) solution. While it is excellent for structured data with relational models and transactional consistency, it is not designed to handle large volumes of unstructured data like logs or media files. It is better suited for applications requiring structured, transactional data.
Azure Cosmos DB (Option C) is a globally distributed, multi-model database designed for low-latency, high-throughput applications. While it can store unstructured data in the form of documents or key-value pairs, it is more appropriate for use cases that require fast reads and writes of highly structured data or semi-structured data across globally distributed applications. It is not optimized for storing large files like log data or media files.
Azure Table Storage (Option D) is a NoSQL key-value store that is useful for storing structured, non-relational data with a schema-less design. It works well for scenarios like storing metadata or application data but is not designed to efficiently handle large binary files or unstructured data, which is the strength of Azure Blob Storage.

In summary, Azure Blob Storage is the correct choice for storing and processing large amounts of unstructured data due to its scalability, cost-effectiveness, and flexibility in handling various types of data like log files and media. It is the go-to solution for unstructured data in Azure-based data engineering solutions.

Question No 7:

Which of the following Azure services should be used for creating a real-time dashboard that visualizes streaming data from multiple IoT devices, providing insights such as device health, metrics, and alerts?

A) Azure Synapse Analytics
B) Azure Monitor
C) Azure Stream Analytics
D) Power BI

Correct Answer: D) Power BI

Explanation:

In the DP-203: Data Engineering on Microsoft Azure certification exam, one of the critical areas of focus is how to handle, process, and visualize data, particularly when it involves real-time analytics or monitoring from various data sources such as IoT devices. To answer this question, it’s important to recognize the best Azure service for building a real-time dashboard that displays insights like device health and metrics. The correct answer is Power BI.

Power BI is a business analytics tool that allows users to visualize and share insights from their data. It is not only useful for static reports, but also for creating real-time dashboards. When combined with Azure services like Azure Stream Analytics, Azure IoT Hub, or Azure Event Hubs, Power BI can stream real-time data into rich, interactive dashboards. For an IoT use case where you want to monitor metrics, device health, and alerts, Power BI is an ideal solution due to its ability to integrate with various data sources, including IoT devices and cloud-based services.

Power BI provides tools to customize the visualizations based on real-time data, display metrics on graphs and charts, and provide alerts or insights into the data. Moreover, Power BI’s integration with other Azure services makes it a powerful choice for real-time data dashboards, making it easy to track trends and anomalies in data streams from IoT devices.

Let’s evaluate the other options:

Azure Synapse Analytics (Option A) is a powerful analytics service that integrates big data and data warehousing. While it excels at processing large datasets and running complex queries, it is not specifically designed for creating real-time dashboards for monitoring IoT device health or live metrics. It is more focused on big data analytics and batch processing rather than real-time data visualization.
Azure Monitor (Option B) is primarily designed for monitoring the health and performance of your Azure resources. It provides logs, metrics, and diagnostic data, and it can alert you when issues occur with your Azure resources. While it offers real-time monitoring, it is not the best tool for creating interactive, data-driven dashboards like Power BI. Instead, it’s more focused on resource-level monitoring rather than presenting data visually for end users.
Azure Stream Analytics (Option C) is an excellent choice for processing and analyzing real-time streaming data, such as from IoT devices. However, while it provides powerful data transformation and streaming capabilities, it does not directly offer visualization features. For real-time dashboards, Stream Analytics works best in conjunction with Power BI, where the processed data can be sent for visualization and presentation.

In conclusion, Power BI is the best option for creating real-time dashboards that display insights from multiple IoT devices. Its capability to seamlessly integrate with other Azure services and its rich visualization features make it the ideal choice for building interactive, real-time reporting solutions.