Term
| Which data integration service allows you to orchestrate data flow without coding? Select only one answer. Azure Data Factory Azure Data Lake Azure Databricks Azure HDInsight |
|
Definition
Azure Data Factory; Why the other options are incorrect: Azure Data Lake is a scalable storage service for big data, not a data integration and orchestration service.
Azure Databricks: Azure Databricks is a data analytics platform for processing and analyzing large amounts of data, but it focuses on coding-based data transformations rather than visual orchestration.
Azure HDInsight: Azure HDInsight is a cloud-based service for processing large amounts of data using open-source frameworks, also primarily coding-based.
Key point: Azure Data Factory's visual interface allows for the creation of data pipelines through a drag-and-drop interface, making it a no-code solution for orchestration. |
|
|
Term
| What should you use to process large amounts of data by using Apache Hadoop? Select only one answer. Azure Data Factory Azure Databricks Azure HDInsight Azure Synapse Analytics |
|
Definition
| Azure HDInsight: Azure HDInsight is a managed, cloud-based service that makes it easy to provision and manage Apache Hadoop clusters in Azure. It is specifically designed to process large amounts of data using open-source big data frameworks like Apache Hadoop, Apache Spark, Apache Hive, and others. |
|
|
Term
| Which two services allow you to create a pipeline to process data in response to an event? Each correct answer presents a complete solution. Select all answers that apply. Azure Data Factory Azure Databricks Azure HDInsight Azure Synapse Analytics |
|
Definition
Azure Data Factory and Azure Synapse Analytics.
Explanation:
Azure Data Factory:
.
This is a cloud-based service specifically designed for creating and managing data pipelines, allowing you to define a series of tasks to process data in response to events. |
|
|
Term
| What is a characteristic of batch processing? Select only one answer. Small datasets are handled based on a time window. Complex analysis can be performed. Individual events are processed. Latency is measured in seconds or milliseconds. |
|
Definition
complex analysis can be performed.
Explanation:
Batch processing is designed to handle large volumes of data. This allows for complex analytical operations to be performed on the data. It's ideal for tasks that require significant computation or aggregation, as it can process the entire dataset at once. |
|
|
Term
Which two services can be used as a source for stream processing? Each correct answer presents a complete solution.
Select all answers that apply.
Azure Databricks
Azure Event Hubs
Azure IoT Hub
Azure SQL Database |
|
Definition
Azure Event Hubs and Azure IoT Hub.
Explanation:
Azure Event Hubs and Azure IoT Hub are designed to handle large volumes of streaming data, making them ideal sources for stream processing applications. They can efficiently ingest data from various sources like IoT devices, applications, and other systems, allowing for real-time analysis and processing. |
|
|
Term
In a stream processing architecture, what can you use to persist the processed results as files?
Select only one answer.
Azure Data Lake Storage Gen2
Azure Databricks
Azure Event Hubs
Azure Synapse Analytics |
|
Definition
Azure Event Hubs and Azure IoT Hub.
Explanation:
Azure Event Hubs and Azure IoT Hub are designed to handle large volumes of streaming data, making them ideal sources for stream processing applications. They can efficiently ingest data from various sources like IoT devices, applications, and other systems, allowing for real-time analysis and processing. |
|
|
Term
| In a stream processing architecture, what can you use to persist the processed results as files? Select only one answer. Azure Data Lake Storage Gen2 Azure Databricks Azure Event Hubs Azure Synapse Analytics |
|
Definition
Azure Data Lake Storage Gen2 to persist processed results as files.
Explanation: Azure Data Lake Storage Gen2 is designed for storing large amounts of data, making it suitable for storing the processed results from a stream processing pipeline. ; Why other options are incorrect:
Azure Databricks:
.
While Azure Databricks is a powerful data processing platform, it primarily focuses on processing data, not necessarily long-term storage. It can be used to write processed data to other storage solutions like Azure Data Lake Storage Gen2, but it's not the primary storage option for persisted results.
Azure Event Hubs:
.
Azure Event Hubs is an event broker designed for high-throughput ingestion of real-time data. It's not meant for long-term data persistence; its purpose is to temporarily hold and distribute events.
Azure Synapse Analytics:
.
Azure Synapse Analytics is a data warehouse and analytics service. While it can store data, its primary function is querying and analyzing data, not necessarily persisting processed results as files. |
|
|
Term
Which three services can be used to ingest data for stream processing? Each correct answer presents a complete solution.
Select all answers that apply.
Azure Data Lake Storage
Azure Event Hubs
Azure Functions
Azure IoT Hub
Azure SQL Database |
|
Definition
Azure Event Hubs, Azure IoT Hub, and Azure Data Lake Storage.
Explanation:
Azure Event Hubs:
.
Designed specifically for high-throughput event ingestion, handling large volumes of data from various sources in real-time.
Azure IoT Hub:
.
Facilitates communication between IoT devices and the cloud, allowing for efficient data ingestion from connected devices.
Azure Data Lake Storage:
.
While primarily a data storage service, it can also be used as a source for streaming data ingestion when combined with other services like Event Hubs.
Why other options are incorrect:
Azure Functions:
.
Azure Functions are serverless compute services used to execute code on-demand. They are not designed for data ingestion but rather for running code triggered by events.
Azure SQL Database:
.
This is a relational database service optimized for storing and querying structured data. It's not primarily intended for high-volume, real-time data ingestion. |
|
|
Term
Which service allows you to perform on-demand analysis of large volumes of data from text logs, websites and IoT devices by using a common querying language for all the data sources?
Select only one answer.
Azure Cosmos DB
Azure Data Explorer
Azure Data Lake Storage Gen2
Azure Stream Analytics |
|
Definition
Azure Data Explorer.
Explanation: Azure Data Explorer is designed for high-throughput analysis of large datasets, including unstructured data like text logs, website data, and IoT sensor readings, and uses its own query language (KQL) to efficiently query this data.
Why other options are incorrect:
Azure Cosmos DB:
.
While Azure Cosmos DB can handle large volumes of data, it's primarily a database designed for fast reads and writes, not necessarily for complex analytical queries across diverse data sources.
Azure Data Lake Storage Gen2:
.
This is a storage service, not designed for real-time analysis. It's more suitable for storing large amounts of data that can then be processed using other tools like Azure Data Explorer or Azure Data Lake Analytics.
Azure Stream Analytics:
.
This service is designed for real-time stream processing, not for querying large, stored datasets. It's used to react to events as they happen, not to analyze historical data. |
|
|
Term
Which service allows you to aggregate data over a specific time window before the data is written to a data lake?
Select only one answer.
Azure Cosmos DB
Azure Event Hubs
Azure SQL Database
Azure Stream Analytics |
|
Definition
Azure Stream Analytics.
Azure Stream Analytics is designed for real-time stream processing, enabling you to ingest, process, and analyze streaming data from various sources, including event hubs or IoT devices. It supports windowing functions (e.g., tumbling, hopping, sliding windows) to aggregate data over defined time intervals before outputting the processed data to various destinations, including Azure Data Lake Storage. |
|
|
Term
Which type of visual in Microsoft Power BI should you use to compare categorized values as the proportions of a total value?
Select only one answer.
bar chart
line chart
pie chart
scatter plot |
|
Definition
A pie chart is the best visual in Microsoft Power BI to compare categorized values as proportions of a total value.
Explanation: A pie chart visually divides a circle into slices, where each slice represents a portion of the whole, making it ideal for illustrating percentages or relative contributions within a dataset.
Why other options are incorrect:
Bar chart:
.
While bar charts can compare categorical data, they don't explicitly show proportions unless further calculations and formatting are added. They are better for comparing absolute values across categories.
Line chart:
.
Line charts are designed to visualize trends over time, not proportions. They are not suitable for comparing categorical values as a percentage of a total.
Scatter plot:
.
Scatter plots are used to visualize relationships between two numeric variables. They are not designed to show proportions within a single categorical variable. |
|
|
Term
What should you use to define an analytical model for Microsoft Power BI?
Select only one answer.
Azure Data Factory
Power BI Desktop
Power BI Phone App
the Power BI service |
|
Definition
Power BI Desktop.
Explanation: Power BI Desktop provides the environment for building and managing data models, including defining relationships, measures, and calculations within the semantic model.
Why other options are incorrect:
Azure Data Factory:
.
Azure Data Factory is a data integration service used for extracting, transforming, and loading (ETL) data. While it can prepare data for Power BI, it doesn't define the analytical model itself.
Power BI Phone App:
.
The Power BI Phone App is for viewing and interacting with existing Power BI reports and dashboards. It's not a tool for building or modifying models.
The Power BI service:
.
The Power BI service is primarily for publishing, sharing, and consuming reports and dashboards. It can host models and allow for some basic model management but is not the primary tool for model creation. |
|
|
Term
Which two visuals in Microsoft Power BI allow you to visually compare numeric values for discrete categories? Each correct answer presents a complete solution.
Select all answers that apply.
a bar chart
a card
a column chart
a matrix |
|
Definition
A bar chart and a column chart are the visuals in Microsoft Power BI that allow you to visually compare numeric values for discrete categories.
Explanation:
Bar chart/Column chart: Both of these visuals display data using rectangular bars, where the length of each bar represents the numeric value of a corresponding category, making them ideal for comparing different categories side-by-side.
Why other options are incorrect:
Card:
A card is typically used to display a single value or metric, not for comparing multiple categories.
Matrix:
While a matrix can show numeric data across multiple dimensions, it's designed for more complex comparisons involving relationships between different variables, not just direct comparisons of discrete categories. |
|
|
Term
| Which visual in Microsoft Power BI allows you to view trends, such as changes in sales over time? Select only one answer. a card a line chart a pie chart a scatter plot |
|
Definition
Explanation: Line charts connect data points over time, visually illustrating how a metric changes. They are specifically designed to show trends and patterns, making them ideal for tracking changes in sales or other time-series data.
Why other options are incorrect:
Card:
Cards display a single data value, like the total sales for a period. They are not suitable for visualizing trends over time as they only show a snapshot of a single point in time.
Pie chart:
Pie charts are used to show the relative proportions of different categories within a whole. They are not designed to track trends over time as they represent a static snapshot of data at a specific point.
Scatter plot:
Scatter plots visualize relationships between two numerical variables. While they can show trends if the data points have a clear pattern |
|
|
Term
You need to share a report that you created in Microsoft Power BI Desktop with other users.
What should you do first?
Select only one answer.
Create a workspace.
Create an app.
Open the report in a web browser.
Publish the report to the Power BI service. |
|
Definition
The first step to share a Power BI report with other users is to Publish the report to the Power BI service. This makes the report accessible in the cloud, allowing others to view and interact with it via a web browser or other supported methods.
Explanation:
Publishing to the Power BI service
.
is the core function for making your report available to others beyond your local machine.
Creating a workspace
.
is a necessary step to organize and manage your reports and datasets, but it doesn't directly share the report itself.
Creating an app
.
is a way to package and distribute related reports and dashboards to specific audiences, but it's not the first step in sharing a single report.
Opening the report in a web browser
.
is something that can be done after publishing to the Power BI service, but it's not the action that makes the report accessible to others.
Key points about publishing:
You need a Power BI account to publish reports to the service.
You can choose a specific workspace to publish your report to.
After publishing, the report is available for sharing with others who have access to that workspace. |
|
|
Term
| Which type of Azure Storage is used for VHDs and is optimized for random read and write operations? Select only one answer. append blobs Azure Files block blob page blob |
|
Definition
The correct answer is page blob.
Explanation:
Page blobs are designed for random read and write operations, making them ideal for storing virtual hard disks (VHDs). VHDs in Azure are essentially stored as page blobs, allowing virtual machines to access any part of the disk quickly and efficiently.
Why other options are incorrect:
Append blobs:
.
These are optimized for sequentially appending data to a blob, like log files. They are not suitable for random access operations needed by VHDs.
Azure Files:
.
Azure Files is a service built on top of Azure Blob Storage. While it can store files, it doesn't specifically optimize for random access like page blobs do.
Block blobs:
.
Block blobs are designed for storing large, unstructured files where data is accessed in large chunks. They are not optimized for random read and write operations typical of VHDs. |
|
|
Term
Which type of Azure Storage is used to store large amounts of data that must be processed by services such as Azure Databricks, Azure Synapse Analytics, and Azure HDInsight?
Select only one answer.
Azure Data Lake Storage Gen2
Azure Files
azure table storage
Azure Storage page blobs |
|
Definition
Azure Data Lake Storage Gen2.
Explanation:
Azure Data Lake Storage Gen2:
.
This service is specifically built for handling large volumes of diverse data, both structured and unstructured, that are common in big data analytics. It offers features like hierarchical namespaces, Hadoop-compatible access, and security mechanisms tailored for big data workloads.
Azure Blob Storage:
.
While Blob Storage is also used for storing large amounts of unstructured data, it's not optimized for the same level of performance and integration with big data analytics tools as Data Lake Storage Gen2.
Azure Files:
.
This service is for file shares and is not typically used for the massive data processing requirements of services like Databricks or Synapse.
Azure Storage page blobs:
.
These are used for storing virtual hard disks (VHDs) and are not suitable for the large-scale data processing scenarios described in the question.
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Which type of Azure Storage is used to store large amounts of data that must be processed by services such as Azure Databricks, Azure Synapse Analytics, and Azure HDInsight? Select only one answer. Azure Files Azure Data Lake Storage Gen2 Azure Storage page blobs Azure Tables storage
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Show all |
|
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Term
Which Azure Blob storage access tier should you use for data that will be used once per year and can have an access time that takes more than an hour?
Select only one answer.
Archive
Cool
Hot |
|
Definition
| archive access tier; is the most suitable for data accessed once a year with retrieval times exceeding an hour. This tier offers the lowest storage costs, ideal for infrequently accessed data. Although the retrieval process can take several hours, it balances cost-effectiveness for data with such low usage frequency. |
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Term
What are two characteristics of Azure Table storage? Each correct answer presents a complete solution.
Select all answers that apply.
Each RowKey value is unique within a table.
Each RowKey value is unique within a table partition.
Items in the same partition are stored in a RowKey order.
Tables use indexes to speed up queries. |
|
Definition
The correct answers are: "Each RowKey value is unique within a table partition" and "Items in the same partition are stored in a RowKey order".
Explanation:
Each RowKey value is unique within a table partition:
.
This is a key feature of Azure Table storage. Within a specific partition, each RowKey must be unique, but the same RowKey can exist across different partitions.
Items in the same partition are stored in a RowKey order:
.
Entities within a partition are sorted based on their RowKey value, which means you can efficiently query data within a partition by its RowKey order.
Why the other options are incorrect:
Each RowKey value is unique within a table:
.
While a RowKey is unique within a partition, it's not necessarily unique across the entire table. Different partitions can have entities with the same RowKey value.
Tables use indexes to speed up queries:
.
Azure Table storage does not use traditional indexes like relational databases. Instead, it relies on partitioning and RowKeys for efficient querying. Indexes are not supported for general queries in Azure Table Storage. |
|
|
Term
You need to replace an existing on-premises SMB shared folder with a cloud solution.
Which storage option should you choose?
Select only one answer.
Azure Blob storage
Azure Data Lake Storage
Azure Files
Azure Table storage |
|
Definition
s Azure Files.
Explanation:
Azure Files is designed specifically to provide SMB (Server Message Block) shares in the cloud, allowing you to seamlessly migrate existing on-premises SMB folders to the cloud while maintaining compatibility with existing applications and workflows. It can be mounted directly on client machines (Windows, Linux, macOS) just like a traditional on-premises share. Additionally, Azure File Sync allows for efficient data synchronization between on-premises and cloud shares, ensuring data consistency across locations.
Why other options are incorrect:
Azure Blob storage:
.
While Blob storage is a versatile option for storing large amounts of unstructured data, it's not designed for direct file sharing via SMB. Accessing data in Blob storage typically involves programmatic access through APIs, making it unsuitable for replacing an SMB share.
Azure Data Lake Storage:
.
Data Lake Storage is optimized for storing and analyzing large datasets, often used in big data scenarios. It's not intended for frequent file access or direct mounting as a network drive like an SMB share.
Azure Table storage:
.
Azure Table storage is a NoSQL database service designed for storing structured data in key-value pairs. It's not suitable for storing files or providing SMB access. |
|
|
Term
Which storage solution should you use to store unstructured documents, graph databases, and key/value pairs?
Select only one answer.
Azure Cosmos DB
Azure Files
Azure SQL
Azure Table storage |
|
Definition
Azure Cosmos DB is a globally distributed, multi-model database service that supports various data models, including:
Document databases: Ideal for storing unstructured documents like JSON.
Graph databases: Suitable for managing relationships and connections in data.
Key/value pairs: Efficient for simple data storage and retrieval based on keys.
The other options are less suitable for the combined requirements:
Azure Files:
.
Primarily used for file shares and not optimized for diverse data models like graph or key/value.
Azure SQL:
.
A relational database service designed for structured, tabular data, not unstructured documents or graph data.
Azure Table storage:
.
Optimized for storing large amounts of structured, non-relational data as key/value pairs, but lacks the multi-model capabilities for documents and graphs. |
|
|
Term
| Which Azure Cosmos DB API should you use for data in a graph structure? Select only one answer. Apache Cassandra Apache Gremlin MongoDB Table |
|
Definition
| The Azure Cosmos DB API to use for data in a graph structure is Apache Gremlin. |
|
|
Term
Which Azure Cosmos DB API should you use for data in the BSON format?
Select only one answer.
Apache Cassandra
Apache Gremlin
MongoDB
Table |
|
Definition
The Azure Cosmos DB API to use for data in the BSON format is MongoDB.
The Azure Cosmos DB API for MongoDB is designed to be compatible with the MongoDB wire protocol, allowing applications and tools that work with MongoDB to seamlessly connect and interact with Azure Cosmos DB, where data is stored in the BSON (Binary JSON) format. |
|
|
Term
Which Azure Cosmos DB API should you use for data in key/value tables?
Select only one answer.
Apache Cassandra
Apache Gremlin
MongoDB
Table |
|
Definition
Table API.
While the provided options do not include "Table," it is important to note that:
Apache Cassandra: is suitable for columnar data.
Apache Gremlin: is for graph data.
MongoDB: is for document data.
Therefore, none of the listed options are the direct answer for key/value tables. However, if the question implies a need to choose the best fit among the given options for a similar structure or a migration scenario, the context would need to be clearer. In a general sense, the native Azure Cosmos DB API for key/value tables is the Table API. |
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|
Term
Which Azure Cosmos DB API is queried by using a syntax based on SQL?
Select only one answer.
Apache Cassandra
Apache Gremlin
MongoDB
Table |
|
Definition
The Azure Cosmos DB API that is queried using a syntax based on SQL is the Core (SQL) API.
While other APIs like MongoDB API also support SQL-like queries, the Core (SQL) API is specifically designed to work with a SQL-like query language for querying JSON documents. |
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|
Term
Which type of data structure should you use to optimize create, read, update, and delete (CRUD) operations for data saved in a multi-column tabular format?
Select only one answer.
document database
graph database
key/value store
relational database |
|
Definition
| Relational databases are specifically designed to store and manage structured data in tables, where rows represent records and columns represent attributes. They utilize Structured Query Language (SQL) for efficient data manipulation, including all CRUD operations, and support relationships between tables through primary and foreign keys. This structure makes them highly effective for maintaining data integrity and performing complex queries on tabular data. |
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|
Term
Which type of data structure allows you to store data in a two-column format without requiring a complex database management system?
Select only one answer.
document database
graph database
key/value store
relational database |
|
Definition
| A key/value store organizes data as a collection of key-value pairs, where each key is unique and associated with a specific value. This inherently creates a two-column structure (key and value) and is a simpler data model compared to those requiring a full database management system with features like schemas, complex querying, and transactional integrity. |
|
|
Term
| You have data stored in two tables in a database. You create a relationship between the tables. Which type of data do you have? Select only one answer. semi-structured structured unstructured |
|
Definition
| Structured data is characterized by its organization in a predefined format, such as tables in a relational database. The presence of two tables and a defined relationship between them, as described in the scenario, is a hallmark of structured data. This organization allows for efficient storage, querying, and analysis through established methods like SQL. |
|
|
Term
You have data that describes products and is stored in JSON documents. The product structure changes over time as new attributes are added.
Which type of data do you have?
Select only one answer.
semi-structured
structured
unstructured |
|
Definition
semi-structure
Key points about the options:
Semi-structured:
Offers a combination of structured elements (like key-value pairs) with flexible structures, allowing for additions or changes to the data without fundamentally altering the entire schema.
Structured:
Data with a predefined, fixed structure like a relational database where each record follows a set of columns and data types.
Unstructured:
Data with no clear structure or defined organization, like raw text or images. |
|
|
Term
You design an application that needs to store data based on the following requirements:
Store historical data from multiple data sources.
Load data on a scheduled basis.
Use a denormalized star or snowflake schema.
Which type of database should you use?
Select only one answer.
Azure Table storage
Graph Database
OLAP Database
OLTP Database |
|
Definition
OLAP Database.
Explanation:
OLAP (Online Analytical Processing) databases
.
are specifically designed for analytical workloads, focusing on querying and analyzing large volumes of historical data for business intelligence and reporting purposes.
Star and Snowflake schemas
.
are common dimensional modeling techniques used in data warehousing, which is the domain of OLAP databases. These schemas optimize data for analytical queries by organizing it into fact and dimension tables.
Storing historical data from multiple sources
.
and loading data on a scheduled basis are characteristic features of data warehousing, where data is extracted, transformed, and loaded (ETL) into a central repository for analysis.
Why other options are not suitable:
Azure Table storage:
.
This is a NoSQL key-value store, not designed for complex analytical queries and dimensional modeling.
Graph Database:
.
Graph databases are used for representing and querying relationships between entities, not for the type of analytical processing described.
OLTP (Online Transaction Processing) Database:
.
OLTP databases are optimized for high-volume, real-time transactional operations, not for historical data analysis and complex analytical queries. They typically use a normalized schema to ensure data integrity during transactions. |
|
|
Term
Which type of database can be used for semi-structured data that will be processed by an Apache Spark pool in Azure Synapse Analytics?
Select only one answer.
column-family
graph
relational |
|
Definition
The type of database that can be used for semi-structured data processed by an Apache Spark pool in Azure Synapse Analytics is a column-family database.
While other NoSQL databases like document or key-value stores can also handle semi-structured data, the provided options specifically point to column-family as a suitable choice within the context of Spark processing in Azure Synapse. Column-family databases are well-suited for handling large volumes of semi-structured data and can be efficiently processed by distributed computing frameworks like Apache Spark. |
|
|
Term
Which two types of file store data in columnar format? Each correct answer presents a complete solution.
Select all answers that apply.
Avro
CSV
Parquet
ORC |
|
Definition
The two types of files that store data in columnar format are: Parquet and ORC.
Avro and CSV are row-based file formats. |
|
|
Term
Which type of data workload is optimized for updates and relies on relationships between entities to correlate data?
Select only one answer.
analytical
graph
time series
transactional |
|
Definition
Explanation:
Transactional workloads are designed to handle frequent updates and maintain data integrity. They are optimized for CRUD (Create, Read, Update, Delete) operations and rely on relationships between entities to correlate data. Databases used for transactional workloads often need to ensure data consistency through ACID properties (Atomicity, Consistency, Isolation, Durability). Examples of transactional systems include online shopping carts, banking systems, and point-of-sale systems.
Why other options are incorrect:
Analytical:
Analytical workloads are primarily focused on reading large amounts of data and performing complex aggregations to gain insights. They are often optimized for read operations rather than updates. Data in analytical systems might be highly denormalized to improve query performance.
Graph:
Graph workloads are designed to store and analyze relationships between entities, represented as nodes and edges. While they can support some updates, their primary focus is on traversing and analyzing the network of connections.
Time series:
Time series workloads are optimized for storing and analyzing data that is ordered chronologically. They are typically used to track trends and identify patterns over time. Updates in time series databases usually involve appending new data points to the series. |
|
|
Term
Which two types of applications are used in transactional systems? Each correct answer presents a complete solution.
Select all answers that apply.
line-of-business (LOB) applications
live applications
reports presenting business metrics
reports presenting OLAP measures |
|
Definition
The correct answers are line-of-business (LOB) applications and live applications.
Explanation:
Line-of-business (LOB) applications:
.
These are custom applications designed to support specific business functions within an organization. They directly interact with transactional databases to process transactions and update data in real-time, making them ideal for operational tasks. Examples include order management systems, point-of-sale systems, and customer relationship management (CRM) systems.
Live applications:
.
This term refers to applications that continuously update their data based on real-time information. In the context of transactional systems, live applications display live data from the database, allowing users to view the latest information as it happens. This is crucial for applications like stock tickers, online banking dashboards, or order tracking systems.
Why other options are incorrect:
Reports presenting business metrics:
.
While reports can be generated from transactional data, they are not considered applications themselves. Reports are static snapshots of data at a specific point in time, whereas transactional systems are designed for ongoing data manipulation and processing.
Reports presenting OLAP measures:
.
OLAP (Online Analytical Processing) is used for analyzing large datasets and identifying trends. While transactional systems may feed data into OLAP systems for analysis, reports presenting OLAP measures are not transactional applications themselves. They are analytical tools used to interpret and visualize the data, not to execute transactions. |
|
|
Term
| Which job role is responsible for designing database solutions, creating databases, and developing stored procedures? Select only one answer. database administrator database analyst database engineer database user |
|
Definition
The correct answer is database engineer.
Explanation:
A database engineer is responsible for designing, creating, and maintaining databases, including developing stored procedures. They focus on the technical aspects of database management, ensuring efficiency, security, and scalability.
Why other options are incorrect:
Database administrator:
.
While they manage databases, their primary role is to maintain and secure existing databases, not design new ones or develop complex stored procedures.
Database analyst:
.
Database analysts primarily work with existing data to analyze it and extract insights. They may not be involved in the design or creation of databases or stored procedures.
Database user:
.
Database users simply use the data stored in a database. They don't have the technical expertise to design or develop databases. |
|
|
Term
Which job role is responsible for creating reports from a database and using OLAP cubes?
Select only one answer.
database administrator
database analyst
database engineer
database user |
|
Definition
|
|
Term
Which job role is responsible for managing the security of the data in a database, implementing backup and recovery plans, and monitoring the performance of database solutions?
Select only one answer.
database administrator
data analyst
data engineer
database user |
|
Definition
|
|
Term
Which job role is responsible for building data models and finding hidden data patterns?
Select only one answer.
database administrator
data analyst
data engineer
Database users |
|
Definition
|
|
Term
| In a relational database, on which type of objects should you configure a datatype? Select only one answer. columns fields relationships rows tables |
|
Definition
|
|
Term
What are two advantages of using normalization over not using normalization in a relational database? Each correct answer presents a complete solution.
Select all answers that apply.
provides storage for non-structured data
optimizes for complex reads
optimizes for updates
uses less storage space |
|
Definition
optimizes for updates
uses less storage space |
|
|
Term
Which SQL operation is used to combine the content of two tables based on a shared column?
Select only one answer.
`HAVING`
INTERSECT
JOIN
UNION |
|
Definition
|
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Term
Which SQL clause can be used to copy all the rows from one table to a new table?
Select only one answer.
INSERT – VALUES
SELECT – HAVING
SELECT - INTO
SELECT - OVER |
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Definition
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Term
Select the answer that correctly completes the sentence.
[Answer choice] is a process to reduce duplicate data in a database and ensure data integrity.
Select only one answer.
Indexing
Normalization
Projecting
Refactoring |
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Definition
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Term
What should you create to improve the performance of a query accessing a table that contains millions of rows?
Select only one answer.
a function
a stored procedure
a view
an index |
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Definition
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Term
You need to recommend a solution that meets the following requirements:
Encapsulates a business logic that can rename the products in a database
Adds entries to tables
What should you include in the recommendation?
Select only one answer.
a stored procedure
a table-valued function
a view
an inline function |
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Definition
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Term
In a relational database, what can you use to create a virtual table from the results of a SELECT statement?
Select only one answer.
a relationship
a stored procedure
a view
an index |
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Definition
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Term
Which service is managed and serverless, avoids the use of Windows Server licenses, and allows for each workload to have its own instance of the service being used?
Select only one answer.
Azure SQL Database
Azure SQL Managed Instance
SQL Server on Azure Virtual Machines running Linux
SQL Server on Azure Virtual Machines running Windows Server |
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Definition
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Term
| Which data service allows you to control the amount of RAM, change the I/O subsystem configuration, and add or remove CPUs? Select only one answer. Azure SQL Database Azure SQL Managed Instance SQL Server on Azure Virtual Machines |
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Definition
The data service that allows for the most granular control over resources like RAM, I/O subsystem configuration, and the ability to add or remove CPUs is SQL Server on Azure Virtual Machines.
This is because SQL Server on Azure Virtual Machines provides full control over the underlying operating system and virtual machine, allowing for direct management of hardware-level configurations, similar to an on-premises SQL Server installation. Azure SQL Database and Azure SQL Managed Instance are Platform-as-a-Service (PaaS) offerings, which provide less direct control over the underlying infrastructure in exchange for managed services. |
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Term
Which open-source database is a hybrid relational-object database?
Select only one answer.
MariaDB
MySQL
Oracle Database
PostgreSQL |
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Definition
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Term
| The feature of Microsoft Purview that can best support tracking data movement and changes across systems for regulatory compliance is _________ |
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Definition
Data lineage visualization.
Explanation: Data lineage visualization allows organizations to visually track the flow of data from its source to its destination, including any transformations applied along the way, which is crucial for identifying potential issues and ensuring compliance with regulations. |
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