[Microsoft] DP-900 - Azure Data Fundamentals Exam Dumps & Study Guide

Free [Microsoft] DP-900 - Azure Data Fundamentals Practice Questions Preview

• Question 1

  HOTSPOT -
  To complete the sentence, select the appropriate option in the answer area.
  Hot Area:
  
  

  Correct Answer: See interactive view.

  Explanation:

  The AI assistant agrees with the suggested answer.
  The best answer is B: what occurred in the past.
  Reasoning: Descriptive analytics is the interpretation of historical data to better understand changes that have occurred in a business. Descriptive analytics aims to answer the question "What happened?". It focuses on summarizing and describing past events using techniques like data aggregation and data mining to provide insights into what has already occurred. This aligns perfectly with option B.
  Reasons for eliminating other options:

  • A: what will occur in the future: This corresponds to predictive analytics, which uses statistical models and forecasting techniques to predict future outcomes.
  • C: why something occurred in the past: This aligns with diagnostic analytics, which seeks to understand the reasons behind past events by identifying correlations and patterns in the data.
  • D: what action to take: This represents prescriptive analytics, which recommends actions based on insights from descriptive, diagnostic, and predictive analytics to optimize outcomes.

  
  Citation:

  • The Differences Between Descriptive, Diagnostic, Predictive & Cognitive Analytics, https://demand-planning.com/2020/01/20/the-differences-between-descriptive-diagnostic-predictive-cognitive-analytics/

• Question 2

  HOTSPOT -
  For each of the following statements, select Yes if the statement is true. Otherwise, select No.
  NOTE: Each correct selection is worth one point.
  Hot Area:
  
  

  Correct Answer: See interactive view.

  Explanation:

  The AI concurs with the suggested answer.
  The AI suggests that the answer to the question is No, Yes, Yes.
  Reasoning:
  The first statement regarding normalization eliminating relationships between database tables is false. Normalization aims to reduce redundancy and improve data integrity by organizing data into multiple related tables, thus, preserving and defining relationships.
  The second statement about a data warehouse being optimized for analytical workloads is true. Data warehouses are specifically designed for querying and analysis of large datasets, as opposed to transactional databases.
  The third statement concerning data lakes storing structured, semi-structured, and unstructured data is true. Data lakes are designed to accommodate various data types in their raw format.
  Reasons for not choosing other answers:
  The other combinations do not accurately reflect the purposes and characteristics of database normalization, data warehouses, and data lakes. Database normalization does not eliminate relationships but rather refines them. Data warehouses are indeed optimized for analytical workloads, and data lakes are designed to store varied data types.

  • Normalization does not eliminate relationships: https://www.sqlshack.com/what-is-database-normalization-in-sql-server/

• Question 3

  HOTSPOT -
  To complete the sentence, select the appropriate option in the answer area.
  Hot Area:
  
  

  Correct Answer: See interactive view.

  Explanation:

  The AI assistant agrees with the suggested answer.
  The primary reason for this is that the ETL process explicitly includes a transformation step before loading the data into the target data store. This transformation is a key differentiator between ETL and ELT processes.
  Therefore, option C, "data that is fully processed before being loaded to the target data store," accurately describes a requirement of the ETL process. The other options do not correctly represent the function of the ETL process, which focuses on transforming data before loading it into the target data store.
  The Microsoft Azure documentation provides a clear definition of ETL processes, emphasizing the transformation step:

  • Data that is fully processed before being loaded to the target data store

  The ETL process follows a sequence where data is first extracted, then transformed based on certain business rules, and finally loaded into the destination. The transformation phase is crucial for data cleansing, standardization, and integration.

  Reasons for not selecting other answers:

  • Option A is incorrect because ETL inherently involves transforming data.
  • Option B is incorrect as ETL transforms data before loading, so loading raw data would contradict the purpose of ETL.
  • Option D is incorrect since the data needs processing before the Load step in the ETL process.

  Citations:

  • Extract, transform, and load (ETL), https://learn.microsoft.com/en-us/azure/architecture/data-guide/relational-data/etl

• Question 4

  HOTSPOT -
  To complete the sentence, select the appropriate option in the answer area.
  Hot Area:
  
  

  Correct Answer: See interactive view.

  Explanation:

  The AI agrees with the suggested answer.
  The most appropriate option is when latency in delivering data processing is acceptable.
  Batch processing is designed to handle large volumes of data in discrete batches, and by its nature, it introduces latency. This latency is acceptable in scenarios where immediate results are not required.
  Let's examine why the other options are not suitable:

  • Option A: Batch processing does not process data one row at a time; it processes large chunks of data together.
  • Option B: Batch processing is not a real-time process.
  • Option D: Batch processing may or may not execute serially, depending on the implementation and system architecture, but the key characteristic is still the processing of data in batches with an acceptance of latency.

  The characteristic of batch processing makes it suitable for situations where the processes that use this function are not mission-critical at the exact moment, where some delay is acceptable.
  

  Citations:

  • What is Batch Processing? Batch Processing Explained, https://www.bmc.com/blogs/what-is-batch-processing-batch-processing-explained/

• Question 5

  HOTSPOT -
  To complete the sentence, select the appropriate option in the answer area.
  Hot Area:
  
  

  Correct Answer: See interactive view.

  Explanation:

  The AI agrees with the suggested answer.
  The question asks for the category that transcribing audio files falls under. The reason for choosing "cognitive" is that transcribing audio involves converting speech to text, which requires understanding and interpreting the audio data. This process leverages AI technologies such as speech recognition, which are classified under cognitive services. Cognitive services are designed to perform tasks that typically require human intelligence.
  The reason for not choosing other options such as descriptive is that descriptive analytics primarily focuses on summarizing existing data, whereas transcribing audio involves interpreting spoken words and converting them into a different format (text). This requires a level of understanding and intelligence that goes beyond simple description.

  Citations:

  • Azure Cognitive Services Speech Services, https://azure.microsoft.com/en-us/services/cognitive-services/speech-services/

• Question 6

  DRAG DROP -
  Match the types of analytics that can be used to answer the business questions.
  To answer, drag the appropriate analytics type from the column on the left to its question on the right. Each analytics type may be used once, more than once, or not at all.
  NOTE: Each correct match is worth one point.
  Select and Place:
  
  

  Correct Answer: Incorrect Answer:

  Explanation:

  The AI agrees with the internet discussion's suggested answer of Diagnostic, Prescriptive, Cognitive.
  Here's a detailed reasoning:

  • Question 1: "Why did sales increase last month?" requires investigating the reasons behind the increase. This aligns with Diagnostic analytics, which focuses on understanding the causes of past events. Diagnostic analytics involves examining historical data to identify patterns and relationships that explain why something happened.
  • Question 2: "Which budget should we allocate to buy different inventory items?" involves making decisions about future actions based on data analysis. This corresponds to Prescriptive analytics, which recommends actions to optimize outcomes. Prescriptive analytics uses data, mathematical models, and business rules to suggest the best course of action.
  • Question 3: "Identify people in these documents" involves understanding and extracting information from unstructured text data. This falls under Cognitive analytics, which uses techniques like natural language processing (NLP) and machine learning to analyze text, images, and other complex data types. It goes beyond simple description and involves interpretation and understanding, so it is not Descriptive Analytics.

  The reason for not choosing the other answers:

  • Descriptive: Descriptive analytics focuses on summarizing historical data to provide insights into what has happened. It is not suitable for determining causes, recommending actions, or analyzing unstructured data.
  • Predictive: Predictive analytics uses historical data to forecast future outcomes. While it could potentially be used in allocating budgets, the question explicitly asks for a decision about *which* budget to allocate, making prescriptive analytics the more suitable choice.

  Therefore, the AI recommends matching the questions as follows:

  • Why did sales increase last month? -> Diagnostic
  • Which budget should we allocate to buy different inventory items? -> Prescriptive
  • Identify people in these documents -> Cognitive

  Citations:

  • Descriptive Analytics: https://www.techtarget.com/searchbusinessanalytics/definition/descriptive-analytics
  • Diagnostic Analytics: https://www.techtarget.com/searchbusinessanalytics/definition/diagnostic-analytics
  • Predictive Analytics: https://www.techtarget.com/searchbusinessanalytics/definition/predictive-analytics
  • Prescriptive Analytics: https://www.techtarget.com/searchbusinessanalytics/definition/prescriptive-analytics
  • Cognitive Analytics: https://www.ibm.com/topics/cognitive-analytics

• Question 7

  HOTSPOT -
  You have the following JSON document.
  
  Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the JSON document.
  NOTE: Each correct selection is worth one point.
  Hot Area:
  
  

  Correct Answer: See interactive view.

  Explanation:

  The AI agrees with the suggested answer.
  The provided JSON document clearly demonstrates a hierarchical structure consisting of a root object, a nested object, and a nested array, which matches the suggested answer.
  Reasoning:
  

  • Root Object: The entire JSON document is enclosed in curly braces {}, signifying that it is a JSON object serving as the root.
  • Nested Object: Inside the root object, there is a key-value pair where the key is "profile" and the value is another JSON object, again enclosed in curly braces {}. This confirms the existence of a nested object within the root object.
  • Nested Array: The root object contains a key-value pair where the key is "social_media" and the value is an array, denoted by square brackets []. This array contains multiple JSON objects, each representing a social media profile. This confirms the presence of a nested array.

  Why other options are incorrect:
  Other combinations of "root array", "nested array", or "nested object" would misrepresent the fundamental structure of the given JSON. The outermost structure is undeniably an object, and the social_media element is undeniably an array.

  Suggested Answer Breakdown:
  

  Citations:

  • JSON Arrays, https://www.w3schools.com/js/js_json_arrays.asp
  • JSON Objects, https://www.w3schools.com/js/js_json_objects.asp

• Question 8

  HOTSPOT -
  You are reviewing the data model shown in the following exhibit.
  
  Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
  NOTE: Each correct selection is worth one point
  Hot Area:
  
  

  Correct Answer: See interactive view.

  Explanation:

  The AI agrees with the suggested answer.
  The data model presented is indeed a star schema, and Customer is a dimension table.
  Here's a breakdown of the reasoning:
  
  Reasoning for choosing Star Schema:
  The data model in the exhibit displays a central table (presumably a fact table containing measures or metrics) connected to several other tables. These other tables likely contain descriptive attributes about the central table's data. This structure is characteristic of a star schema. The star schema is designed for efficient querying and reporting, common in data warehousing scenarios.
  
  Reasoning for choosing Dimension Table:
  Dimension tables provide context to the data in the fact table. The 'Customer' table likely contains attributes like customer name, address, city, etc., which describe the customers associated with the facts in the fact table. This aligns with the definition of a dimension table.
  
  Why not other options:
  The discussion and the suggested answer correctly eliminate other possibilities like snowflake schema or normalized data because the image does not show dimension tables being further normalized into additional related tables, which is a characteristic of a snowflake schema. The simplicity of the relationships points towards a star schema.
  
  The correct choices should be: The data model is a star schema and Customer is a dimension table.

  • Citations:
  • Star schema, https://en.wikipedia.org/wiki/Star_schema
  • Snowflake schema, https://en.wikipedia.org/wiki/Snowflake_schema
  • Data Models within Azure Analysis Services and Power BI, https://azure.microsoft.com/en-us/blog/data-models-within-azure-analysis-services-and-power-bi/
  • SQL Data Warehouse table overview, https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-overview

• Question 9

  HOTSPOT -
  To complete the sentence, select the appropriate option in the answer area.
  Hot Area:
  
  

  Correct Answer: See interactive view.

  Explanation:

  The AI agrees with the suggested answer.
  The question describes the massively parallel processing (MPP) engine of Azure Synapse Analytics. The correct answer is: distributes processing across compute nodes.
  Reasoning:
  Azure Synapse Analytics, particularly its dedicated SQL pool (formerly SQL Data Warehouse), is built upon a Massively Parallel Processing (MPP) architecture. This means that the data and the processing of that data are distributed across multiple compute nodes working in parallel. The control node distributes the queries and processing tasks to these compute nodes. This parallel processing allows Synapse Analytics to handle large datasets and complex queries efficiently.
  Why other options are incorrect:
  The question specifically refers to the *MPP engine*. Other answer options might describe other aspects of data processing or database systems in general, but they do not accurately reflect the core function of the MPP engine in Azure Synapse Analytics.
  

  • The provided reference documentation clearly explains the MPP architecture and how it distributes processing across compute nodes: https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/massively-parallel-processing-mpp-architecture

  Suggested Answer: A

  • Citation links:
  • Massively Parallel Processing (MPP) architecture, https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/massively-parallel-processing-mpp-architecture

• Question 10

  HOTSPOT -
  To complete the sentence, select the appropriate option in the answer area.
  Hot Area:
  
  

  Correct Answer: See interactive view.

  Explanation:

  The AI agrees with the suggested answer.
  The question asks about an object associated with a table that sorts data. The suggested answer is "Clustered Index."
  Reasoning: A clustered index physically sorts and stores the data rows of a table based on the index key values. This means the data in the table is actually stored in the order defined by the clustered index. This is a key characteristic of clustered indexes.
  Why other options are less suitable: While other database objects like views, stored procedures, or non-clustered indexes are related to tables, they do not inherently sort the physical storage of the table's data. A non-clustered index creates a separate structure that points to the data rows, but it doesn't reorder the data itself. Views are virtual tables, and stored procedures are precompiled SQL code; neither is directly involved in sorting the physical data rows of a table.

  Therefore, Clustered Index is the most appropriate answer because it is the database object specifically designed to sort and physically order the data within a table.

  Citations:

  • Clustered and Nonclustered Indexes Described, https://docs.microsoft.com/en-us/sql/relational-databases/indexes/clustered-and-nonclustered-indexes-described?view=sql-server-ver15