Q21. You are designing an application that will use an Azure Data Lake Storage Gen 2 account to store petabytes of license plate photos from toll booths. The account will use zone-redundant storage (ZRS). You identify the following usage patterns: * The data will be accessed several times a day during the first 30 days after the data is created. The data must meet an availability SU of 99.9%. * After 90 days, the data will be accessed infrequently but must be available within 30 seconds. * After 365 days, the data will be accessed infrequently but must be available within five minutes.
Explanation:
Box 1: Hot
The data will be accessed several times a day during the first 30 days after the data is created. The data must meet an availability SLA of 99.9%.
Box 2: Cool
After 90 days, the data will be accessed infrequently but must be available within 30 seconds.
Data in the Cool tier should be stored for a minimum of 30 days.
When your data is stored in an online access tier (either Hot or Cool), users can access it immediately. The Hot tier is the best choice for data that is in active use, while the Cool tier is ideal for data that is accessed less frequently, but that still must be available for reading and writing.
Box 3: Cool
After 365 days, the data will be accessed infrequently but must be available within five minutes.
Reference: https://docs.microsoft.com/en-us/azure/storage/blobs/access-tiers-overview
https://docs.microsoft.com/en-us/azure/storage/blobs/archive-rehydrate-overview
Q22. You have an Azure Data Lake Storage Gen2 account named account1 that stores logs as shown in the following table. You do not expect that the logs will be accessed during the retention periods. You need to recommend a solution for account1 that meets the following requirements: * Automatically deletes the logs at the end of each retention period * Minimizes storage costs What should you include in the recommendation? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Explanation:
Table Description automatically generated
Box 1: Store the infrastructure logs in the Cool access tier and the application logs in the Archive access tier For infrastructure logs: Cool tier – An online tier optimized for storing data that is infrequently accessed or modified. Data in the cool tier should be stored for a minimum of 30 days. The cool tier has lower storage costs and higher access costs compared to the hot tier.
For application logs: Archive tier – An offline tier optimized for storing data that is rarely accessed, and that has flexible latency requirements, on the order of hours. Data in the archive tier should be stored for a minimum of 180 days.
Box 2: Azure Blob storage lifecycle management rules
Blob storage lifecycle management offers a rule-based policy that you can use to transition your data to the desired access tier when your specified conditions are met. You can also use lifecycle management to expire data at the end of its life.
Reference:
https://docs.microsoft.com/en-us/azure/storage/blobs/access-tiers-overview
Q24. You are designing an application that will store petabytes of medical imaging data When the data is first created, the data will be accessed frequently during the first week. After one month, the data must be accessible within 30 seconds, but files will be accessed infrequently. After one year, the data will be accessed infrequently but must be accessible within five minutes. You need to select a storage strategy for the data. The solution must minimize costs. Which storage tier should you use for each time frame? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Explanation:
First week: Hot
Hot – Optimized for storing data that is accessed frequently.
After one month: Cool
Cool – Optimized for storing data that is infrequently accessed and stored for at least 30 days.
After one year: Cool
Q25. A company plans to use Apache Spark analytics to analyze intrusion detection data. You need to recommend a solution to analyze network and system activity data for malicious activities and policy violations. The solution must minimize administrative efforts. What should you recommend?
Explanation Three common analytics use cases with Microsoft Azure Databricks Recommendation engines, churn analysis, and intrusion detection are common scenarios that many organizations are solving across multiple industries. They require machine learning, streaming analytics, and utilize massive amounts of data processing that can be difficult to scale without the right tools. Recommendation engines, churn analysis, and intrusion detection are common scenarios that many organizations are solving across multiple industries. They require machine learning, streaming analytics, and utilize massive amounts of data processing that can be difficult to scale without the right tools. Note: Recommendation engines, churn analysis, and intrusion detection are common scenarios that many organizations are solving across multiple industries. They require machine learning, streaming analytics, and utilize massive amounts of data processing that can be difficult to scale without the right tools. Reference: https://azure.microsoft.com/es-es/blog/three-critical-analytics-use-cases-with-microsoft-azure-databricks/ Topic 1, Litware, inc. Case study This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided. To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study. At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section. To start the case study To display the first question in this case study, click the button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the button to return to the question. Overview Litware, Inc. owns and operates 300 convenience stores across the US. The company sells a variety of packaged foods and drinks, as well as a variety of prepared foods, such as sandwiches and pizzas. Litware has a loyalty club whereby members can get daily discounts on specific items by providing their membership number at checkout. Litware employs business analysts who prefer to analyze data by using Microsoft Power BI, and data scientists who prefer analyzing data in Azure Databricks notebooks. Requirements Business Goals Litware wants to create a new analytics environment in Azure to meet the following requirements: See inventory levels across the stores. Data must be updated as close to real time as possible. Execute ad hoc analytical queries on historical data to identify whether the loyalty club discounts increase sales of the discounted products. Every four hours, notify store employees about how many prepared food items to produce based on historical demand from the sales data. Technical Requirements Litware identifies the following technical requirements: Minimize the number of different Azure services needed to achieve the business goals. Use platform as a service (PaaS) offerings whenever possible and avoid having to provision virtual machines that must be managed by Litware. Ensure that the analytical data store is accessible only to the company’s on-premises network and Azure services. Use Azure Active Directory (Azure AD) authentication whenever possible. Use the principle of least privilege when designing security. Stage Inventory data in Azure Data Lake Storage Gen2 before loading the data into the analytical data store. Litware wants to remove transient data from Data Lake Storage once the data is no longer in use. Files that have a modified date that is older than 14 days must be removed. Limit the business analysts’ access to customer contact information, such as phone numbers, because this type of data is not analytically relevant. Ensure that you can quickly restore a copy of the analytical data store within one hour in the event of corruption or accidental deletion. Planned Environment Litware plans to implement the following environment: The application development team will create an Azure event hub to receive real-time sales data, including store number, date, time, product ID, customer loyalty number, price, and discount amount, from the point of sale (POS) system and output the data to data storage in Azure. Customer data, including name, contact information, and loyalty number, comes from Salesforce, a SaaS application, and can be imported into Azure once every eight hours. Row modified dates are not trusted in the source table. Product data, including product ID, name, and category, comes from Salesforce and can be imported into Azure once every eight hours. Row modified dates are not trusted in the source table. Daily inventory data comes from a Microsoft SQL server located on a private network. Litware currently has 5 TB of historical sales data and 100 GB of customer data. The company expects approximately 100 GB of new data per month for the next year. Litware will build a custom application named FoodPrep to provide store employees with the calculation results of how many prepared food items to produce every four hours. Litware does not plan to implement Azure ExpressRoute or a VPN between the on-premises network and Azure.
Q30. You have an Azure subscription that contains an Azure Data Lake Storage Gen2 account named storage1 and an Azure Synapse Analytics workspace named Workspace1. Workspace1 has a serverless SQL pool. You use the serverless SQL pool to query customer orders from the files in storage1. You run the following query. SELECT * FROM OPENROWSET(BULK ‘https://storage1.blob.core.windows.net/data/orders/year =* /month =* / *.* ‘, FORMAT = ‘parquet’) AS customerorders WHERE customerorders. filepath(1) = ‘2024’ AND customerorders.filepath(2) IN (‘3′,’4’); For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Explanation:
Storage1 provides a hierarchical namespace: Yes
Files from March 2025 will be included: No
Only files that have a Parquet file extension will be included: Yes
Query Breakdown
* Data Source:
* The OPENROWSET function queries data stored in Azure Data Lake Storage Gen2 (storage1) using the serverless SQL pool in Synapse Analytics.
* The data is stored in Parquet files in the folder structure data/orders/year=YYYY/month=MM/.
* Query Filter:
* The filter conditions in the query are:
* customerorders.filepath(1) = ‘2024’: Limits the query to files in the folder year=2024.
* customerorders.filepath(2) IN (‘3’, ‘4’): Limits the query to files in the subfolders month=3 or month=4.
* File Format:
* The FORMAT = ‘parquet’ clause specifies that only Parquet files will be queried.
Statements Analysis
* Storage1 provides a hierarchical namespace.aswer: Yes
* Azure Data Lake Storage Gen2 supports a hierarchical namespace, which enables folder-based organization.
* The folder structure (e.g., data/orders/year=2024/month=3/) demonstrates the use of a hierarchical namespace.
* Files from March 2025 will be included.aswer: No
* The query explicitly filters for year=2024, so files from 2025 will not be included in the results.
* Only files that have a Parquet file extension will be included.aswer: Yes
* The FORMAT = ‘parquet’ clause in the query ensures that only Parquet files are queried. Files with other extensions (e.g., .csv or .json) will not be included.
Q35. You have an Azure subscription that contains an Azure Databricks workspace named databricks1 and an Azure Synapse Analytics workspace named synapse1. The synapse1 workspace contains an Apache Spark pool named pool1. You need to share an Apache Hive catalog of pool1 with databricks1. What should you do? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Explanation:
Box 1: Azure SQL Database
Use external Hive Metastore for Synapse Spark Pool
Azure Synapse Analytics allows Apache Spark pools in the same workspace to share a managed HMS (Hive Metastore) compatible metastore as their catalog.
Set up linked service to Hive Metastore
Follow below steps to set up a linked service to the external Hive Metastore in Synapse workspace.
* Open Synapse Studio, go to Manage > Linked services at left, click New to create a new linked service.
* Set up Hive Metastore linked service
* Choose Azure SQL Database or Azure Database for MySQL based on your database type, click Continue.
* Provide Name of the linked service. Record the name of the linked service, this info will be used to configure Spark shortly.
* You can either select Azure SQL Database/Azure Database for MySQL for the external Hive Metastore from Azure subscription list, or enter the info manually.
* Provide User name and Password to set up the connection.
* Test connection to verify the username and password.
* Click Create to create the linked service.
Box 2: A Hive Metastore
Reference: https://docs.microsoft.com/en-us/azure/synapse-analytics/spark/apache-spark-external-metastore
Q38. You have an Azure subscription that contains an Azure Data Lake Storage account. The storage account contains a data lake named DataLake1. You plan to use an Azure data factory to ingest data from a folder in DataLake1, transform the data, and land the data in another folder. You need to ensure that the data factory can read and write data from any folder in the DataLake1 file system. The solution must meet the following requirements: * Minimize the risk of unauthorized user access. * Use the principle of least privilege. * Minimize maintenance effort. How should you configure access to the storage account for the data factory? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Explanation
Text Description automatically generated with low confidence
Box 1: Azure Active Directory (Azure AD)
On Azure, managed identities eliminate the need for developers having to manage credentials by providing an identity for the Azure resource in Azure AD and using it to obtain Azure Active Directory (Azure AD) tokens.
Box 2: a managed identity
A data factory can be associated with a managed identity for Azure resources, which represents this specific data factory. You can directly use this managed identity for Data Lake Storage Gen2 authentication, similar to using your own service principal. It allows this designated factory to access and copy data to or from your Data Lake Storage Gen2.
Note: The Azure Data Lake Storage Gen2 connector supports the following authentication types.
* Account key authentication
* Service principal authentication
* Managed identities for Azure resources authentication
Reference:
https://docs.microsoft.com/en-us/azure/active-directory/managed-identities-azure-resources/overview
https://docs.microsoft.com/en-us/azure/data-factory/connector-azure-data-lake-storage
Explanation
Graphical user interface, text, application, email Description automatically generated
Box 1: 0
The YearlyIncome column is of the money data type.
The Default masking function: Full masking according to the data types of the designated fields
* Use a zero value for numeric data types (bigint, bit, decimal, int, money, numeric, smallint, smallmoney, tinyint, float, real).
Box 2: the values stored in the database
Users with administrator privileges are always excluded from masking, and see the original data without any mask.
Reference:
https://docs.microsoft.com/en-us/azure/azure-sql/database/dynamic-data-masking-overview
Q40. Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You are designing an Azure Stream Analytics solution that will analyze Twitter data. You need to count the tweets in each 10-second window. The solution must ensure that each tweet is counted only once. Solution: You use a tumbling window, and you set the window size to 10 seconds. Does this meet the goal?
Explanation
Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time intervals. The following diagram illustrates a stream with a series of events and how they are mapped into 10-second tumbling windows.
Reference:
https://docs.microsoft.com/en-us/stream-analytics-query/tumbling-window-azure-stream-analytics