[Oct 04, 2022] Dumps Collection DP-203 Test Engine Dumps Training With 250 Questions
Microsoft DP-203 Dumps - 100% Cover Real Exam Questions
Microsoft DP-203 Exam Syllabus Topics:
| Topic | Details |
|---|---|
Design and Implement Data Storage (40-45%) | |
| Design a data storage structure | - design an Azure Data Lake solution - recommend file types for storage - recommend file types for analytical queries - design for efficient querying - design for data pruning - design a folder structure that represents the levels of data transformation - design a distribution strategy - design a data archiving solution |
| Design a partition strategy | - design a partition strategy for files - design a partition strategy for analytical workloads - design a partition strategy for efficiency/performance - design a partition strategy for Azure Synapse Analytics - identify when partitioning is needed in Azure Data Lake Storage Gen2 |
| Design the serving layer | - design star schemas - design slowly changing dimensions - design a dimensional hierarchy - design a solution for temporal data - design for incremental loading - design analytical stores - design metastores in Azure Synapse Analytics and Azure Databricks |
| Implement physical data storage structures | - implement compression - implement partitioning - implement sharding - implement different table geometries with Azure Synapse Analytics pools - implement data redundancy - implement distributions - implement data archiving |
| Implement logical data structures | - build a temporal data solution - build a slowly changing dimension - build a logical folder structure - build external tables - implement file and folder structures for efficient querying and data pruning |
| Implement the serving layer | - deliver data in a relational star schema - deliver data in Parquet files - maintain metadata - implement a dimensional hierarchy |
Design and Develop Data Processing (25-30%) | |
| Ingest and transform data | - transform data by using Apache Spark - transform data by using Transact-SQL - transform data by using Data Factory - transform data by using Azure Synapse Pipelines - transform data by using Stream Analytics - cleanse data - split data - shred JSON - encode and decode data - configure error handling for the transformation - normalize and denormalize values - transform data by using Scala - perform data exploratory analysis |
| Design and develop a batch processing solution | - develop batch processing solutions by using Data Factory, Data Lake, Spark, Azure Synapse Pipelines, PolyBase, and Azure Databricks - create data pipelines - design and implement incremental data loads - design and develop slowly changing dimensions - handle security and compliance requirements - scale resources - configure the batch size - design and create tests for data pipelines - integrate Jupyter/Python notebooks into a data pipeline - handle duplicate data - handle missing data - handle late-arriving data - upsert data - regress to a previous state - design and configure exception handling - configure batch retention - design a batch processing solution - debug Spark jobs by using the Spark UI |
| Design and develop a stream processing solution | - develop a stream processing solution by using Stream Analytics, Azure Databricks, and Azure Event Hubs - process data by using Spark structured streaming - monitor for performance and functional regressions - design and create windowed aggregates - handle schema drift - process time series data - process across partitions - process within one partition - configure checkpoints/watermarking during processing - scale resources - design and create tests for data pipelines - optimize pipelines for analytical or transactional purposes - handle interruptions - design and configure exception handling - upsert data - replay archived stream data - design a stream processing solution |
| Manage batches and pipelines | - trigger batches - handle failed batch loads - validate batch loads - manage data pipelines in Data Factory/Synapse Pipelines - schedule data pipelines in Data Factory/Synapse Pipelines - implement version control for pipeline artifacts - manage Spark jobs in a pipeline |
Design and Implement Data Security (10-15%) | |
| Design security for data policies and standards | - design data encryption for data at rest and in transit - design a data auditing strategy - design a data masking strategy - design for data privacy - design a data retention policy - design to purge data based on business requirements - design Azure role-based access control (Azure RBAC) and POSIX-like Access Control List (ACL) for Data Lake Storage Gen2 - design row-level and column-level security |
| Implement data security | - implement data masking - encrypt data at rest and in motion - implement row-level and column-level security - implement Azure RBAC - implement POSIX-like ACLs for Data Lake Storage Gen2 - implement a data retention policy - implement a data auditing strategy - manage identities, keys, and secrets across different data platform technologies - implement secure endpoints (private and public) - implement resource tokens in Azure Databricks - load a DataFrame with sensitive information - write encrypted data to tables or Parquet files - manage sensitive information |
Monitor and Optimize Data Storage and Data Processing (10-15%) | |
| Monitor data storage and data processing | - implement logging used by Azure Monitor - configure monitoring services - measure performance of data movement - monitor and update statistics about data across a system - monitor data pipeline performance - measure query performance - monitor cluster performance - understand custom logging options - schedule and monitor pipeline tests - interpret Azure Monitor metrics and logs - interpret a Spark directed acyclic graph (DAG) |
NEW QUESTION 143
You are designing a security model for an Azure Synapse Analytics dedicated SQL pool that will support multiple companies. You need to ensure that users from each company can view only the data of their respective company. Which two objects should you include in the solution? Each correct answer presents part of the solution NOTE: Each correct selection it worth one point.
- A. a security policy
- B. a custom role-based access control (RBAC) role.
- C. a column encryption key
- D. a predicate function
- E. asymmetric keys
Answer: A,B
Explanation:
Reference:
https://docs.microsoft.com/en-us/sql/relational-databases/security/row-level-security
https://docs.microsoft.com/en-us/azure/synapse-analytics/security/synapse-workspace-access-control-overview
NEW QUESTION 144
You are creating an Azure Data Factory data flow that will ingest data from a CSV file, cast columns to specified types of data, and insert the data into a table in an Azure Synapse Analytics dedicated SQL pool. The CSV file contains columns named username, comment and date.
The data flow already contains the following:
* A source transformation
* A Derived Column transformation to set the appropriate types of data
* A sink transformation to land the data in the pool
You need to ensure that the data flow meets the following requirements;
* All valid rows must be written to the destination table.
* Truncation errors in the comment column must be avoided proactively.
* Any rows containing comment values that will cause truncation errors upon insert must be written to a file in blob storage.
Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point
- A. Add a filter transformation that filters out rows which will cause truncation errors.
- B. Add a sink transformation that writes the rows to a file in blob storage.
- C. Add a select transformation that selects only the rows which will cause truncation errors.
- D. Add a Conditional Split transformation that separates the rows which will cause truncation errors.
Answer: B,D
NEW QUESTION 145
You need to create a partitioned table in an Azure Synapse Analytics dedicated SQL pool.
How should you complete the Transact-SQL statement? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/sql/t-sql/statements/create-table-azure-sql-data-warehouse?
NEW QUESTION 146
You have an Azure SQL database named Database1 and two Azure event hubs named HubA and HubB. The data consumed from each source is shown in the following table.
You need to implement Azure Stream Analytics to calculate the average fare per mile by driver.
How should you configure the Stream Analytics input for each source? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-use-reference-data
NEW QUESTION 147
You need to design a data storage structure for the product sales transactions. The solution must meet the sales transaction dataset requirements.
What should you include in the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Topic 2, Litware, inc.
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.
NEW QUESTION 148
You are designing an Azure Synapse Analytics dedicated SQL pool.
Groups will have access to sensitive data in the pool as shown in the following table.
You have policies for the sensitive dat
a. The policies vary be region as shown in the following table.
You have a table of patients for each region. The tables contain the following potentially sensitive columns.
You are designing dynamic data masking to maintain compliance.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/azure-sql/database/dynamic-data-masking-overview
NEW QUESTION 149
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 scenario, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have an Azure Storage account that contains 100 GB of files. The files contain text and numerical values. 75% of the rows contain description data that has an average length of 1.1 MB.
You plan to copy the data from the storage account to an Azure SQL data warehouse.
You need to prepare the files to ensure that the data copies quickly.
Solution: You modify the files to ensure that each row is less than 1 MB.
Does this meet the goal?
- A. Yes
- B. No
Answer: A
Explanation:
Explanation
When exporting data into an ORC File Format, you might get Java out-of-memory errors when there are large text columns. To work around this limitation, export only a subset of the columns.
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/guidance-for-loading-data
NEW QUESTION 150
You have an Azure Synapse Analystics dedicated SQL pool that contains a table named Contacts. Contacts contains a column named Phone.
You need to ensure that users in a specific role only see the last four digits of a phone number when querying the Phone column.
What should you include in the solution?
- A. a default value
- B. dynamic data masking
- C. column encryption
- D. table partitions
- E. row-level security (RLS)
Answer: B
Explanation:
Dynamic data masking helps prevent unauthorized access to sensitive data by enabling customers to designate how much of the sensitive data to reveal with minimal impact on the application layer. It's a policy-based security feature that hides the sensitive data in the result set of a query over designated database fields, while the data in the database is not changed.
Reference:
https://docs.microsoft.com/en-us/azure/azure-sql/database/dynamic-data-masking-overview
NEW QUESTION 151
You are designing an Azure Stream Analytics solution that receives instant messaging data from an Azure Event Hub.
You need to ensure that the output from the Stream Analytics job counts the number of messages per time zone every 15 seconds.
How should you complete the Stream Analytics query? To answer, select the appropriate options in the answer are a.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-window-functions
NEW QUESTION 152
You are designing an inventory updates table in an Azure Synapse Analytics dedicated SQL pool. The table will have a clustered columnstore index and will include the following columns:
You identify the following usage patterns:
* Analysts will most commonly analyze transactions for a warehouse.
* Queries will summarize by product category type, date, and/or inventory event type.
You need to recommend a partition strategy for the table to minimize query times.
On which column should you partition the table?
- A. WarehouseID
- B. ProductCategoryTypeID
- C. EventTypeID
- D. EventDate
Answer: A
Explanation:
Explanation
The number of records for each warehouse is big enough for a good partitioning.
Note: Table partitions enable you to divide your data into smaller groups of data. In most cases, table partitions are created on a date column.
When creating partitions on clustered columnstore tables, it is important to consider how many rows belong to each partition. For optimal compression and performance of clustered columnstore tables, a minimum of 1 million rows per distribution and partition is needed. Before partitions are created, dedicated SQL pool already divides each table into 60 distributed databases.
NEW QUESTION 153
You are designing a solution that will copy Parquet files stored in an Azure Blob storage account to an Azure Data Lake Storage Gen2 account.
The data will be loaded daily to the data lake and will use a folder structure of 2022/{Month}/{Day}/.
You need to design a daily Azure Data Factory data load to minimize the data transfer between the two accounts.
Which two configurations should you include in the design? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. Specify a file naming pattern for the destination.
- B. Delete the files in the destination before loading new data.
- C. Filter by the last modified date of the source files.
- D. Delete the source files after they are copied.
Answer: C,D
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/connector-azure-data-lake-storage
NEW QUESTION 154
You have an Azure Data Lake Storage Gen2 account that contains a JSON file for customers. The file contains two attributes named FirstName and LastName.
You need to copy the data from the JSON file to an Azure Synapse Analytics table by using Azure Databricks. A new column must be created that concatenates the FirstName and LastName values.
You create the following components:
A destination table in Azure Synapse
An Azure Blob storage container
A service principal
Which five actions should you perform in sequence next in is Databricks notebook? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/azure-databricks/databricks-extract-load-sql-data-warehouse
NEW QUESTION 155
You need to trigger an Azure Data Factory pipeline when a file arrives in an Azure Data Lake Storage Gen2 container.
Which resource provider should you enable?
- A. Microsoft.Sql
- B. Microsoft.EventHub
- C. Microsoft-Automation
- D. Microsoft.EventGrid
Answer: D
NEW QUESTION 156
You have an Azure Data Lake Storage Gen2 account that contains a JSON file for customers. The file contains two attributes named FirstName and LastName.
You need to copy the data from the JSON file to an Azure Synapse Analytics table by using Azure Databricks.
A new column must be created that concatenates the FirstName and LastName values.
You create the following components:
* A destination table in Azure Synapse
* An Azure Blob storage container
* A service principal
Which five actions should you perform in sequence next in is Databricks notebook? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation
Step 1: Read the file into a data frame.
You can load the json files as a data frame in Azure Databricks.
Step 2: Perform transformations on the data frame.
Step 3:Specify a temporary folder to stage the data
Specify a temporary folder to use while moving data between Azure Databricks and Azure Synapse.
Step 4: Write the results to a table in Azure Synapse.
You upload the transformed data frame into Azure Synapse. You use the Azure Synapse connector for Azure Databricks to directly upload a dataframe as a table in a Azure Synapse.
Step 5: Drop the data frame
Clean up resources. You can terminate the cluster. From the Azure Databricks workspace, select Clusters on the left. For the cluster to terminate, under Actions, point to the ellipsis (...) and select the Terminate icon.
Reference:
https://docs.microsoft.com/en-us/azure/azure-databricks/databricks-extract-load-sql-data-warehouse
NEW QUESTION 157
You implement an enterprise data warehouse in Azure Synapse Analytics.
You have a large fact table that is 10 terabytes (TB) in size.
Incoming queries use the primary key SaleKey column to retrieve data as displayed in the following table:
You need to distribute the large fact table across multiple nodes to optimize performance of the table.
Which technology should you use?
- A. hash distributed table with clustered index
- B. round robin distributed table with clustered Columnstore index
- C. hash distributed table with clustered Columnstore index
- D. round robin distributed table with clustered index
- E. heap table with distribution replicate
Answer: C
Explanation:
Explanation
Hash-distributed tables improve query performance on large fact tables.
Columnstore indexes can achieve up to 100x better performance on analytics and data warehousing workloads and up to 10x better data compression than traditional rowstore indexes.
Reference:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables-distribute
https://docs.microsoft.com/en-us/sql/relational-databases/indexes/columnstore-indexes-query-performance
NEW QUESTION 158
You have an Azure subscription that is linked to a hybrid Azure Active Directory (Azure AD) tenant. The subscription contains an Azure Synapse Analytics SQL pool named Pool1.
You need to recommend an authentication solution for Pool1. The solution must support multi-factor authentication (MFA) and database-level authentication.
Which authentication solution or solutions should you include in the recommendation? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-authentication
NEW QUESTION 159
You have an Azure Active Directory (Azure AD) tenant that contains a security group named Group1. You have an Azure Synapse Analytics dedicated SQL pool named dw1 that contains a schema named schema1.
You need to grant Group1 read-only permissions to all the tables and views in schema1. The solution must use the principle of least privilege.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.
Answer:
Explanation:
Explanation
Step 1: Create a database role named Role1 and grant Role1 SELECT permissions to schema You need to grant Group1 read-only permissions to all the tables and views in schema1.
Place one or more database users into a database role and then assign permissions to the database role.
Step 2: Assign Rol1 to the Group database user
Step 3: Assign the Azure role-based access control (Azure RBAC) Reader role for dw1 to Group1 Reference:
https://docs.microsoft.com/en-us/azure/data-share/how-to-share-from-sql
NEW QUESTION 160
You have an Azure subscription that contains the following resources:
An Azure Active Directory (Azure AD) tenant that contains a security group named Group1 An Azure Synapse Analytics SQL pool named Pool1 You need to control the access of Group1 to specific columns and rows in a table in Pool1.
Which Transact-SQL commands should you use? To answer, select the appropriate options in the answer area.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/column-level-security
NEW QUESTION 161
You use Azure Data Lake Storage Gen2 to store data that data scientists and data engineers will query by using Azure Databricks interactive notebooks. Users will have access only to the Data Lake Storage folders that relate to the projects on which they work.
You need to recommend which authentication methods to use for Databricks and Data Lake Storage to provide the users with the appropriate access. The solution must minimize administrative effort and development effort.
Which authentication method should you recommend for each Azure service? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/databricks/data/data-sources/azure/adls-gen2/azure-datalake-gen2-sas-access
https://docs.microsoft.com/en-us/azure/databricks/security/credential-passthrough/adls-passthrough
NEW QUESTION 162
You have an Azure Data Factory instance that contains two pipelines named Pipeline1 and Pipeline2.
Pipeline1 has the activities shown in the following exhibit.
Pipeline2 has the activities shown in the following exhibit.
You execute Pipeline2, and Stored procedure1 in Pipeline1 fails.
What is the status of the pipeline runs?
- A. Pipeline1 failed and Pipeline2 succeeded.
- B. Pipeline1 and Pipeline2 failed.
- C. Pipeline1 succeeded and Pipeline2 failed.
- D. Pipeline1 and Pipeline2 succeeded.
Answer: D
Explanation:
Explanation
Activities are linked together via dependencies. A dependency has a condition of one of the following:
Succeeded, Failed, Skipped, or Completed.
Consider Pipeline1:
If we have a pipeline with two activities where Activity2 has a failure dependency on Activity1, the pipeline will not fail just because Activity1 failed. If Activity1 fails and Activity2 succeeds, the pipeline will succeed.
This scenario is treated as a try-catch block by Data Factory.
Waterfall chart Description automatically generated with medium confidence
The failure dependency means this pipeline reports success.
Note:
If we have a pipeline containing Activity1 and Activity2, and Activity2 has a success dependency on Activity1, it will only execute if Activity1 is successful. In this scenario, if Activity1 fails, the pipeline will fail.
Reference:
https://datasavvy.me/category/azure-data-factory/
NEW QUESTION 163
You have an Azure subscription that contains a logical Microsoft SQL server named Server1. Server1 hosts an Azure Synapse Analytics SQL dedicated pool named Pool1.
You need to recommend a Transparent Data Encryption (TDE) solution for Server1. The solution must meet the following requirements:
* Track the usage of encryption keys.
* Maintain the access of client apps to Pool1 in the event of an Azure datacenter outage that affects the availability of the encryption keys.
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.
Answer:
Explanation:
Explanation
Box 1: TDE with customer-managed keys
Customer-managed keys are stored in the Azure Key Vault. You can monitor how and when your key vaults are accessed, and by whom. You can do this by enabling logging for Azure Key Vault, which saves information in an Azure storage account that you provide.
Box 2: Create and configure Azure key vaults in two Azure regions
The contents of your key vault are replicated within the region and to a secondary region at least 150 miles away, but within the same geography to maintain high durability of your keys and secrets.
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/security/workspaces-encryption
https://docs.microsoft.com/en-us/azure/key-vault/general/logging
NEW QUESTION 164
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.
Answer:
Explanation:
NEW QUESTION 165
You have the following Azure Stream Analytics query.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Yes
You can now use a new extension of Azure Stream Analytics SQL to specify the number of partitions of a stream when reshuffling the data.
The outcome is a stream that has the same partition scheme. Please see below for an example:
WITH step1 AS (SELECT * FROM [input1] PARTITION BY DeviceID INTO 10),
step2 AS (SELECT * FROM [input2] PARTITION BY DeviceID INTO 10)
SELECT * INTO [output] FROM step1 PARTITION BY DeviceID UNION step2 PARTITION BY DeviceID Note: The new extension of Azure Stream Analytics SQL includes a keyword INTO that allows you to specify the number of partitions for a stream when performing reshuffling using a PARTITION BY statement.
Box 2: Yes
When joining two streams of data explicitly repartitioned, these streams must have the same partition key and partition count.
Box 3: Yes
10 partitions x six SUs = 60 SUs is fine.
Note: Remember, Streaming Unit (SU) count, which is the unit of scale for Azure Stream Analytics, must be adjusted so the number of physical resources available to the job can fit the partitioned flow. In general, six SUs is a good number to assign to each partition. In case there are insufficient resources assigned to the job, the system will only apply the repartition if it benefits the job.
Reference:
https://azure.microsoft.com/en-in/blog/maximize-throughput-with-repartitioning-in-azure-stream-analytics/
NEW QUESTION 166
You have an Azure subscription that contains an Azure Data Lake Storage account named myaccount1. The myaccount1 account contains two containers named container1 and contained. The subscription is linked to an Azure Active Directory (Azure AD) tenant that contains a security group named Group1.
You need to grant Group1 read access to contamer1. The solution must use the principle of least privilege. Which role should you assign to Group1?
- A. Storage Table Data Reader for myaccount1
- B. Storage Blob Data Reader for myaccount1
- C. Storage Table Data Reader for container1
- D. Storage Blob Data Reader for container1
Answer: D
NEW QUESTION 167
......
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