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質問 # 117
You have an Azure Synapse Analytics serverless SQL pool, an Azure Synapse Analytics dedicated SQL pool, an Apache Spark pool, and an Azure Data Lake Storage Gen2 account.
You need to create a table in a lake database. The table must be available to both the serverless SQL pool and the Spark pool.
Where should you create the table, and Which file format should you use for data in the table? TO answer, select the appropriate options in the answer are a.
NOTE: Each correct selection is worth one point.
正解:
解説:
質問 # 118
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 plan to create an Azure Databricks workspace that has a tiered structure. The workspace will contain the following three workloads:
* A workload for data engineers who will use Python and SQL.
* A workload for jobs that will run notebooks that use Python, Scala, and SOL.
* A workload that data scientists will use to perform ad hoc analysis in Scala and R.
The enterprise architecture team at your company identifies the following standards for Databricks environments:
* The data engineers must share a cluster.
* The job cluster will be managed by using a request process whereby data scientists and data engineers provide packaged notebooks for deployment to the cluster.
* All the data scientists must be assigned their own cluster that terminates automatically after 120 minutes of inactivity. Currently, there are three data scientists.
You need to create the Databricks clusters for the workloads.
Solution: You create a Standard cluster for each data scientist, a High Concurrency cluster for the data engineers, and a Standard cluster for the jobs.
Does this meet the goal?
- A. No
- B. Yes
正解:A
解説:
Explanation
We would need a High Concurrency cluster for the jobs.
Note:
Standard clusters are recommended for a single user. Standard can run workloads developed in any language:
Python, R, Scala, and SQL.
A high concurrency cluster is a managed cloud resource. The key benefits of high concurrency clusters are that they provide Apache Spark-native fine-grained sharing for maximum resource utilization and minimum query latencies.
Reference:
https://docs.azuredatabricks.net/clusters/configure.html
質問 # 119
You are building an Azure Stream Analytics job to identify how much time a user spends interacting with a feature on a webpage.
The job receives events based on user actions on the webpage. Each row of data represents an event. Each event has a type of either 'start' or 'end'.
You need to calculate the duration between start and end events.
How should you complete the query? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解:
解説:
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-stream-analytics-query-patterns
質問 # 120
You are creating dimensions for a data warehouse in an Azure Synapse Analytics dedicated SQL pool.
You create a table by using the Transact-SQL statement 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.
正解:
解説:
Explanation
Box 1: Type 2
A Type 2 SCD supports versioning of dimension members. Often the source system doesn't store versions, so the data warehouse load process detects and manages changes in a dimension table. In this case, the dimension table must use a surrogate key to provide a unique reference to a version of the dimension member. It also includes columns that define the date range validity of the version (for example, StartDate and EndDate) and possibly a flag column (for example, IsCurrent) to easily filter by current dimension members.
Reference:
https://docs.microsoft.com/en-us/learn/modules/populate-slowly-changing-dimensions-azure-synapse-analytics-p
質問 # 121
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
In which order should you perform the actions? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
正解:
解説:
Reference:
https://docs.microsoft.com/en-us/azure/azure-databricks/databricks-extract-load-sql-data-warehouse
質問 # 122
You use Azure Data Factory to prepare data to be queried by Azure Synapse Analytics serverless SQL pools.
Files are initially ingested into an Azure Data Lake Storage Gen2 account as 10 small JSON files. Each file contains the same data attributes and data from a subsidiary of your company.
You need to move the files to a different folder and transform the data to meet the following requirements:
* Provide the fastest possible query times.
* Automatically infer the schema from the underlying files.
How should you configure the Data Factory copy activity? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解:
解説:
Explanation
Box 1: Preserver herarchy
Compared to the flat namespace on Blob storage, the hierarchical namespace greatly improves the performance of directory management operations, which improves overall job performance.
Box 2: Parquet
Azure Data Factory parquet format is supported for Azure Data Lake Storage Gen2.
Parquet supports the schema property.
Reference:
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-introduction
https://docs.microsoft.com/en-us/azure/data-factory/format-parquet
質問 # 123
You plan to create a table in an Azure Synapse Analytics dedicated SQL pool.
Data in the table will be retained for five years. Once a year, data that is older than five years will be deleted.
You need to ensure that the data is distributed evenly across partitions. The solution must minimize the amount of time required to delete old data.
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.
正解:
解説:
Explanation
Box 1: HASH
Box 2: OrderDateKey
In most cases, table partitions are created on a date column.
A way to eliminate rollbacks is to use Metadata Only operations like partition switching for data management.
For example, rather than execute a DELETE statement to delete all rows in a table where the order_date was in October of 2001, you could partition your data early. Then you can switch out the partition with data for an empty partition from another table.
Reference:
https://docs.microsoft.com/en-us/sql/t-sql/statements/create-table-azure-sql-data-warehouse
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/best-practices-dedicated-sql-pool
質問 # 124
You develop a dataset named DBTBL1 by using Azure Databricks.
DBTBL1 contains the following columns:
* SensorTypeID
* GeographyRegionID
* Year
* Month
* Day
* Hour
* Minute
* Temperature
* WindSpeed
* Other
You need to store the data to support daily incremental load pipelines that vary for each GeographyRegionID.
The solution must minimize storage costs.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解:
解説:
Explanation
Graphical user interface, text, application Description automatically generated
質問 # 125
You have an Azure Data Lake Storage Gen2 account named adls2 that is protected by a virtual network.
You are designing a SQL pool in Azure Synapse that will use adls2 as a source.
What should you use to authenticate to adls2?
- A. a shared access signature (SAS)
- B. an Azure Active Directory (Azure AD) user
- C. a managed identity
- D. a shared key
正解:C
質問 # 126
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. EventDate
- C. EventTypeID
- D. ProductCategoryTypeID
正解:A
解説:
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.
質問 # 127
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.
正解:
解説:
Reference:
https://docs.microsoft.com/en-us/azure/data-share/how-to-share-from-sql
質問 # 128
You need to implement a Type 3 slowly changing dimension (SCD) for product category data in an Azure Synapse Analytics dedicated SQL pool.
You have a table that was created by using the following Transact-SQL statement.
Which two columns should you add to the table? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. [EffectiveScarcDate] [datetime] NOT NULL,
- B. [ProductCategory] [nvarchar] (100) NOT NULL,
- C. [OriginalProduccCacegory] [nvarchar] (100) NOT NULL,
- D. [EffectiveEndDace] [dacecime] NULL,
- E. [CurrentProduccCacegory] [nvarchar] (100) NOT NULL,
正解:C、E
解説:
Explanation
A Type 3 SCD supports storing two versions of a dimension member as separate columns. The table includes a column for the current value of a member plus either the original or previous value of the member. So Type 3 uses additional columns to track one key instance of history, rather than storing additional rows to track each change like in a Type 2 SCD.
This type of tracking may be used for one or two columns in a dimension table. It is not common to use it for many members of the same table. It is often used in combination with Type 1 or Type 2 members.
Graphical user interface, application, email Description automatically generated
Reference:
https://k21academy.com/microsoft-azure/azure-data-engineer-dp203-q-a-day-2-live-session-review/
質問 # 129
You are building an Azure Data Factory solution to process data received from Azure Event Hubs, and then ingested into an Azure Data Lake Storage Gen2 container.
The data will be ingested every five minutes from devices into JSON files. The files have the following naming pattern.
/{deviceType}/in/{YYYY}/{MM}/{DD}/{HH}/{deviceID}_{YYYY}{MM}{DD}HH}{mm}.json You need to prepare the data for batch data processing so that there is one dataset per hour per deviceType. The solution must minimize read times.
How should you configure the sink for the copy activity? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解:
解説:
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/concepts-pipeline-execution-triggers
https://docs.microsoft.com/en-us/azure/data-factory/connector-file-system
質問 # 130
You have an Azure Storage account that generates 200,000 new files daily. The file names have a format of
{YYYY}/{MM}/{DD}/{HH}/{CustomerID}.csv.
You need to design an Azure Data Factory solution that will load new data from the storage account to an Azure Data Lake once hourly. The solution must minimize load times and costs.
How should you configure the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解:
解説:
Explanation
Table Description automatically generated
Box 1: Incremental load
Box 2: Tumbling window
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.
Timeline Description automatically generated
Reference:
https://docs.microsoft.com/en-us/stream-analytics-query/tumbling-window-azure-stream-analytics
質問 # 131
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.
正解:
解説:
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/column-level-security
質問 # 132
Which Azure Data Factory components should you recommend using together to import the daily inventory data from the SQL server to Azure Data Lake Storage? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解:
解説:
Explanation
Box 1: Self-hosted integration runtime
A self-hosted IR is capable of running copy activity between a cloud data stores and a data store in private network.
Box 2: Schedule trigger
Schedule every 8 hours
Box 3: Copy activity
Scenario:
* Customer data, including name, contact information, and loyalty number, comes from Salesforce 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.
質問 # 133
You need to collect application metrics, streaming query events, and application log messages for an Azure Databrick cluster.
Which type of library and workspace should you implement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解:
解説:
Reference:
https://docs.microsoft.com/en-us/azure/architecture/databricks-monitoring/application-logs
質問 # 134
You have an Azure data factory.
You need to ensure that pipeline-run data is retained for 120 days. The solution must ensure that you can query the data by using the Kusto query language.
Which four 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.
正解:
解説:
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/monitor-using-azure-monitor
質問 # 135
You need to implement an Azure Databricks cluster that automatically connects to Azure Data Lake Storage Gen2 by using Azure Active Directory (Azure AD) integration.
How should you configure the new cluster? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解:
解説:
Explanation
Box 1: High Concurrency
Enable Azure Data Lake Storage credential passthrough for a high-concurrency cluster.
Incorrect:
Support for Azure Data Lake Storage credential passthrough on standard clusters is in Public Preview.
Standard clusters with credential passthrough are supported on Databricks Runtime 5.5 and above and are limited to a single user.
Box 2: Azure Data Lake Storage Gen1 Credential Passthrough
You can authenticate automatically to Azure Data Lake Storage Gen1 and Azure Data Lake Storage Gen2 from Azure Databricks clusters using the same Azure Active Directory (Azure AD) identity that you use to log into Azure Databricks. When you enable your cluster for Azure Data Lake Storage credential passthrough, commands that you run on that cluster can read and write data in Azure Data Lake Storage without requiring you to configure service principal credentials for access to storage.
References:
https://docs.azuredatabricks.net/spark/latest/data-sources/azure/adls-passthrough.html
質問 # 136
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.
正解:
解説:
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
質問 # 137
You have an Azure Synapse Analytics SQL pool named Pool1 on a logical Microsoft SQL server named Server1.
You need to implement Transparent Data Encryption (TDE) on Pool1 by using a custom key named key1.
Which five 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.
正解:
解説:
Explanation
Graphical user interface, text, application Description automatically generated
Step 1: Assign a managed identity to Server1
You will need an existing Managed Instance as a prerequisite.
Step 2: Create an Azure key vault and grant the managed identity permissions to the vault Create Resource and setup Azure Key Vault.
Step 3: Add key1 to the Azure key vault
The recommended way is to import an existing key from a .pfx file or get an existing key from the vault.
Alternatively, generate a new key directly in Azure Key Vault.
Step 4: Configure key1 as the TDE protector for Server1
Provide TDE Protector key
Step 5: Enable TDE on Pool1
Reference:
https://docs.microsoft.com/en-us/azure/azure-sql/managed-instance/scripts/transparent-data-encryption-byok-pow
質問 # 138
You have an on-premises data warehouse that includes the following fact tables. Both tables have the following columns: DateKey, ProductKey, RegionKey. There are 120 unique product keys and 65 unique region keys.
Queries that use the data warehouse take a long time to complete.
You plan to migrate the solution to use Azure Synapse Analytics. You need to ensure that the Azure-based solution optimizes query performance and minimizes processing skew.
What should you recommend? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point
正解:
解説:
Reference:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables-distribute
質問 # 139
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?
- A. No
- B. Yes
正解:B
解説:
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
質問 # 140
You have a self-hosted integration runtime in Azure Data Factory.
The current status of the integration runtime has the following configurations:
* Status: Running
* Type: Self-Hosted
* Version: 4.4.7292.1
* Running / Registered Node(s): 1/1
* High Availability Enabled: False
* Linked Count: 0
* Queue Length: 0
* Average Queue Duration. 0.00s
The integration runtime has the following node details:
* Name: X-M
* Status: Running
* Version: 4.4.7292.1
* Available Memory: 7697MB
* CPU Utilization: 6%
* Network (In/Out): 1.21KBps/0.83KBps
* Concurrent Jobs (Running/Limit): 2/14
* Role: Dispatcher/Worker
* Credential Status: In Sync
Use the drop-down menus to select the answer choice that completes each statement based on the information presented.
NOTE: Each correct selection is worth one point.
正解:
解説:
Explanation
Box 1: fail until the node comes back online
We see: High Availability Enabled: False
Note: Higher availability of the self-hosted integration runtime so that it's no longer the single point of failure in your big data solution or cloud data integration with Data Factory.
Box 2: lowered
We see:
Concurrent Jobs (Running/Limit): 2/14
CPU Utilization: 6%
Note: When the processor and available RAM aren't well utilized, but the execution of concurrent jobs reaches a node's limits, scale up by increasing the number of concurrent jobs that a node can run Reference:
https://docs.microsoft.com/en-us/azure/data-factory/create-self-hosted-integration-runtime
質問 # 141
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.
正解:
解説:
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
質問 # 142
......
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