File Info
| Exam | Developing SQL Data Models |
| Number | 70-768 |
| File Name | Microsoft.70-768.CertKiller.2019-01-24.23q.tqb |
| Size | 2 MB |
| Posted | Jan 24, 2019 |
| Download | Microsoft.70-768.CertKiller.2019-01-24.23q.tqb |
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Demo Questions
Question 1
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution. Determine whether the solution meets the stated goals.
You deploy a tabular data model to an instance of Microsoft SQL Server Analysis Services (SSAS). The model uses an in-memory cache to store and query data. The data set is already the same size as the available RAM on the server. Data volumes are likely to continue to increase rapidly.
Your data model contains multiple calculated tables.
The data model must begin processing each day at 2:00 and processing should be complete by 4:00 the same day. You observe that the data processing operation often does not complete before 7:00. This is adversely affecting team members.
You need to improve the performance.
Solution: Change the storage mode for the data model to DirectQuery.
Does the solution meet the goal?
- Yes
- No
Correct answer: A
Explanation:
By default, tabular models use an in-memory cache to store and query data. When tabular models query data residing in-memory, even complex queries can be incredibly fast. However, there are some limitations to using cached data. Namely, large data sets can exceed available memory, and data freshness requirements can be difficult if not impossible to achieve on a regular processing schedule. DirectQuery overcomes these limitations while also leveraging RDBMS features making query execution more efficient. With DirectQuery: +Data is up-to-date, and there is no extra management overhead of having to maintain a separate copy of the data (in the in-memory cache). Changes to the underlying source data can be immediately reflected in queries against the data model. Datasets can be larger than the memory capacity of an Analysis Services server. Etc. References:https://docs.microsoft.com/en-us/sql/analysis-services/tabular-models/directquery-mode-ssas-tabular By default, tabular models use an in-memory cache to store and query data. When tabular models query data residing in-memory, even complex queries can be incredibly fast. However, there are some limitations to using cached data. Namely, large data sets can exceed available memory, and data freshness requirements can be difficult if not impossible to achieve on a regular processing schedule.
DirectQuery overcomes these limitations while also leveraging RDBMS features making query execution more efficient.
With DirectQuery: +
Data is up-to-date, and there is no extra management overhead of having to maintain a separate copy of the data (in the in-memory cache). Changes to the underlying source data can be immediately reflected in queries against the data model.
Datasets can be larger than the memory capacity of an Analysis Services server.
Etc.
References:https://docs.microsoft.com/en-us/sql/analysis-services/tabular-models/directquery-mode-ssas-tabular
Question 2
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution. Determine whether the solution meets the stated goals.
You deploy a tabular data model to an instance of Microsoft SQL Server Analysis Services (SSAS). The model uses an in-memory cache to store and query data. The data set is already the same size as the available RAM on the server. Data volumes are likely to continue to increase rapidly.
Your data model contains multiple calculated tables.
The data model must begin processing each day at 2:00 and processing should be complete by 4:00 the same day. You observe that the data processing operation often does not complete before 7:00. This is adversely affecting team members.
You need to improve the performance.
Solution: Install solid-state disk drives to store the tabular data model.
Does the solution meet the goal?
- Yes
- No
Correct answer: B
Explanation:
By default, tabular models use an in-memory cache to store and query data. When tabular models query data residing in-memory, even complex queries can be incredibly fast. However, there are some limitations to using cached data. Namely, large data sets can exceed available memory, and data freshness requirements can be difficult if not impossible to achieve on a regular processing schedule. DirectQuery overcomes these limitations while also leveraging RDBMS features making query execution more efficient. With DirectQuery: +References:https://docs.microsoft.com/en-us/sql/analysis-services/tabular-models/directquery-mode-ssas-tabular By default, tabular models use an in-memory cache to store and query data. When tabular models query data residing in-memory, even complex queries can be incredibly fast. However, there are some limitations to using cached data. Namely, large data sets can exceed available memory, and data freshness requirements can be difficult if not impossible to achieve on a regular processing schedule.
DirectQuery overcomes these limitations while also leveraging RDBMS features making query execution more efficient.
With DirectQuery: +
References:https://docs.microsoft.com/en-us/sql/analysis-services/tabular-models/directquery-mode-ssas-tabular
Question 3
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution. Determine whether the solution meets the stated goals.
You deploy a tabular data model to an instance of Microsoft SQL Server Analysis Services (SSAS). The model uses an in-memory cache to store and query data. The data set is already the same size as the available RAM on the server. Data volumes are likely to continue to increase rapidly.
Your data model contains multiple calculated tables.
The data model must begin processing each day at 2:00 and processing should be complete by 4:00 the same day. You observe that the data processing operation often does not complete before 7:00. This is adversely affecting team members.
You need to improve the performance.
Solution: Enable Buffer Cache Extensions.
Does the solution meet the goal?
- Yes
- No
Correct answer: B
Explanation:
In this scenario we would need both Buffer Cache Extensions and SSD. The buffer pool extension provides the seamless integration of a nonvolatile random access memory (that is, solid-state drive) extension to the Database Engine buffer pool to significantly improve I/O throughput. References:https://docs.microsoft.com/en-us/sql/database-engine/configure-windows/buffer-pool-extension In this scenario we would need both Buffer Cache Extensions and SSD.
The buffer pool extension provides the seamless integration of a nonvolatile random access memory (that is, solid-state drive) extension to the Database Engine buffer pool to significantly improve I/O throughput.
References:https://docs.microsoft.com/en-us/sql/database-engine/configure-windows/buffer-pool-extension
