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NEW QUESTION: 1
You have an Azure Stream Analytics query. The query returns a result set that contains 10,000 distinct values for a column named clusterID.
You monitor the Stream Analytics job and discover high latency.
You need to reduce the latency.
Which two actions should you perform? Each correct answer presents a complete solution.
注：それぞれの正しい選択には1ポイントの価値があります。
A. Increase the number of streaming units.
B. Convert the query to a reference query.
C. Add a pass-through query.
D. Scale out the query by using PARTITION BY.
E. Add a temporal analytic function.
Answer: A,D
Explanation:
Explanation
C: Scaling a Stream Analytics job takes advantage of partitions in the input or output. Partitioning lets you divide data into subsets based on a partition key. A process that consumes the data (such as a Streaming Analytics job) can consume and write different partitions in parallel, which increases throughput.
E: Streaming Units (SUs) represents the computing resources that are allocated to execute a Stream Analytics job. The higher the number of SUs, the more CPU and memory resources are allocated for your job. This capacity lets you focus on the query logic and abstracts the need to manage the hardware to run your Stream Analytics job in a timely manner.
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-parallelization
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-streaming-unit-consumption
NEW QUESTION: 2
What do you use to define a logical grouping of deployable that function as a single unit and contain
definitions of automations and target environments?
A. Resource
B. Application
C. Resource template
D. Component template
Answer: A
Explanation:
Component-type resources represent the components that are deployed to target environments.
A resource is a logical deployment target that typically resolves to an agent and a user-defined construct
that is based on the architectural model of IBM UrbanCode Deploy.
If a resource is part of a hierarchy, it delegates its automation, if any, up the chain until it finds an agent.
In IBM UrbanCode Deploy, deployable items are combined into logical groupings called components.
NEW QUESTION: 3
You are developing an ETL process to cleanse and consolidate incoming data. The ETL process will use a reference table to identify which data must be cleansed in the target table. The server that hosts the table restarts daily.
You need to minimize the amount of time it takes to execute the query and the amount of time it takes to populate the reference table.
What should you do?
A. Convert the reference table to a memory-optimized table. Set the DURABILITY option to SCHEMA_AND_DATA.
B. Create a native compiled stored procedure to implement the ETL process for both tables.
C. Convert the reference table to a memory-optimized table. Set the DURABILITY option to SCHEMA_ONLY.
D. Convert the target table to a memory-optimized table. Create a natively compiled stored procedure to cleanse and consolidate the data.
Answer: C
Explanation:
Explanation/Reference:
Explanation:
If you use temporary tables, table variables, or table-valued parameters, consider conversions of them to leverage memory-optimized tables and table variables to improve performance.
In-Memory OLTP provides the following objects that can be used for memory-optimizing temp tables and table variables:
Memory-optimized tables
Durability = SCHEMA_ONLY
Memory-optimized table variables
Must be declared in two steps (rather than inline):
CREATE TYPE my_type AS TABLE ...; , then
DECLARE @mytablevariable my_type;.
References: https://docs.microsoft.com/en-us/sql/relational-databases/in-memory-oltp/faster-temp-table- and-table-variable-by-using-memory-optimization?view=sql-server-2017
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