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NEW QUESTION: 1
Which is a key aspect of composition?
A. Using inheritance
B. Method delegation
C. Implementing the composite interface
D. Creating abstract classes
In the composition approach, the subclass becomes the "front-end class," and the superclass becomes the "back-end class." With inheritance, a subclass automatically inherits an implemenation of any non-private superclass method that it doesn't override. With composition, by contrast, the front-end class must explicitly invoke a corresponding method in the back-end class from its own implementation of the method. This explicit call is sometimes called "forwarding" or "delegating" the method invocation to the back-end object.
Note:Composition means the same as:
*is part of
Note2:As you progress in an object-oriented design, you will likely encounter objects in the problem domain that contain other objects. In this situation you will be drawn to modeling a similar arrangement in the design of your solution. In an object-oriented design of a Java program, the way in which you model objects that contain other objects is with composition, the act of composing a class out of references to other objects. With composition, references to the constituent objects become fields of the containing object. To use composition in Java, you use instance variables of one object to hold references to other objects.
NEW QUESTION: 2
A. Option B
B. Option D
C. Option A
D. Option C
await sourceStream.WriteAsync(encodedText, 0, encodedText.Length);
The following example has the statement await sourceStream.WriteAsync(encodedText, 0, encodedText.Length);, which is a contraction of the following two statements:
Task theTask = sourceStream.WriteAsync(encodedText, 0, encodedText.Length); await theTask; Example: The following example writes text to a file. At each await statement, the method immediately exits.
When the file I/O is complete, the method resumes at the statement that follows the await statement. Note that the async modifier is in the definition of methods that use the await statement.
public async void ProcessWrite()
string filePath = @"temp2.txt";
string text = "Hello World\r\n";
await WriteTextAsync(filePath, text);
private async Task WriteTextAsync(string filePath, string text)
byte encodedText = Encoding.Unicode.GetBytes(text);
using (FileStream sourceStream = new FileStream(filePath,
FileMode.Append, FileAccess.Write, FileShare.None,
bufferSize: 4096, useAsync: true))
await sourceStream.WriteAsync(encodedText, 0, encodedText.Length);
Reference: Using Async for File Access (C# and Visual Basic)
NEW QUESTION: 3
あなたはContosoEntertainment System USA（USMF）の機能コンサルタントです。
Navigate to General ledger > Ledger setup > Date intervals.
In the Date Interval Code field, type a code such as CurrMth PY.
In the Description field, type a description such as Current Month Previous Year.
In the Interval Start section, select Month in the From date period type field.
In the From date start/end, select Start.
In the From date +/- field, type -1 for -1 year (previous year).
In the From date adjustment unit field, select Year.
In the Interval End section, select Month in the From date period type field.
In the From date start/end, select End.
Click Save to save the date interval.
NEW QUESTION: 4
A company is using a fleet of Amazon EC2 instances to ingest data from on-premises data sources. The data is in JSON format and ingestion rates can be as high as 1 MB/s. When an EC2 instance is rebooted, the data in-flight is lost. The company's data science team wants to query ingested data in near-real time.
Which solution provides near-real-time data querying that is scalable with minimal data loss?
A. Store ingested data in an Amazon Elastic Block Store (Amazon EBS) volume. Publish data to Amazon ElastiCache for Redis. Subscribe to the Redis channel to query the data.
B. Store ingested data in an EC2 instance store Publish data to Amazon Kinesis Data Firehose with Amazon S3 as the destination. Use Amazon Athena to query the data.
C. Publish data to Amazon Kinesis Data firehose with Amazon Redshift as the destination. Use Amazon Redshift to query the data.
D. Publish data to Amazon Kinesis Data Streams. Use Kinesis Data Analytics to query the data.