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NEW QUESTION: 1
Which of these options is a variable used to determine which support delivery methods are most appropriate?
A. The Service Desks KPIs
B. The stipulations of the SLA
C. The complexity of the Incidents or Service Requests
D. The effectiveness of Knowledge Management
Answer: A

NEW QUESTION: 2
コンピュータビジョンの種類を適切なシナリオに一致させます。
答えるには、適切なワークロードタイプを左側の列から右側のシナリオにドラッグします。各ワークロードタイプは、1回使用することも、複数回使用することも、まったく使用しないこともできます。
注：正しい選択はそれぞれ1ポイントの価値があります。

Answer:
Explanation:

Explanation
Box 1: Facial recognition
Face detection that perceives faces and attributes in an image; person identification that matches an individual in your private repository of up to 1 million people; perceived emotion recognition that detects a range of facial expressions like happiness, contempt, neutrality, and fear; and recognition and grouping of similar faces in images.
Box 2: OCR
Box 3: Objection detection
Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image.
The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like
"indoor", which can't be localized with bounding boxes.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/face/
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection

NEW QUESTION: 3
What must be included when database statistics are generated?
A. Views
B. Procedures
C. Indexes C Packages
Answer: C

NEW QUESTION: 4
You are designing a data processing solution that will run as a Spark job on an HDInsight cluster. The solution will be used to provide near real-time information about online ordering for a retailer.
The solution must include a page on the company intranet that displays summary information.
The summary information page must meet the following requirements:
* Display a summary of sales to date grouped by product categories, price range, and review scope.
* Display sales summary information including total sales, sales as compared to one day ago and sales as compared to one year ago.
* Reflect information for new orders as quickly as possible.
You need to recommend a design for the solution.
What should you recommend? To answer, select the appropriate configuration in the answer area.

Answer:
Explanation:

Explanation

Box 1: DataFrame
DataFrames
Best choice in most situations.
Provides query optimization through Catalyst.
Whole-stage code generation.
Direct memory access.
Low garbage collection (GC) overhead.
Not as developer-friendly as DataSets, as there are no compile-time checks or domain object programming.
Box 2: parquet
The best format for performance is parquet with snappy compression, which is the default in Spark 2.x.
Parquet stores data in columnar format, and is highly optimized in Spark.