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NEW QUESTION: 1

A. ResultSuggestionChosen
B. QueryChanged
C. QuerySubmitted
D. SuggestionsRequested
Answer: D

NEW QUESTION: 2
Refer to the graphic.

R1 is unable to establish an OSPF neighbor relationship with R3. What are possible reasons for this problem? (Choose two.)
A. All of the routers need to be configured for backbone Area 1.
B. R1 and R3 are configured in different areas.
C. R1 and R2 are the DR and BDR, so OSPF will not establish neighbor adjacency with R3.
D. The hello and dead interval timers are not set to the same values on R1 and R3.
E. A static route has been configured from R1 to R3 and prevents the neighbor adjacency from being established.
F. EIGRP is also configured on these routers with a lower administrative distance.
Answer: B,D
Explanation:
This question is to examine the conditions for OSPF to create neighborhood.
So as to make the two routers become neighbors, each router must be matched with the following items:
1 . The area ID and its types;
2 . Hello and failure time interval timer;
3 . OSPF Password (Optional).

NEW QUESTION: 3
Match the types of computer vision to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

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: 4
Of the following, which is NOT considered justification to approve overtime? Overtime is not justified\
A. for unpredictable fluctuations in volume.
B. for employees who want to supplement their wages.
C. when there is unusually high absenteeism.
D. when there is a temporary change of work methods.
Answer: B