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NEW QUESTION: 1
You manage a bot in a serverless architecture.
The bot provides custom responses to questions based upon the identity of the user.
The bot must meet the following requirements:
* Identify the user by face.
* Provide text-to-speech reading of questions to the user.
* Analyze the text of the user's responses for patterns.
What should you recommend? To answer, drag the appropriate solution to the correct scenario. Each solution may be used once, more than once, or not at all. You may need to drag the split bar between panes to scroll or view content.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Note: With Azure, built-in intelligence is within the reach of all app developers. Enable your serverless code or logic to use Machine Learning and Cognitive Services.
Box 1, Identify users by face: Azure Cognitive Services
Microsoft Face API, a cloud-based service that provides the most advanced face algorithms. Face API has two main functions: face detection with attributes and face recognition.
Box 2, Provide text-to-speech services: Azure Cognitive Services
Infuse your apps, websites and bots with intelligent algorithms to see, hear, speak, understand and interpret your user needs through natural methods of communication.
Example:
Speech: Convert spoken audio into text, use voice for verification, or add speaker recognition to your app.
Box 3, Read questions to users: Azure Functions
Azure Functions is a serverless solution.

NEW QUESTION: 2
Table metadata in Hive is:
A. Stored in the Metastore.
B. Stored as metadata on the NameNode.
C. Stored along with the data in HDFS.
D. Stored in ZooKeeper.
Answer: A
Explanation:
By default, hive use an embedded Derby database to store metadata information.
The metastore is the "glue" between Hive and HDFS. It tells Hive where your data files live in
HDFS, what type of data they contain, what tables they belong to, etc.
The Metastore is an application that runs on an RDBMS and uses an open source ORM layer
called DataNucleus, to convert object representations into a relational schema and vice versa.
They chose this approach as opposed to storing this information in hdfs as they need the
Metastore to be very low latency. The DataNucleus layer allows them to plugin many different
RDBMS technologies.
Note:
*By default, Hive stores metadata in an embedded Apache Derby database, and other client/server databases like MySQL can optionally be used.
*features of Hive include:
Metadata storage in an RDBMS, significantly reducing the time to perform semantic checks during
query execution.
Reference: Store Hive Metadata into RDBMS

NEW QUESTION: 3

Answer:
Explanation:

Explanation