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NEW QUESTION: 1
アプリケーションを開発しています。アプリケーションソースには複数のブランチがあります。
実験に使用するブランチにいくつかの変更を加えます。
実験ブランチに加えられた変更をキャプチャし、Gitリポジトリの履歴を上書きするには、メインブランチを更新する必要があります。
どのGitオプションを使用する必要がありますか？
A. フェッチ
B. マージ
C. プッシュ
D. リベース
Answer: B
Explanation:
Create pull requests to review and merge code in a Git project. Pull requests let your team review code and give feedback on changes before merging it into the master branch.
Reference:
https://docs.microsoft.com/en-us/azure/devops/repos/git/pull-requests

NEW QUESTION: 2

A. Option D
B. Option B
C. Option A
D. Option C
Answer: D
Explanation:
Explanation
Answer - C
The following are the components of Clastic Beanstalk
1) Application - An Clastic Beanstalk application is a logical collection of Clastic Beanstalk components, including environments, versions, and environment configurations. In Clastic Beanstalk an application is conceptually similar to a folder
2) Application version - In Clastic Beanstalk, an application version refers to a specific, labeled iteration of deployable code for a web application
3) environment - An environment is a version that is deployed onto AWS resources. Cach environment runs only a single application version at a time, however you can run the same version or different versions in many environments at the same time.
4) environment Configuration - An environment configuration identifies a collection of parameters and settings that define how an environment and its associated resources behave.
5) Configuration Template - A configuration template is a starting point for creating unique environment configurations. For more information on the components of Clastic beanstalk please refer to the below link
http://docs.aws.amazon.com/elasticbeanstalk/latest/dg/concepts.components.html

NEW QUESTION: 3
You are deploying a new storage system for your mobile application, which is a media streaming service.
You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity 'Movie'the property 'actors'and the property 'tags' have multiple values but the property 'date released' does not. A typical query would ask for all movies with actor=<actorname>ordered by date_releasedor all movies with tag=Comedyordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?


C: Set the following in your entity options: exclude_from_indexes = 'actors, tags' D: Set the following in your entity options: exclude_from_indexes = 'date_published'
A. Option D
B. Option C
C. Option B.
D. Option A
Answer: D

NEW QUESTION: 4
How can you get a neural network to learn about relationships between categories in a categorical feature?
A. Create an embedding column
B. Create a one-hot column
C. Create a multi-hot column
D. Create a hash bucket
Answer: A
Explanation:
There are two problems with one-hot encoding. First, it has high dimensionality, meaning that instead of having just one value, like a continuous feature, it has many values, or dimensions. This makes computation more time-consuming, especially if a feature has a very large number of categories. The second problem is that it doesn't encode any relationships between the categories. They are completely independent from each other, so the network has no way of knowing which ones are similar to each other.
Both of these problems can be solved by representing a categorical feature with an embedding column. The idea is that each category has a smaller vector with, let's say, 5 values in it.
But unlike a one-hot vector, the values are not usually 0. The values are weights, similar to the weights that are used for basic features in a neural network. The difference is that each category has a set of weights (5 of them in this case).
You can think of each value in the embedding vector as a feature of the category. So, if two categories are very similar to each other, then their embedding vectors should be very similar too.
Reference: https://cloudacademy.com/google/introduction-to-google-cloud-machine-learning-engine-course/a-wide-and-deep-model.html