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NEW QUESTION: 1
マルチクラスの画像分類の深層学習モデルを作成します。
PyTorchバージョン1.2を使用してモデルをトレーニングします。
モデルがデプロイされるときに、推論環境に対して正しいバージョンのPyTorchを識別できることを確認する必要があります。
あなたは何をするべきか？
A. .ptファイル拡張子とデフォルトのバージョンプロパティを使用してモデルを登録します。
B. モデルをローカルに.ptファイルとして保存し、モデルをローカルWebサービスとしてデプロイします。
C. model_frameworkおよびmodel_framework_versionプロパティを指定して、モデルを登録します。
D. 既定のAzure Machine Learning conda環境を使用するように構成されているコンピューターにモデルをデプロイします。
Answer: C
Explanation:
framework_version: The PyTorch version to be used for executing training code.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn.pytorch?view=azure-ml-py

NEW QUESTION: 2
An organization has contracted with a third party to implement and configure a new accounting application. Once the application is implemented, in-house staff will provide all application support and maintenance. Which of the following is MOST important to the success of this initiative?
A. Ensuring the third party completed testing
B. Conducting a post-implementation review
C. Documenting an implementation plan
D. Establishing a knowledge transfer plan
Answer: D

NEW QUESTION: 3
Note: This question is part of a series of questions that present the same scenario. Each question
in the series contains a unique solution that might meet the stated goals. Some question sets
might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these
questions will not appear in the review screen.
You are designing an Azure SQL Database that will use elastic pools. You plan to store data about
customers in a table. Each record uses a value for CustomerID.
You need to recommend a strategy to partition data based on values in CustomerID.
Proposed Solution: Separate data into customer regions by using horizontal partitioning.
Does the solution meet the goal?
A. No
B. Yes
Answer: A
Explanation:
Explanation/Reference:
Explanation:
We should use Horizontal Partitioning through Sharding, not divide through regions.
Note: Horizontal Partitioning - Sharding: Data is partitioned horizontally to distribute rows across a scaled
out data tier. With this approach, the schema is identical on all participating databases. This approach is
also called "sharding". Sharding can be performed and managed using (1) the elastic database tools
libraries or (2) self-sharding. An elastic query is used to query or compile reports across many shards.
References:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-elastic-query-overview