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NEW QUESTION: 1
Drag and Drop Question
You import data into Dynamics 365 for Customer Service by using the Import Data wizard.
Errors occur when you try to import the following data lines:

You need to identify the cause of the errors.
What error types have occurred? To answer, drag the appropriate error types to the correct data.
Each error type may be used once, more than once, or not at all.
You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

NEW QUESTION: 2
OSIモデルのレイヤー2で情報を転送するために使用されるユニットは、次のうちどれですか？
A. セッション
B. パケット
C. フレーム
D. ソケット
Answer: C

NEW QUESTION: 3
게임 애플리케이션은 user_id, user_name, user_score 및 user_rank 속성이 4 개있는 Amazon DynamoDB 테이블에 플레이어 점수를 저장합니다. 사용자는 자신의 이름 만 업데이트 할 수 있습니다. 사용자는 웹 ID 연합에 의해 인증됩니다.
dynamodb Putltem API 호출에 대한 역할에 연결된 정책에 어떤 조건 세트를 추가해야 합니까?
ㅏ)

비)

씨)

디)

A. 옵션 C
B. 옵션 D
C. 옵션 A
D. 옵션 B
Answer: C

NEW QUESTION: 4
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 have a Python script named train.py in a local folder named scripts. The script trains a regression model by using scikit-learn. The script includes code to load a training data file which is also located in the scripts folder.
You must run the script as an Azure ML experiment on a compute cluster named aml-compute.
You need to configure the run to ensure that the environment includes the required packages for model training. You have instantiated a variable named aml-compute that references the target compute cluster.
Solution: Run the following code:

Does the solution meet the goal?
A. No
B. Yes
Answer: A
Explanation:
Explanation
The scikit-learn estimator provides a simple way of launching a scikit-learn training job on a compute target. It is implemented through the SKLearn class, which can be used to support single-node CPU training.
Example:
from azureml.train.sklearn import SKLearn
}
estimator = SKLearn(source_directory=project_folder,
compute_target=compute_target,
entry_script='train_iris.py'
)
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-scikit-learn