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NEW QUESTION: 1
Azure Machine Learningサービスを使用して、training.dataという名前の表形式のデータセットを作成します。このデータセットをトレーニングスクリプトで使用する予定です。
次のコードを使用して、データセットを参照する変数を作成します。
training_ds = workspace.datasets.get（ "training_data"）
スクリプトを実行する推定器を定義します。
スクリプトがtraining.dataデータセットにアクセスできるようにするには、推定器の正しいプロパティを設定する必要があります。どのプロパティを設定する必要がありますか？
A）

B）

C）

D）

A. オプションD
B. オプションA
C. オプションB
D. オプションC
Answer: B
Explanation:
Example:
# Get the training dataset
diabetes_ds = ws.datasets.get("Diabetes Dataset")
# Create an estimator that uses the remote compute
hyper_estimator = SKLearn(source_directory=experiment_folder,
inputs=[diabetes_ds.as_named_input('diabetes')], # Pass the dataset as an input compute_target = cpu_cluster, conda_packages=['pandas','ipykernel','matplotlib'], pip_packages=['azureml-sdk','argparse','pyarrow'], entry_script='diabetes_training.py') Reference:
https://notebooks.azure.com/GraemeMalcolm/projects/azureml-primers/html/04%20-%20Optimizing%20Model%20Training.ipynb

NEW QUESTION: 2
Solutions Architect는 사용자가 Amazon S3에 사진을 업로드 할 때 Lambda를 사용하여 메타 데이터를 생성하는 새로운 기능을 구축하고 있습니다. 모든 메타 데이터는 색인화되어야 합니다.
Architect가이 메타 데이터를 저장하기 위해 사용해야 하는 AWS 서비스는 무엇입니까?
A. Amazon EFC
B. Amazon DynamoDB
C. 아마존 S3
D. 아마존 키네 시스
Answer: B
Explanation:
Explanation
https://aws.amazon.com/blogs/big-data/building-and-maintaining-an-amazon-s3-metadata-index-without-server

NEW QUESTION: 3
What does CaaS represent?
A. combining applications in a single access system
B. communication applications provided by an offsite location service provider
C. computer maintenance services provided by an external service provider
D. computing services provided by an offsite location service provider
Answer: B