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NEW QUESTION: 1
Which types of jobs can be sent to tape using a "Backup to Tape" job? (Choose two.)
A. Configuration Backup
B. Backup Copy
C. Backup
D. Replication
Answer: B,C
Explanation:
Explanation: References:
NEW QUESTION: 2
ソリューションアーキテクトは、セッションデータとJSONドキュメントを保存および取得する必要があるソリューションを設計しています。
ソリューションは、高可用性、強力な一貫性、およびデータの耐久性を提供する必要があります。これらの要件を満たすソリューションはどれですか。
A. Amazon DynamoDBテーブル
B. Amazon SQS
C. 暫定IOPSを使用したAmazon EBSボリューム
D. AmazonEC2インスタンスが破損した
Answer: A
NEW QUESTION: 3
You are customizing standard content types for an organization. For the tracking of a department, you must allow users to easily select or enter a value.
In some cases, users may refer to a single department by more than one name. Users must be able to select or type any name for a department but the document must be associated with only one department.
You need to create a column that supports the requirements.
What should you create?
A. a Choice site column that uses check boxes
B. a Choice site column that uses a drop-down menu.
C. a Choice listthat uses a drop-down menu
D. a Managed Metadata site column
Answer: B
Explanation:
Explanation/Reference:
Explanation:
References:
http://en.share-gate.com/blog/start-learn-sharepoint-advanced-column-types
NEW QUESTION: 4
A Machine Learning Specialist is developing a custom video recommendation model for an application The dataset used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket The Specialist wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance.
Which approach allows the Specialist to use all the data to train the model?
A. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to train the full dataset.
B. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to the instance Train on a small amount of the data to verify the training code and hyperparameters. Go back to Amazon SageMaker and tram using the full dataset.
C. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode
D. Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatible with Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode
Answer: B
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