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NEW QUESTION: 1
法医学会計データを共有したい企業は、外部監査人とAmazon RDS DBインスタンスに保存されます。監査人には独自のAWSアカウントがあり、データベースの独自のコピーが必要です。
会社はどのようにデータベースを監査人と安全に共有する必要がありますか？
A. データベースの暗号化されたスナップショットを作成し、スナップショットを共有して、AWS Key Management Service（AWS KMS）暗号化キーへのアクセスを許可します。
B. データベースのリードレプリカを作成し、IAM標準データベース認証を構成して監査人にアクセスを許可します。
C. データベースの内容をテキストファイルにエクスポートし、ファイルをAmazon S3に保存し、そのバケットへのアクセス権を持つ監査者用の新しいIAMユーザーを作成します。
D. データベースのスナップショットをAmazon S3にコピーし、IAMロールを監査人に割り当てて、そのバケット内のオブジェクトへのアクセスを許可します。
Answer: C

NEW QUESTION: 2
When designing an EMC RecoverPoint environment, what must be taken into account when a site consists of multiple VMAX splitters or multiple RecoverPoint clusters?
A. When the VMAX splitter/RecoverPoint cluster is connected through Fibre Channel only two gatekeepers must be used.
B. A total of eight gatekeepers are required for each VMAX splitter/RecoverPoint cluster combination.
C. All gatekeepers must be connected to each VMAX splitter/RecoverPoint cluster combination with fixed paths.
D. A total of 16 gatekeepers are required for each VMAX splitter/RecoverPoint cluster combination.
Answer: D

NEW QUESTION: 3
A company has a large on-premises Apache Hadoop cluster with a 20 PB HDFS database. The cluster is growing every quarter by roughly 200 instances and 1 PB. The company's goals are to enable resiliency for its Hadoop data, limit the impact of losing cluster nodes, and significantly reduce costs. The current cluster runs
24/7 and supports a variety of analysis workloads, including interactive queries and batch processing.
Which solution would meet these requirements with the LEAST expense and down time?
A. Use AWS Snowmobile to migrate the existing cluster data to Amazon S3. Create a persistent Amazon EMR cluster of similar size and configuration to the current cluster. Store the data on EMRFS.
Minimize costs by using Reserved Instances. As the workload grows each quarter, purchase additional Reserved Instances and add to the cluster.
B. Use AWS Snowmobile to migrate the existing cluster data to Amazon S3. Create a persistent Amazon EMR cluster initially sized to handle the interactive workload based on historical data from the on-premises cluster. Store the data on EMRFS. Minimize costs using Reserved Instances for master and core nodes and Spot Instances for task nodes, and auto scale task nodes based on Amazon CloudWatch metrics. Create job-specific, optimized clusters for batch workloads that are similarly optimized.
C. Use AWS Snowball to migrate the existing cluster data to Amazon S3. Create a persistent Amazon EMR cluster initially sized to handle the interactive workloads based on historical data from the on-premises cluster. Store the on EMRFS. Minimize costs using Reserved Instances for master and core nodes and Spot Instances for task nodes, and auto scale task nodes based on Amazon CloudWatch metrics. Create job-specific, optimized clusters for batch workloads that are similarly optimized.
D. Use AWS Direct Connect to migrate the existing cluster data to Amazon S3. Create a persistent Amazon EMR cluster initially sized to handle the interactive workload based on historical data from the on-premises cluster. Store the data on EMRFS. Minimize costs using Reserved Instances for master and core nodes and Spot Instances for task nodes, and auto scale task nodes based on Amazon CloudWatch metrics. Create job-specific, optimized clusters for batch workloads that are similarly optimized.
Answer: B
Explanation:
Explanation
Q: How should I choose between Snowmobile and Snowball?
To migrate large datasets of 10PB or more in a single location, you should use Snowmobile. For datasets less than 10PB or distributed in multiple locations, you should use Snowball. In addition, you should evaluate the amount of available bandwidth in your network backbone. If you have a high speed backbone with hundreds of Gb/s of spare throughput, then you can use Snowmobile to migrate the large datasets all at once. If you have limited bandwidth on your backbone, you should consider using multiple Snowballs to migrate the data incrementally.