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NEW QUESTION: 1
Which Oracle Cloud Infrastructure (OCI) service can be used to protect sensitive and regulated data in OCI database services?
A. OCI Audit
B. OCI OS management
C. Oracle Data Safe
D. Oracle Data Guard
Answer: C
Explanation:
Explanation
Oracle Data Safe is a unified control center for your Oracle databases which helps you understand the sensitivity of your data, evaluate risks to data, mask sensitive data, implement and monitor security controls, assess user security, monitor user activity, and address data security compliance requirements.
Whether you're using an Autonomous Database or an Oracle DB system, Oracle Data Safe delivers essential data security capabilities as a service on Oracle Cloud Infrastructure.
Features of Oracle Data Safe:
Oracle Data Safe provides the following set of features for protecting sensitive and regulated data in Oracle Cloud databases, all in a single, easy-to-use management console:
1) Security Assessment helps you assess the security of your cloud database configurations. It analyzes database configurations, user accounts, and security controls, and then reports the findings with recommendations for remediation activities that follow best practices to reduce or mitigate risk.
2) User Assessment helps you assess the security of your database users and identify high risk users. It reviews information about your users in the data dictionary on your target databases, and calculates a risk score for each user. For example, it evaluates the user types, how users are authenticated, the password policies assigned to each user, and how long it has been since each user has changed their password. It also provides a direct link to audit records related to each user. With this information, you can then deploy appropriate security controls and policies.
3) Data Discovery helps you find sensitive data in your cloud databases. You tell Data Discovery what kind of sensitive data to search for, and it inspects the actual data in your database and its data dictionary, and then returns to you a list of sensitive columns. By default, Data Discovery can search for a wide variety of sensitive data pertaining to identification, biographic, IT, financial, healthcare, employment, and academic information.
4) Data Masking provides a way for you to mask sensitive data so that the data is safe for non-production purposes. For example, organizations often need to create copies of their production data to support development and test activities. Simply copying the production data exposes sensitive data to new users. To avoid a security risk, you can use Data Masking to replace the sensitive data with realistic, but fictitious data.
5) Activity Auditing lets you audit user activity on your databases so you can monitor database usage and be alerted of unusual database activities.
NEW QUESTION: 2
Azureサブスクリプションがあります。
あなたの会社には、SUSE Linux Enterprise Server（SLES）サーバーとSAPHANAで実行されるSAP環境があります。環境には、プライマリサイトとディザスタリカバリサイトがあります。ディザスタリカバリは、SAPHANAシステムレプリケーションに基づいています。 SAPERP環境は4TBであり、月に5％の成長が見込まれています。
同社は、99.99％の稼働時間サービスレベル契約（SLA）、4時間の最大回復時間目標（RTO）、および10分の回復ポイント目標（RPO）を持っています。
Azureへの移行を計画しています。
会社のSAPランドスケープを設計する必要があります。
どのオプションが会社の要件を満たしていますか？
A. Pacemakerフェンスエージェントを使用するASCS / ERSおよびSLESクラスタリング
AzureアベイラビリティーゾーンにデプロイされたSAPアプリケーションサーバー
データベースの高可用性とディザスタリカバリのためにSAPHANAシステムレプリケーションを使用するSAPHANA on Azure（ラージインスタンス）
B. Azure仮想マシンとSAPアプリケーションサーバー用のSLES
高可用性とディザスタリカバリのためにSAPHANAシステムレプリケーションを使用するSAPHANA on Azure（ラージインスタンス）
C. Azure可用性セットにデプロイされたSAPアプリケーションインスタンス
データベースの高可用性とディザスタリカバリのためにSAPHANAシステムレプリケーションを使用するSAPHANA on Azure（ラージインスタンス）
D. Azureフェンスエージェントを使用するASCS / ERSおよびSLESクラスタリング
Azure可用性セットにデプロイされたSAPアプリケーションサーバー
データベースの高可用性とディザスタリカバリのためにSAPHANAシステムレプリケーションを使用するSAPHANA on Azure（ラージインスタンス）
Answer: A
Explanation:
Explanation
With Availability Zones, Azure offers industry best 99.99% VM uptime SLA.
References:
https://docs.microsoft.com/en-us/azure/traffic-manager/traffic-manager-faqs
NEW QUESTION: 3
A Data Science team is designing a dataset repository where it will store a large amount of training data commonly used in its machine learning models. As Data Scientists may create an arbitrary number of new datasets every day the solution has to scale automatically and be cost-effective. Also, it must be possible to explore the data using SQL.
Which storage scheme is MOST adapted to this scenario?
A. Store datasets as tables in a multi-node Amazon Redshift cluster.
B. Store datasets as files in Amazon S3.
C. Store datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance.
D. Store datasets as global tables in Amazon DynamoDB.
Answer: B
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