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NEW QUESTION: 1
Which of the following is true for Single loss expectancy (SLE), Annual rate of occurrence (ARO), and Annual loss expectancy (ALE)?
A. ARO= SLE/ALE
B. ARO= ALE*SLE
C. Explanation:
A quantitative risk assessment quantifies risk in terms of numbers such as dollar values. This involves gathering data and then entering it into standard formulas. The results can help in identifying the priority of risks. These results are also used to determine the effectiveness of controls. Some of the terms associated with quantitative risk assessments are : Single loss expectancy (SLE)-It refers to the total loss expected from a single incident. This incident can occur when vulnerability is being exploited by threat. The loss is expressed as a dollar value such as $1,000. It includes the value of data, software, and hardware. SLE = Asset value * Exposure factor Annual rate of occurrence (ARO)-It refers to the number of times expected for an incident to occur in a year. If an incident occurred twice a month in the past year, the ARO is 24. Assuming nothing changes, it is likely that it will occur 24 times next year. Annual loss expectancy (ALE)-It is the expected loss for a year. ALE is calculated by multiplying SLE with ARO. Because SLE is a given in a dollar value, ALE is also given in a dollar value. For example, if the SLE is $1,000 and the ARO is 24, the ALE is $24,000. ALE = SLE * ARO Safeguard value-This is the cost of a control. Controls are used to mitigate risk. For example, antivirus software of an average cost of $50 for each computer. If there are 50 computers, the safeguard value is $2,500.
D. ALE= ARO*SLE
E. ALE= ARO/SLE
Answer: C,D
Explanation:
A, and B are incorrect. These are wrong formulas and are not used in quantitative risk assessment.

NEW QUESTION: 2
A university is building a storage system to support a high-performance computing environment (HPC).
Which IBM product is designed for this kind of workload?
A. IBM DS8886
B. IBM Spectrum Accelerate
C. IBM FlashSystem A9000R
D. IBM Spectrum Scale
Answer: D

NEW QUESTION: 3
オンライン小売業者がAmazon DynamoDBを使用して、顧客トランザクションに関連するデータを保存しています。テーブル内のアイテムには、トランザクションを説明するいくつかの文字列属性と、ショッピングカートやトランザクションに対応するその他の詳細を含むJSON属性が含まれています。平均アイテムサイズは最大250 KBで、そのほとんどはJSON属性に関連付けられています。平均では、1か月あたり最大3GBのデータが生成されます。
顧客はテーブルにアクセスして取引履歴を表示し、必要に応じて取引の詳細を確認します。テーブルに対するクエリの90％は、トランザクション履歴ビューを作成するときに実行されます。
トランザクションの詳細を取得する10％。テーブルはCustomerIDでパーティション分割され、トランザクションデータでソートされます。
クライアントはテーブルにプロビジョニングされた非常に高い読み取り容量を持ち、非常に均一な使用率を経験しますが、他のNoSQLソリューションと比較したAmazon DynamoDBのコストについて不平を言っています。
品質を低下させずに、クライアントの読み取りクエリに関連するコストを削減する戦略はどれですか？
A. 日付でソートされたLSIを作成し、JSON属性をインデックスに投影し、サマリーデータのプライマリテーブルとJSON詳細のLSIをクエリします
B. 最終的に一貫した読み取りを使用するようにすべてのデータベース呼び出しを変更し、トランザクション履歴が1秒古くなる可能性があることをお客様に通知します。
C. テーブルを垂直方向にパーティション化し、基本属性をプライマリテーブルに格納し、JSONデータを含むセカンダリテーブルへの外部キー参照を作成します。サマリーデータのプライマリテーブルとJSONの詳細のセカンダリテーブルをクエリします。
D. TransactionIDでパーティション化するプライマリテーブルを変更し、顧客でパーティション化され、日付でソートされたGSIを作成し、小さな属性をGSIに投影し、GSIにサマリーデータとJSON詳細のプライマリテーブルを照会します。
Answer: C

NEW QUESTION: 4
You are designing storage for very large text files for a data pipeline on Google Cloud. You want to support ANSI SQL queries. You also want to support compression and parallel load from the input locations using Google recommended practices. What should you do?
A. Compress text files to gzip using the Grid Computing Tools. Use Cloud Storage, and then import into Cloud Bigtable for query.
B. Transform text files to compressed Avro using Cloud Dataflow. Use BigQuery for storage and query.
C. Transform text files to compressed Avro using Cloud Dataflow. Use Cloud Storage and BigQuery permanent linked tables for query.
D. Compress text files to gzip using the Grid Computing Tools. Use BigQuery for storage and query.
Answer: A