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NEW QUESTION: 1
In which two situations is web authentication appropriate?(Choose two)
A. When 802.1x authentication is required.
B. When a fallback authentication method is necessary.
C. When WEP encryption must be deployed on a large scale
D. When secure connections to the network are unnecessary.
E. When devices outside the control of the organization's IT department are permitted to connect to the network.
Answer: A,B

NEW QUESTION: 2
次のうちどれが危険にさらされたLinuxシステムの法医学分析に使用されるでしょうか？
（3を選択）
A. ログファイルで不正なIPからのログインを確認します。
B. vmstatを使って過剰なディスクI / Oを探します。
C. / etc / shadow内の暗号化されていないパスワードを調べます。
D. gpgを使って危険にさらされたデータファイルを暗号化します。
E. システムバイナリのMD5チェックサムを確認してください。
F. 将来のタイムスタンプを持つファイルを判別するためにlsofを使用してください。
G. 妥協の頃に変更されたファイルのタイムスタンプを確認します。
H. / proc / kmemで断片化したメモリセグメントを確認します。
Answer: A,E,G
Explanation:
The MD5 checksum of the system binaries will allow you to carry out a forensic analysis of the compromised Linux system. Together with the log files of logins into the compromised system from unauthorized IPs and the timestamps for those files that were modified around the time that the compromise occurred will serve as useful forensic tools.
Incorrect Answers:
B: Checking for fragmented memory segments' is not a forensic analysis tool to be used in this case.
C: The ``/etc/shadow'', contains encrypted password as well as other information such as account or password expiration values, etc. The /etc/shadow file is readable only by the root account. This is a useful tool for Linux passwords and shadow file formats and is in essence used to keep user account information.
E: Isof is used on Linux as a future timestamp tool and not a forensic analysis tool.
F: Gpg is an encryption tool that works on Mac OS X.
H: vmstat reports information about processes, memory, paging, block IO, traps, and cpu activity. The first report produced gives averages since the last reboot. Additional reports give information on a sampling period of length delay. The process and memory reports are instantaneous in either case. This is more of an administrator tool.
References:
Gregg, Michael, and Billy Haines, CASP CompTIA Advanced Security Practitioner Study Guide, John Wiley & Sons, Indianapolis, 2012, p. 387
https://en.wikipedia.org/wiki/List_of_digital_forensics_tools

NEW QUESTION: 3
You are developing a solution using a Lambda architecture on Microsoft Azure.
The data at test layer must meet the following requirements:
Data storage:
* Serve as a repository (or high volumes of large files in various formats.
* Implement optimized storage for big data analytics workloads.
* Ensure that data can be organized using a hierarchical structure.
Batch processing:
* Use a managed solution for in-memory computation processing.
* Natively support Scala, Python, and R programming languages.
* Provide the ability to resize and terminate the cluster automatically.
Analytical data store:
* Support parallel processing.
* Use columnar storage.
* Support SQL-based languages.
You need to identify the correct technologies to build the Lambda architecture.
Which technologies should you use? To answer, select the appropriate options in the answer area
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
Data storage: Azure Data Lake Store
A key mechanism that allows Azure Data Lake Storage Gen2 to provide file system performance at object storage scale and prices is the addition of a hierarchical namespace. This allows the collection of objects/files within an account to be organized into a hierarchy of directories and nested subdirectories in the same way that the file system on your computer is organized. With the hierarchical namespace enabled, a storage account becomes capable of providing the scalability and cost-effectiveness of object storage, with file system semantics that are familiar to analytics engines and frameworks.
Batch processing: HD Insight Spark
Aparch Spark is an open-source, parallel-processing framework that supports in-memory processing to boost the performance of big-data analysis applications.
HDInsight is a managed Hadoop service. Use it deploy and manage Hadoop clusters in Azure. For batch processing, you can use Spark, Hive, Hive LLAP, MapReduce.
Languages: R, Python, Java, Scala, SQL
Analytic data store: SQL Data Warehouse
SQL Data Warehouse is a cloud-based Enterprise Data Warehouse (EDW) that uses Massively Parallel Processing (MPP).
SQL Data Warehouse stores data into relational tables with columnar storage.
References:
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-namespace
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/batch-processing
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-overview-what-is