CAS-005 Practice Questions

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NEW QUESTION: 1
Which of the following layer of an enterprise data flow architecture represents subset of information from the core Data Warehouse selected and organized to meet the needs of a particular business unit or business line?
A. Data Mart layer
B. Desktop Access Layer
C. Data preparation layer
D. Data access layer
Answer: A
Explanation:
Explanation/Reference:
Data Mart layer - Data mart represents subset of information from the core Data Warehouse selected and organized to meet the needs of a particular business unit or business line. Data mart can be relational databases or some form on-line analytical processing (OLAP) data structure.
For CISA exam you should know below information about business intelligence:
Business intelligence(BI) is a broad field of IT encompasses the collection and analysis of information to assist decision making and assess organizational performance. To deliver effective BI, organizations need to design and implement a data architecture. The complete data architecture consists of two components The enterprise data flow architecture (EDFA) A logical data architecture
Various layers/components of this data flow architecture are as follows:
Presentation/desktop access layer - This is where end users directly deal with information. This layer includes familiar desktop tools such as spreadsheets, direct querying tools, reporting and analysis suits offered by vendors such as Congas and business objects, and purpose built application such as balanced source cards and digital dashboards.
Data Source Layer - Enterprise information derives from number of sources:
Operational data - Data captured and maintained by an organization's existing systems, and usually held in system-specific database or flat files.
External Data - Data provided to an organization by external sources. This could include data such as customer demographic and market share information.
Nonoperational data - Information needed by end user that is not currently maintained in a computer accessible format.
Core data warehouse -This is where all the data of interest to an organization is captured and organized to assist reporting and analysis. DWs are normally instituted as large relational databases. A property constituted DW should support three basic form of an inquiry.
Drilling up and drilling down - Using dimension of interest to the business, it should be possible to aggregate data as well as drill down. Attributes available at the more granular levels of the warehouse can also be used to refine the analysis.
Drill across - Use common attributes to access a cross section of information in the warehouse such as sum sales across all product lines by customer and group of customers according to length of association with the company.
Historical Analysis - The warehouse should support this by holding historical, time variant data. An example of historical analysis would be to report monthly store sales and then repeat the analysis using only customer who were preexisting at the start of the year in order to separate the effective new customer from the ability to generate repeat business with existing customers.
Data Mart Layer- Data mart represents subset of information from the core DW selected and organized to meet the needs of a particular business unit or business line. Data mart can be relational databases or some form on-line analytical processing (OLAP) data structure.
Data Staging and quality layer -This layer is responsible for data copying, transformation into DW format and quality control. It is particularly important that only reliable data into core DW. This layer needs to be able to deal with problems periodically thrown by operational systems such as change to account number format and reuse of old accounts and customer numbers.
Data Access Layer -This layer operates to connect the data storage and quality layer with data stores in the data source layer and, in the process, avoiding the need to know to know exactly how these data stores are organized. Technology now permits SQL access to data even if it is not stored in a relational database.
Data Preparation layer -This layer is concerned with the assembly and preparation of data for loading into data marts. The usual practice is to per-calculate the values that are loaded into OLAP data repositories to increase access speed. Data mining is concern with exploring large volume of data to determine patterns and trends of information. Data mining often identifies patterns that are counterintuitive due to number and complexity of data relationships. Data quality needs to be very high to not corrupt the result.
Metadata repository layer - Metadata are data about data. The information held in metadata layer needs to extend beyond data structure names and formats to provide detail on business purpose and context. The metadata layer should be comprehensive in scope, covering data as they flow between the various layers, including documenting transformation and validation rules.
Warehouse Management Layer -The function of this layer is the scheduling of the tasks necessary to build and maintain the DW and populate data marts. This layer is also involved in administration of security.
Application messaging layer -This layer is concerned with transporting information between the various layers. In addition to business data, this layer encompasses generation, storage and targeted communication of control messages.
Internet/Intranet layer - This layer is concerned with basic data communication. Included here are browser based user interface and TCP/IP networking.
Various analysis models used by data architects/ analysis follows:
Activity or swim-lane diagram - De-construct business processes.
Entity relationship diagram -Depict data entities and how they relate. These data analysis methods obviously play an important part in developing an enterprise data model. However, it is also crucial that knowledgeable business operative are involved in the process. This way proper understanding can be obtained of the business purpose and context of the data. This also mitigates the risk of replication of suboptimal data configuration from existing systems and database into DW.
The following were incorrect answers:
Desktop access layer or presentation layer is where end users directly deal with information. This layer includes familiar desktop tools such as spreadsheets, direct querying tools, reporting and analysis suits offered by vendors such as Congas and business objects, and purpose built application such as balanced source cards and digital dashboards.
Data preparation layer -This layer is concerned with the assembly and preparation of data for loading into data marts. The usual practice is to per-calculate the values that are loaded into OLAP data repositories to increase access speed.
Data access layer - his layer operates to connect the data storage and quality layer with data stores in the data source layer and, in the process, avoiding the need to know to know exactly how these data stores are organized. Technology now permits SQL access to data even if it is not stored in a relational database.
The following reference(s) were/was used to create this question:
CISA review manual 2014 Page number 188

NEW QUESTION: 2
According to Requirement 3 of the Payment Card Industry's Data Security Standard (PCI DSS) there is a requirement to "protect stored cardholder data." Which of the following items cannot be stored by the merchant?
A. The Card Validation Code (CVV2)
B. Expiration Date
C. Cardholder Name
D. Primary Account Number
Answer: A
Explanation:
Requirement 3 of the Payment Card Industry's Data Security Standard (PCI DSS) is to "protect stored cardholder data." The public assumes merchants and financial institutions will protect data on payment cards to thwart theft and prevent unauthorized use.
But merchants should take note: Requirement 3 applies only if cardholder data is stored. Merchants who do not store any cardholder data automatically provide stronger protection by having eliminated a key target for data thieves.
For merchants who have a legitimate business reason to store cardholder data, it is important to understand what data elements PCI DSS allows them to store and what measures they must take to protect those data. To prevent unauthorized storage, only council certified PIN entry devices and payment applications may be used.
PCI DSS compliance is enforced by the major payment card brands who established the PCI DSS and the PCI Security Standards Council: American Express, Discover Financial Services, JCB International, MasterCard Worldwide and Visa Inc.
PCI DSS Requirement 3 It details technical guidelines for protecting stored cardholder data. Merchants should develop a data retention and storage policy that strictly limits storage amount and retention time to that which is required for business, legal, and/or regulatory purposes.
Sensitive authentication data must never be stored after authorization - even if this data is encrypted.
Never store full contents of any track from the card's magnetic stripe or chip (referred to as full track, track, track 1, track 2, or magnetic stripe data). If required for business purposes, the cardholder's name, PAN, expiration date, and service code may be stored as long as they are
rotected in accordance with PCI DSS requirements.
Never store the card-validation code (CVV) or value (three- or four-digit number printed on the front or back of a payment card used to validate card-not-present transactions).
Never store the personal identification number (PIN) or PIN Block. Be sure to mask PAN whenever it is displayed. The first six and last four digits are the maximum number of digits that may be displayed. This requirement does not apply to those authorized with a specific need to see the full PAN, nor does it supersede stricter requirements in place for displays of cardholder data such as in a point-of-sale receipt.
PCI Data Storage
[1] These data elements must be protected if stored in conjunction with the PAN. This protection should be per PCI DSS requirements for general protection of the cardholder data environment. Additionally, other legislation (e.g., related to consumer personal data protection, privacy, identity theft, or data security) may require specific protection of this data, or proper disclosure of a company's practices if consumer related personal data is being collected during the course of business. PCI DSS, however, does not apply if PANs are not stored, processed, or transmitted.
[2] Sensitive authentication data must not be stored after authorization (even if encrypted).
[3] Full track data from the magnetic stripe, magnetic stripe image on the chip, or elsewhere.
Technical Guidelines for Protecting Stored Payment Card Data At a minimum, PCI DSS requires PAN to be rendered unreadable anywhere it is stored - including portable digital media, backup media, and in logs. Software solutions for this requirement may include one of the following:
One-way hash functions based on strong cryptography - also called hashed index, which displays only index data that point to records in the database where sensitive data actually reside.
Truncation - removing a data segment, such as showing only the last four digits.
Index tokens and securely stored pads - encryption algorithm that combines sensitive plain text data with a random key or "pad" that works only once.
Strong cryptography - with associated key management processes and procedures. Refer to the PCI DSS and PA-DSS Glossary of Terms, Abbreviations and Acronyms for the definition of "strong cryptography."
Some cryptography solutions encrypt specific fields of information stored in a database; others encrypt a singular file or even the entire disk where data is stored. If full-disk encryption is used, logical access must be managed independently of native operating system access control mechanisms. Decryption keys must not be tied to user accounts. Encryption keys used for encryption of cardholder data must be protected against both disclosure and misuse. All key management processes and procedures for keys used for encryption of cardholder data must be fully documented and implemented. Strong Cryptography is define in the glossary of PCI DSS as: Cryptography based on industry-tested and accepted algorithms, along with strong key lengths and proper key-management practices. Cryptography is a method to protect data and includes both encryption (which is reversible) and hashing (which is not reversible, or "one way"). Examples of industry-tested and accepted standards and algorithms for encryption include AES (128 bits and higher), TDES (minimum double-length keys), RSA (1024 bits and higher), ECC (160 bits and higher), and ElGamal (1024 bits and higher).
See NIST Special Publication 800-57 (www.csrc.nist.gov/publications/) for more information on strong crypto.
The following answers are all incorrect: Primary Account Number Cardholder Name Expiration Date All of the items above can be stored according to the PCI Data Storage Guidelines. See graphic above.
The following reference(s) were/was used to create this question: https://www.pcisecuritystandards.org/pdfs/pci_fs_data_storage.pdf

NEW QUESTION: 3
Your customer's legal department has a new training course they would like to assign to a target group of
users. The target group of users is dynamic and changes frequently.
What do you use to automate the assignment of this new course?
Please choose the correct answer.
Response:
A. Direct Assignment from the User Record
B. User Needs Management
C. Assignment profiles
D. Direct Assignment from the item Record
Answer: C

NEW QUESTION: 4
Click the Exhibit button.

IBM 000-657 : Practice Test
An administrator at Widgetcomintends to use the standard multitier architecture configuration that ships with IBM TivoliNetcool/OMNIbus V7.4 (Netcool/OMNIbus) to deploy an aggregation pair of ObjectServers AGG_P (primary) andAGG_B (backup) with a failover bidirectional gateway connecting them:AGG_GATE.
Two physical computer systems will be used to deploy this single tier deployment of Netcool/OMNIbus.What is theminimum memory that should be provisioned for the Netcool/OMNIbus components running on the primary aggregationObjectServer host?
A. 4 GB
B. 2 GB
C. 6 GB
D. 1 GB
Answer: A
Explanation: