Oracle Others Oracle Certifications 1Z0-515 Questions & Answers

• Question 21:

  Data Guard compresses data:

  A. Always

  B. When using logical standby

  C. When using physical standby

  D. When catching up after a network failure

  Correct Answer: C

  Explanation:

  A Physical standby database replicates the exact contents of its primary database across the Oracle Net

  network layer. While the physical storage locations can be different, the data in the database will be

  exactly the same as the primary database.

  Incorrect answer:

  A, B: Logical standby databases convert the redo generated at the Primary database into data and SQL

  and then re-apply those SQL transactions on the Logical standby, thus physical structures and

  organization will be different from the Primary database. Users can read from logical standby databases

  while the changes are being applied and, if the GUARD is set to STANDBY (ALTER DATABASE GUARD

  STANDBY;), write to tables in the Logical standby database that are not being maintained by SQL Apply.

  Unfortunately there are a number of unsupported objects (ie: tables or sequences owned by SYS, tables

  that use table compression, tables that underlie a materialized view or Global temporary tables (GTTs))

  and unsupported data types (ie: Datatypes BFILE, ROWID, and UROWID, user-defined TYPEs,

  Multimedia data types like Oracle Spatial, ORDDICOM, and Oracle Text Collections (e.g. nested tables,

  VARRAYs), SecureFile LOBs, OBJECT RELATIONAL XMLTypes and BINARY XML).[2] Physical standby

  may be appropriate in such a case.

• Question 22:

  For which task would you NOT use Oracle Data Mining?

  A. Predicting customer behavior

  B. Associating factors with a business issue

  C. Determining associations within a population

  D. Reducing the amount of data used in a data warehouse

  Correct Answer: D

  Explanation:

  Data mining does not reduce the amount of data in the warehouse.

  Note:

  Data mining (the analysis step of the knowledge discovery in databases process, or KDD), a relatively

  young and interdisciplinary field of computer science is the process of discovering new patterns from large

  data sets involving methods at the intersection of artificial intelligence, machine learning, statistics and

  database systems. The overall goal of the data mining process is to extract knowledge from a data set in a

  human-understandable structure and besides the raw analysis step involves database and data

  management aspects, data preprocessing, model and inference considerations, interestingness metrics,

  complexity considerations, post-processing of found structure, visualization and online updating.

• Question 23:

  Identify the type of refresh that is NOT supported by materialized views.

  A. Deferred

  B. Incremental

  C. Full

  D. Heuristic

  Correct Answer: D

  Explanation:

  Use the CREATE MATERIALIZED VIEW statement to create a materialized view. A materialized view is a

  database object that contains the results of a query.

  Incorrect answer:

  A: Specify DEFERRED to indicate that the materialized view is to be populated by the next REFRESH

  operation.

  B: Oracle Database uses the default index to speed up incremental (FAST) refresh of the materialized

  view.

  C: By default, Oracle Database creates a primary key materialized view with refresh on demand only. If a

  materialized view log exists on the table, then the column can be altered to be capable of fast refresh. If no

  such log exists, then only full refresh of the column is possible.

  References:

• Question 24:

  Your customer wants to determine "market baskets." What do you recommend?

  A. Use Oracle OLAP Option.

  B. Use Oracle SQL Analytic Functions.

  C. Use associations algorithm in Oracle Data Mining.

  D. Use regression analysis in Oracle Data Mining

  Correct Answer: C

  Explanation:

  Association is a data mining function that discovers the probability of the co-occurrence of items in a

  collection. The relationships between co-occurring items are expressed as association rules.

  Market-Basket Analysis

  Association rules are often used to analyze sales transactions. For example, it might be noted that

  customers who buy cereal at the grocery store often buy milk at the same time. In fact, association

  analysis might find that 85% of the checkout sessions that include cereal also include milk. This

  relationship could be formulated as the following rule.

  Cereal implies milk with 85% confidence

  This application of association modeling is called market-basket analysis. It is valuable for direct

  marketing, sales promotions, and for discovering business trends. Market-basket analysis can also be

  used effectively for store layout, catalog design, and cross-sell.

  Association Algorithm

  Oracle Data Mining uses the Apriori algorithm to calculate association rules for items in frequent itemsets.

  References:

• Question 25:

  How can you implement near real time data integration with Oracle Data Integrator?

  A. By accessing Change Data Capture records from logs

  B. By using Exchange Partition

  C. By mining Oracle UNDO segments

  D. By reading operating system logs

  Correct Answer: A

  Explanation: Conventional "Extract, Transform, Load" (ETL) tools closely intermix data transformation rules with integration process procedures, requiring the development of both data transformations and data flow. Oracle Data Integrator (ODI) takes a different approach to integration by clearly separating the declarative rules (the "what") from the actual implementation (the "how"). With ODI, declarative rules describing mappings and transformations are defined graphically, through a drag-and-drop interface, and stored independently from the implementation. ODI automatically generates the data flow, which can be fine-tuned if required. This innovative approach for declarative design has also been applied to ODI's framework for Changed Data Capture. ODI's CDC moves only changed data to the target systems and can be integrated with Oracle GoldenGate, thereby enabling the kind of real time integration that businesses require.

  References:

• Question 26:

  Which can be used in scenario where there are large data loads of a sensitive nature into a data warehouse?

  A. Direct path loading

  B. External tables for loading flat files

  C. Partition exchange loading

  D. Any of these are valid for certain situations.

  Correct Answer: A

  Explanation: Instead of filling a bind array buffer and passing it to the Oracle database with a SQL INSERT statement, a direct path load uses the direct path API to pass the data to be loaded to the load engine in the server. The load engine builds a column array structure from the data passed to it. The direct path load engine uses the column array structure to format Oracle data blocks and build index keys. The newly formatted database blocks are written directly to the database (multiple blocks per I/O request using asynchronous writes if the host platform supports asynchronous I/O).

  Internally, multiple buffers are used for the formatted blocks. While one buffer is being filled, one or more buffers are being written if asynchronous I/O is available on the host platform. Overlapping computation with I/O increases load performance.

  http://download.oracle.com/docs/cd/B19306_01/server.102/b14215/ldr_modes.htm#i1008815

• Question 27:

  One goal of your Information Lifecycle Management strategy using Oracle's ILM capabilities is to reduce cost or online storage. Identify two database options that would help in enabling such a strategy.

  A. RAC and Advanced Compression

  B. RAC and Partitioning

  C. Partitioning and Advanced Compression

  D. RAC One and Advanced Compression

  Correct Answer: B

  Explanation: Advanced compression: Advanced Compression, an option introduced in Oracle Database 11 g Enterprise Edition, offers a comprehensive set of compression capabilities to help organizations reduce costs, while maintaining or improving performance. It significantly reduces the storage footprint of databases through compression of structured data (numbers, characters) as well as unstructured data (documents, spreadsheets, XML and other files). It provides enhanced compression for database backups and also includes network compression capabilities for faster synchronization of standby databases. Archival Compression:

  *

  Built on HCC technology

  *

  Compression algorithm optimized for maximum storage savings

  *

  Benefits any application with data retention requirements

  *

  Best approach for ILM and data archival

  Partitioning: There are a number of benefits to partitioning data. Partitioning provides an easy way to distribute the data across appropriate storage devices depending on its usage, while still keeping the data online and stored on the most cost-effective device. Since partitioning is completely transparent to anyone accessing the data, no application changes are required, thus partitioning can be implemented at any time.

  Note There is a wide variety of information held in an organization today, for example it could be an email message, a picture, or an order in an Online Transaction Processing System. Therefore, once the type of data being retained has been identified, you already have an understanding of what its evolution and final destiny is likely to be. The challenge now before all organizations, is to understand how their data evolves and grows, monitor how its usage changes over time, and decide how long it should survive. In addition, the evolving rules and regulations such as Sarbanes-Oxley, place additional constraints on the data that is being retained and some organizations now require that data is deleted when there is no longer a legal requirement to keep it, to avoid expensive e-discovery when the data is requested for a legal matter.

  Implementing ILM using Oracle Database 11g Page 4 Information Lifecycle Management (ILM) is designed to address these issues, with a combination of processes, policies, software and hardware so that the appropriate technology can be used for each phase of the lifecycle of the data.1

  References:

• Question 28:

  You customer wants to segment their customers1 demographic data into those that use and do not use loyalty card. What would you recommend?

  A. Use Oracle OLAP Option.

  B. Use Oracle SQL Analytic Functions.

  C. Use classification algorithm in Oracle Data Mining.

  D. Use non-negative matrix factorization in Oracle Data Mining.

  Correct Answer: C

  Explanation:

  Classification is a data mining function that assigns items in a collection to target categories or classes. The goal of classification is to accurately predict the target class for each case in the data. For example, a

  classification model could be used to identify loan applicants as low, medium, or high credit risks.

  The simplest type of classification problem is binary classification. In binary classification, the target

  attribute has only two possible values: for example, high credit rating or low credit rating

  Note:

  Oracle Data Mining provides the following algorithms for classification:

  *

  Decision Tree

  Decision trees automatically generate rules, which are conditional statements that reveal the logic used to

  build the tree.

  *

  Naive Bayes

  Naive Bayes uses Bayes' Theorem, a formula that calculates a probability by counting the frequency of

  values and combinations of values in the historical data.

  *

  Generalized Linear Models (GLM)

  GLM is a popular statistical technique for linear modeling. Oracle Data Mining implements GLM for binary

  classification and for regression. GLM provides extensive coefficient statistics and model statistics, as well

  as row diagnostics. GLM also supports confidence bounds.

  *

  Support Vector Machine

  Support Vector Machine (SVM) is a powerful, state-of-the-art algorithm based on linear and nonlinear

  regression. Oracle Data Mining implements SVM for binary and multiclass classification.

  References:

• Question 29:

  How can you use Oracle Data Mining with Oracle Warehouse builder?

  A. To identify records to extract

  B. As a standard transform operation

  C. To increase write performance

  D. To eliminate ETL logging

  Correct Answer: A

  Explanation: Data Mining and Data Warehousing Data can be mined whether it is stored in flat files, spreadsheets, database tables, or some other storage format. The important criteria for the data is not the storage format, but its applicability to the problem to be solved. Proper data cleansing and preparation are very important for data mining, and a data warehouse can facilitate these activities. However, a data warehouse will be of no use if it does not contain the data you need to solve your problem. Oracle Data Mining requires that the data be presented as a case table in single-record case format. All the data for each record (case) must be contained within a row. Most typically, the case table is a view that presents the data in the required format for mining Note: Oracle Warehouse Builder (OWB) enables the design and deployment of enterprise data warehouses, data marts, and e-business intelligence applications.

  References:

• Question 30:

  You can use Oracle Data Mining unstructured data.

  A. TRUE

  B. FALSE

  Correct Answer: A

  Explanation: Data that cannot be meaningfully interpreted as numerical or categorical is considered unstructured for purposes of data mining. It has been estimated that as much as 85% of enterprise data falls into this category. Extracting meaningful information from this unstructured data can be critical to the success of a business.

  Unstructured data may be binary objects, such as image or audio files, or text objects, which are language-based. Oracle Data Mining supports text objects. Text must undergo a transformation process before it can be mined. Once the data has been properly transformed, the case table can be used for building, testing, or scoring data mining models. Most Oracle Data Mining algorithms support text

  References: