Oracle 1Z0-515 Online Practice Questions and Exam Preparation

Oracle 1Z0-515 Online Questions & Answers

• Question 11:

  Identify the indexing technique you would use to minimize partition maintenance.

  A. Local indexes
  B. Global partitioned indexes
  C. Global nonpartitioned indexes
  D. Both global partitioned and global nonpartitioned indexes
  A. Local indexes

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  If your priority is manageability, use a local index.

  Local partitioned indexes are easier to manage than other types of partitioned indexes. They also offer greater availability and are common in DSS environments. The reason for this is equipartitioning: each partition of a local index is

  associated with exactly one partition of the table. This enables Oracle to automatically keep the index partitions in sync with the table partitions, and makes each table-index pair independent. Any actions that make one partition's data invalid

  or unavailable only affect a single partition.

  Local partitioned indexes support more availability when there are partition or subpartition maintenance operations on the table. A type of index called a local nonprefixed index is very useful for historical databases. In this type of index, the

  partitioning is not on the left prefix of the index columns.

• Question 12:

  Identify the statement about ASM that is NOT true.

  A. ASM is easier to manage than file systems.
  B. ASM delivers the performance of raw partitions.
  C. ASM is an extra cost option for Oracle databases.
  D. ASM delivers automatic striping and mirroring.
  B. ASM delivers the performance of raw partitions.

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  ASM is a management tool, not a RAW performance tool.

  Note:

  Automatic Storage Management (ASM) is a new feature that has be introduced in Oracle 10g to simplify the storage of Oracle datafiles, controlfiles and logfiles.

  Automatic Storage Management (ASM) simplifies administration of Oracle related files by allowing the administrator to reference disk groups rather than individual disks and files, which are managed by ASM. The ASM functionality is an

  extention of the Oracle Managed Files (OMF) functionality that also includes striping and mirroring to provide balanced and secure storage. The new ASM functionality can be used in combination with existing raw and cooked file systems,

  along with OMF and manually managed files.

  The ASM functionality is controlled by an ASM instance. This is not a full database instance, just the memory structures and as such is very small and lightweight.

  The main components of ASM are disk groups, each of which comprise of several physical disks that are controlled as a single unit. The physical disks are known as ASM disks, while the files that reside on the disks are known as ASM files.

  The locations and names for the files are controlled by ASM, but user- friendly aliases and directory structures can be defined for ease of reference. The level of redundancy and the granularity of the striping can be controlled using templates.

  Default templates are provided for each file type stored by ASM, but additional templates can be defined as needed. Failure groups are defined within a disk group to support the required level of redundancy. For two- way mirroring you would

  expect a disk group to contain two failure groups so individual files are written to two locations.

  In summary ASM provides the following functionality:

  *Manages groups of disks, called disk groups.

  *Manages disk redundancy within a disk group.

  *Provides near-optimal I/O balancing without any manual tuning. *Enables management of database objects without specifying mount points and filenames.

  *Supports large files.

• Question 13:

  Which questions CANNOT be addressed by Oracle Data Mining?

  A. Fraud detection
  B. Prediction of customer behavior
  C. Root cause de
  D. Identify factors associated with a business problem
  C. Root cause de

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  Data Mining can provide valuable results:

  *Predict customer behavior (Classification) (not B)

  *Predict or estimate a value (Regression)

  *Segment a population (Clustering)

  *Identify factors more associated with a business

  problem (Attribute Importance) (not D)

  *

  Find profiles of targeted people or items (Decision Trees)

  *

  Determine important relationships and market baskets within the population (Associations)

  *

  Find fraudulent or rare events (Anomaly Detection) (not A)

• Question 14:

  Which statement is true for you to get the benefits of partition-wise joins?

  A. The parent table must be partitioned on the join Key and the child table must be partitioned on] the join key.
  B. The parent table must be partitioned on the primary key and the child table must be partition the join key.
  C. The child table must use a reference partition.
  D. The parent table must be partitioned on the primary key and the child table must use a ref partition.
  A. The parent table must be partitioned on the join Key and the child table must be partitioned on] the join key.

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  Note:

  Partition-wise joins reduce query response time by minimizing the amount of data exchanged among parallel execution servers when joins execute in parallel. This significantly reduces response time and improves the use of both CPU and

  memory resources. In Oracle Real Application Clusters (RAC) environments, partition-wise joins also avoid or at least limit the data traffic over the interconnect, which is the key to achieving good scalability for massive join operations.

  Partition-wise joins can be full or partial.

  Oracle decides which type of join to use.

• Question 15:

  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
  B. RAC and Partitioning

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  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

• Question 16:

  Your BI tool (for example, Oracle Business Intelligence Enterprise Edition Plus, Business Objects and Cognos) will be used to query an Oracle database that includes the Oracle OLAP Option. What does tool generate in submitting queries that might include data stored in relational tables and OLAP cubes?

  A. SQL
  B. PL/SQL
  C. Proprietary API code
  D. SQL for relational and proprietary API code for OLAP
  B. PL/SQL

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  DBMS_CUBE PL/SQL Package. In Database 11gR2, a new feature was added that allows cubes and dimensions to be entirely defined via PL/SQL calls, thus making it a much simpler job to automate the creation and refresh of cubes within the context of an application.

• Question 17:

  You can use Oracle Data Mining unstructured data.

  A. TRUE
  B. FALSE
  A. TRUE

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  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

• Question 18:

  Which feature of Oracle Warehouse Builder can be used to help ensure data quality?

  A. Data extraction
  B. Data profiling
  C. Logical mapping
  D. Exception reporting
  B. Data profiling

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  After you connect to your data sources in Oracle Warehouse Builder (including Oracle databases, sources accessed through gateways, and flat file sources) you can apply full-featured data profiling to generate statistics about data quality, and to discover complex patterns, foreign key relationships, and functional dependencies. You can then design complex data rules and create data auditors to monitor compliance with those rules in any source or target system in your landscape, regardless of whether those sources are loaded using Oracle Warehouse Builder or other ETL tools.

  For customers who have selected solutions other than Oracle Warehouse Builder for data profiling and data quality, these can be applied independently of Oracle Warehouse Builder ETL and design features. Note: Oracle Warehouse Builder is a full-featured data integration, data warehousing, data quality and metadata management solution designed for the Oracle database. Oracle Warehouse Builder is an integral part of Oracle Database 11g Release 2 (11.2) and is installed as part of every database installation (other than Oracle Database XE).

  The major feature areas of Oracle Warehouse Builder include: *Data modeling *Extraction, Transformation, and Load (ETL) *Data profiling and data quality *Metadata management *Business-level integration of ERP application data *Integration with Oracle business intelligence tools for reporting purposes *Advanced data lineage and impact analysis Oracle Warehouse Builder is also an extensible data integration and data quality solutions platform. Oracle Warehouse Builder can be extended to manage metadata specific to any application, and can integrate with new data source and target types, and implement support for new data access mechanisms and platforms, enforce your organization's best practices, and foster the reuse of components across solutions.

• Question 19:

  Identify the benefit of using interval partitioning.

  A. Automatic creation of new partitions based on hash values
  B. Automatic creation of new partitions based on the value of data being entered
  C. Improved performance compared to range partitions
  D. Automatic transfer of older partitions lower cost storage
  B. Automatic creation of new partitions based on the value of data being entered

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  Interval Partitioning was introduced in 11g, interval partitions are extensions to range partitioning. These provide automation for equi-sized range partitions. Partitions are created as metadata and only the start partition is made persistent. The additional segments are allocated as the data arrives. The additional partitions and local indexes are automatically created.

• Question 20:

  Identify the true statement about adaptive parallelism.

  A. It Is turned on by default.
  B. It is turned off by default.
  C. You should always leave the default setting
  D. There is no such thing.
  A. It Is turned on by default.

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  Adaptive Parallelism: The adaptive multiuser algorithm, which is enabled by default, reduces the degree of parallelism as the load on the system increases. When using the Oracle Database adaptive parallelism capabilities, the database uses

  an algorithm at SQL execution time to determine whether a parallel operation should receive the requested DOP or have its DOP lower to ensure the system is not overloaded.

  In a system that makes aggressive use of parallel execution by using a high DOP, the adaptive algorithm adjusts the DOP down when only a few operations are running in parallel. While the algorithm still ensures optimal resource utilization,

  users may experience inconsistent response times. Using solely the adaptive parallelism capabilities in an environment that requires deterministic response times is not advised. Adaptive parallelism is controlled through the database

  initialization parameter PARALLEL_ADAPTIVE_MULTI_USER.