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NEW QUESTION: 1
An application issues many expensive join aggregations type queries. Examine the Exhibit to view the queries.

Which two could help improve the performance of these SQL statements without changing application code?
A. Create an STS for these queries and use SQL Performance Analyzer, which may generate advice about MVIEWs.
B. Create an STS for these queries and use SQL Access Advisor, which may generate advice about MVIEWs.
C. Create B*-Tree indexes on the join columns.
D. Create a materialized view with query rewrite enabled for the first statement and nested MVIEWs for the other statements.
E. Collect histogram statistics on columns for which aggregating functions are performed.
Answer: B,D
Explanation:
B:Materialized views and indexes are essential when tuning a database to achieve optimum performance for complex, data-intensive queries.
D:
*STS - SQL tuning set. *A SQL Tuning Set is a database object that includes one or more SQL statements and their execution statistics and execution context. You can use the set as an input source for various advisors, such as SQL Tuning Advisor, SQL Access Advisor, and SQL Performance Analyzer.
*SQL Access Advisor: Materialized views and indexes are essential when tuning a database to achieve optimum performance for complex, data-intensive queries. The SQL Access Advisor helps you achieve your performance goals by recommending the proper set of materialized views, materialized view logs, and indexes for a given workload. Understanding and using these structures is essential when optimizing SQL as they can result in significant performance improvements in data retrieval. The advantages, however, do not come without a cost. Creation and maintenance of these objects can be time consuming, and space requirements can be significant.
The SQL Access Advisor recommends bitmap, function-based, and B-tree indexes. A bitmap index offers a reduced response time for many types of ad hoc queries and reduced storage requirements compared to other indexing techniques. B-tree indexes are most commonly used in a data warehouse to index unique or near-unique keys.
Note: *Conventional wisdom holds that bitmap indexes are most appropriate for columns having low distinct values--such as GENDER, MARITAL_STATUS, and RELATION. This assumption is not completely accurate, however. In reality, a bitmap index is always advisable for systems in which data is not frequently updated by many concurrent systems.Abitmap index on a column with 100-percent unique values (a column candidate for primary key) is as efficient as a B-tree index. *By default, the Oracle creates a b_tree index. In a b-tree, you walk the branches until you get to the node that has the data you want to use. In the classic b-tree structure, there are branches from the top that lead to leaf nodes that contain the data.
Incorrect: not E:SQL Performance Analyzer enables you to assess the performance impact of any system change resulting in changes to SQL execution plans and performance characteristics. Examples of common system changes for which you can use SQL Performance Analyzer include:
Database upgrade Configuration changes to the operating system, hardware, or database Database initialization parameter changes Schema changes, for example, adding new indexes or materialized views Gathering optimizer statistics Validating SQL tuning actions, for example, creating SQL profiles or implementing partitioning

NEW QUESTION: 2
In which three situations will a Smart Scan occur on a table for which Smart Scan is possible, assuming that nothing in the environment prevents a Smart Scan?
A. Executing queries in parallel.
B. An update to the table is made identifying the row to be modified by primary key.
C. setting serial_direct_read= true in the session issuing the SQL statements and executing a serial query.
D. The ASM diskgroup containing the table's tablespace has a 4 MByte AU size.
E. Having direct path reads used at execution time.
F. An index range scan occurs for the table.
Answer: A,C,E
Explanation:
Explanation/Reference:
Whether you get a direct path read & smart scan, depends on the current buffer cache size, how big segment you're about to scan and how much of that segment is actually cached at the moment. This dynamic decision unfortunately can cause unexpected surprises and variance in your report/batch job runtimes.
To work around these problems and force a direct path read/smart scan, you can either:
(B) Run your query in parallel as parallel full segment scans will use direct path reads, unless your

parallel_degree_policy = AUTO, then you may still get buffered reads thanks to the dynamic in-memory parallel execution decision of Oracle 11.2
(C) Run your query in serial, but force the serial direct path reads by setting _serial_direct_read

TRUE (or ALWAYS in 11.2.0.2+)
D: One of the most common Exadata performance problems is that the direct path reads (and thus also Smart Scans) don't sometimes kick in when running full scans in serial sessions.
Note: Smart Scan is a subset of Cell Offload Processing and is perhaps the feature that is most highly associated with Exadata. Smart Scan allows Oracle databases to push query information down to the storage level, specifically:
Filtration (predicate information)

Column Projection

Join Filtering

Incorrect Answers:
F: Objects such as clusters and index organized tables cannot be Smart Scanned.
References: http://blog.tanelpoder.com/2013/05/29/forcing-smart-scans-on-exadata-is-_serial_direct_read- parameter-safe-to-use-in-production/

NEW QUESTION: 3
Note: This question is part of a series of questions that use the same or similar answer choices. An answer choice may be correct for more than one question in the series. Each question is independent of the other questions in this series. Information and details provided in a question apply only to that question.
You are developing Microsoft SQL Server Analysis Services (SSAS) tabular model.
The model must meet the following requirements:
You need to configure model.
What should you do?
A. Ensure that the Transactional Deployment property is set to False.
B. Process the model in Process Full mode.
C. Ensure that the Transactional Deployment property is set to True.
D. Process the model in Process Defrag mode.
E. Ensure that DirectQuery is enabled for the model.
F. Process the model in Process Data mode.
G. Ensure that DirectQuery is disabled for the model.
Answer: E
Explanation:
DAX originally emerged from a Power Pivot add-in for Excel, as a formula language extension for creating calculated columns and measures for data analysis (which are also integral elements of one SSAS Tabular model database, too), but when Microsoft added support for DAX queries in SQL Server 2012, BI experts started "daxing" data from Tabular model databases.
That trend continues, because of simplicity and fast query execution (related to DirectQuery mode in SSAS Tabular).
References:https://www.sqlshack.com/query-ssas-tabular-model-database-using-dax- functions/