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NEW QUESTION: 1



A. Option A
B. Option B
C. Option D
D. Option C
Answer: A

NEW QUESTION: 2
Which three options are true about parallel queries when PARALLEL_DEGREE_POLICY is set to MANUAL and the session is using the default settings for parallel query, DDL, and DML?
A. In a CREATE TABLE . . . AS SELECT (CTAS) statement, SELECT is parallelized only if create TABLE is parallelized.
B. Single row inserts are never executed is parallel.
C. A subquery in a parallel DML is parallelized only if it includes a parallel hint.
D. In an INSERT INTO . . . SELECT FROM statement, INSERT is parallelized if select is parallelized.
E. The number of parallel execution servers requested for a cursor is based on the greatest degree of parallelism associated with any object accessed by the cursor.
F. A SELECT statement can be executed in parallel only if no scalar subqueries are contained in the SELECT list.
Answer: B,D,F
Explanation:
* Decision to Parallelize
A SELECT statement can be parallelized only if the following conditions are satisfied:
/ The query includes a parallel hint specification (PARALLEL or PARALLEL_INDEX) or the schema objects referred to in the query have a PARALLEL declaration associated with them.
/ At least one of the tables specified in the query requires one of the following:
A full table scan
An index range scan spanning multiple partitions
/ (C) No scalar subqueries are in the SELECT list.
*By default, the system only uses parallel execution when a parallel degree has been explicitly set on an object or if a parallel hint is specified in the SQL statement.
*CREATE TABLE ... AS SELECT in Parallel
Parallel execution lets you parallelize the query and create operations of creating a table as a subquery from another table or set of tables. This can be extremely useful in the creation of summary or rollup tables.
Clustered tables cannot be created and populated in parallel.
* PARALLEL_DEGREE_POLICY specifies whether or not automatic degree of Parallelism, statement queuing, and in-memory parallel execution will be enabled.
MANUAL
Disables automatic degree of parallelism, statement queuing, and in-memory parallel execution. This reverts the behavior of parallel execution to what it was prior to Oracle Database 11g Release 2 (11.2). This is the default.
Incorrect:
A:
*For parallel DML (INSERT, UPDATE, MERGE, and DELETE), the reference object that
determines the DOP (degree of parallelism) is the table being modified by and insert, update, or delete operation. Parallel DML also adds some limits to the DOP to prevent deadlock. If the parallel DML statement includes a subquery, the subquery's DOP is the same as the DML operation.
*For parallel DDL, the reference object that determines the DOP is the table, index, or partition being created, rebuilt, split, or moved. If the parallel DDL statement includes a subquery, the subquery's DOP is the same as the DDL operation.
D: The CREATE TABLE ... AS SELECT statement contains two parts: a CREATE part (DDL) and a SELECT part (query). Oracle Database can parallelize both parts of the statement.
The query part of a CREATE TABLE ... AS SELECT statement can be parallelized only if the following conditions are satisfied: - The query includes a parallel hint specification (PARALLEL or PARALLEL_INDEX) or the CREATE part of the statement has a PARALLEL clause specification or the schema objects referred to in the query have a PARALLEL declaration associated with them. - At least one of the tables specified in the query requires one of the following: a full table scan or an index range scan spanning multiple partitions.
Reference: Oracle Database VLDB and Partitioning Guide, Using Parallel Execution

NEW QUESTION: 3
You have a petabyte of analytics data and need to design a storage and processing platform for it. You must be able to perform data warehouse-style analytics on the data in Google Cloud and expose the dataset as files for batch analysis tools in other cloud providers. What should you do?
A. Store the full dataset in BigQuery, and store a compressed copy of the data in a Cloud Storage bucket.
B. Store and process the entire dataset in Cloud Bigtable.
C. Store the warm data as files in Cloud Storage, and store the active data in BigQuery. Keep this ratio as
80% warm and 20% active.
D. Store and process the entire dataset in BigQuery.
Answer: A