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NEW QUESTION: 1
What is the effect of setting the STATISTICS_LEVEL initialization parameter to BASIC?
A. The Oracle server dynamically generates the necessary object-level statistics on tables as part of query optimization.
B. Optimizer statistics are collected automatically.
C. Automatic Workload Repository (AWR) snapshots are not generated automatically.
D. Only timed operating system (OS) statistics and plan execution statistics are collected.
Answer: C
Explanation:
Explanation/Reference:
References: https://docs.oracle.com/cd/B28359_01/server.111/b28320/initparams240.htm#REFRN10214

NEW QUESTION: 2
You are developing a solution using a Lambda architecture on Microsoft Azure.
The data at test layer must meet the following requirements:
Data storage:
*Serve as a repository (or high volumes of large files in various formats.
*Implement optimized storage for big data analytics workloads.
*Ensure that data can be organized using a hierarchical structure.
Batch processing:
*Use a managed solution for in-memory computation processing.
*Natively support Scala, Python, and R programming languages.
*Provide the ability to resize and terminate the cluster automatically.
Analytical data store:
*Support parallel processing.
*Use columnar storage.
*Support SQL-based languages.
You need to identify the correct technologies to build the Lambda architecture.
Which technologies should you use? To answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Data storage: Azure Data Lake Store
A key mechanism that allows Azure Data Lake Storage Gen2 to provide file system performance at object storage scale and prices is the addition of a hierarchical namespace. This allows the collection of objects/files within an account to be organized into a hierarchy of directories and nested subdirectories in the same way that the file system on your computer is organized. With the hierarchical namespace enabled, a storage account becomes capable of providing the scalability and cost-effectiveness of object storage, with file system semantics that are familiar to analytics engines and frameworks.
Batch processing: HD Insight Spark
Aparch Spark is an open-source, parallel-processing framework that supports in-memory processing to boost the performance of big-data analysis applications.
HDInsight is a managed Hadoop service. Use it deploy and manage Hadoop clusters in Azure. For batch processing, you can use Spark, Hive, Hive LLAP, MapReduce.
Languages: R, Python, Java, Scala, SQL
Analytic data store: SQL Data Warehouse
SQL Data Warehouse is a cloud-based Enterprise Data Warehouse (EDW) that uses Massively Parallel Processing (MPP).
SQL Data Warehouse stores data into relational tables with columnar storage.
References:
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-namespace
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/batch-processing
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-overview-what-is

NEW QUESTION: 3
The storyteller's __________ anecdotes earned her the __________ attention of the crowd.
A. meandering. .distracted
B. compelling. .rapt
C. pointed. .spellbound
D. poignant. .abrasive
E. moribund. .lucid
Answer: B
Explanation:
Attack the second blank first. The most likely adjective to describe attention will be something like
undivided or rapt. (A), which includes rapt, is the answer. (B), which includes spellbound, is also possible.
But pointed anecdotes doesn't make sense, so the answer is (A).