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NEW QUESTION: 1
View the Exhibits exhibit1 and exhibit2.

Both the processes use PROG_1 as the job template that is already available. The time taken by the jobs are recorded in the TEST_LOG table. While comparing the time taken to create the jobs, the process in exhibit1 takes less time than the process in exhibit2.
What is the reason for this?
A. It updates several tables in the SYSTEM tablespace instead of creating new tables.
B. It creates less metadata for the jobs.
C. It creates jobs temporarily in memory only.
D. It writes the job metadata to disk in compressed format.
Answer: B

NEW QUESTION: 2
Refer to the Exhibit.

A servlet sets a session-scoped attribute product with an instance of com.example.product an forward to a JSP. Which two output the name of the product in the response? (Choose two)
A. <jsp:useBean id="com.example.Product" scope="page">
<%= product.getName() %>
</jsp:useBean>
B. <jsp:useBean id="product" type="com.example.Product" scope="page" /> <%= product.getName() %>
C. <jsp:useBean id="product" class="com.example.Product" /> <%= product.getName() %>
D. <jsp:useBean id="product" type="com.example.Product">
<%= product.getName() %>
</jsp:useBean>
E. <%= product.getName() %>
Answer: A,C

NEW QUESTION: 3
展示を参照してください。

すべての隣接ルータはPEER-1という名前のBGPピアグループにあります。すべてのパスワードはCiscoとして設定されます。これらのメッセージは、ルーターDSW1のコンソールに記録されます。ルーターコアとピアリングしています。 DSW1とコア間でピアリングセッションを形成できる2つの構成はどれですか？ （2つ選択してください。）
A. DSW1(config-router)#neighbor 5.0.0.5 peer-group PEER-1 DSW1(config-router)#neighbor PEER-1 password cisco
B. Core(config-router)#neighbor 5.0.0.5 peer-group PEER-1 Core(config-router)#neighbor PEER-1 password cisco
C. Core(config-router)#neighbor 5.0.0.5 peer-group PEER-2 Core(config-router)#neighbor PEER-1 password cisco
D. Core(config-router)#neighbor 51.51.51.1 peer-group PEER-1 Core(config-router)#neighbor PEER-1 password cisco
E. DSW1(config-router)#neighbor 51.51.51.1 peer-group PEER-1 DSW1(config-router)#neighbor PEER-2 password cisco
Answer: B,E

NEW QUESTION: 4
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