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NEW QUESTION: 1
Using the customers table, you need to generate a report that shows 50% of each credit amount in each income level. The report should NOT show any repeated credit amounts in each income level.
Which query would give the required result?

A. Option B
B. Option D
C. Option C
D. Option A
Answer: C
Explanation:
Duplicate Rows Unless you indicate otherwise, SQL displays the results of a query without eliminating the duplicate rows. To eliminate duplicate rows in the result, include the DISTINCT keyword in the SELECT clause immediately after the SELECT keyword. You can specify multiple columns after the DISTINCT qualifier. The DISTINCT qualifier affects all the selected columns, and the result is every distinct combination of the columns.

NEW QUESTION: 2
コマンドip ospf authentication key 1はどのレベルに実装されていますか。
A. process
B. enable
C. Interface
D. global
Answer: C
Explanation:
Use the ip ospf authentication-key interface command to specify this password. If you enable MD5 authentication with the message-digest keyword, you must configure a password with the ip ospf message- digest-key interface command.
interface GigabitEthernet0/1
ip address 192.168.10.1 255.255.255.0
ip ospf authentication message-digest
ip ospf message-digest-key 1 md5 CCNA
Source: Cisco Official Certification Guide, Implement Routing Update Authentication on OSPF, p.348 The OSPFv2 Cryptographic Authentication feature allows you to configure a key chain on the OSPF interface to authenticate OSPFv2 packets by using HMAC-SHA algorithms. You can use an existing key chain that is being used by another protocol, or you can create a key chain specifically for OSPFv2.
If OSPFv2 is configured to use a key chain, all MD5 keys that were previously configured using the ip ospf message-digest-key command are ignored.
Device> enable
Device# configure terminal
Device(config)# interface GigabitEthernet0/0/0
Device (config-if)# ip ospf authentication key-chain sample1 Device (config-if)# end
Source: http://www.cisco.com/c/en/us/td/docs/ios-xml/ios/iproute_ospf/configuration/xe-3s/iro-xe-3s-book/iro- ospfv2-crypto-authen-xe.html
In both cases OSPF and OSPFv1 the ip ospf authentication is inserted at interface level

NEW QUESTION: 3
Which two statements are true about Page Layout of a report?
There are 2 correct answers to this question.
Response:
A. It defines what the report will look like when printed.
B. It displays the definition of the block.
C. It displays the report page by default.
D. It defines what the report will look like in PDF mode.
Answer: A,D

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