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NEW QUESTION: 1



A. 0
B. 1
C. 2
D. 3
Answer: C

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
Which two considerations would be appropriate when designing a Business Objects Enterprise system for high availability? (Choose two.)
A. Load balancing algorithm
B. Heartbeat cycle
C. Disaster recovery
D. Fault tolerance
Answer: C,D

NEW QUESTION: 4
Which of the following security concepts can prevent a user from logging on from home during the weekends?
A. Implicit deny
B. Time of day restrictions
C. Multifactor authentication
D. Common access card
Answer: B
Explanation:
Time of day restrictions limit when users can access specific systems based on the time of day or week. It can limit access to sensitive environments to normal business hours when oversight and monitoring can be performed to prevent fraud, abuse, or intrusion.
Incorrect Answers:
B. Multifactor authentication requires a user to supply two or more authentication factors in order to prove their identity. This would not be restricted to weekends only.
C. Implicit deny says that if you aren't explicitly granted access or privileges for a resource, you're denied access by default. If this is true, time of the week will not be a factor.
D. Common Access Cards (CACs) are smart cards used by the U.S. government and military to physically access facilities.
References:
Stewart, James Michael, CompTIA Security+ Review Guide, Sybex, Indianapolis, 2014, pp. 280-284