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NEW QUESTION: 1
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:

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: 2
In the survey of outdoor bridging, the main consideration is the issue of wireless line of sight. You should be able to see the opposite antenna on the site, and there should be no obstacles in the middle. In general, the following methods can be used to solve the sight distance problem and the Fresnel interval problem. Which is the simplest and most effective method?
A. Choose a good relay device.
B. Choosing a high-performance antenna can solve the "partially invisible" problem.
C. Raise the height of the antenna
D. Be aware of obstacles in the Fresnel area, such as cutting down trees.
Answer: C

NEW QUESTION: 3
Which action can increase the bottom line and add value to the customer?
A. Offer unlimited warranties on all equipment.
B. Offer 24 hour TAC support free of charge
C. Offer a Cisco Software Services contract.
D. Offer a discount for referrals.
Answer: C
Explanation:
Explanation
https://blogs.cisco.com/partner/building-your-customer-success-team-much-much-more-than-just-good-custome

NEW QUESTION: 4
Analytical techniques are a tool and technique of which process in Project Procurement Management?
A. Plan Procurement Management
B. Close Procurements
C. Conduct Procurements
D. Control Procurements
Answer: C
Explanation:
Explanation/Reference:
Explanation:
4.4.2.2 Analytical Techniques
Analytical techniques are applied in project management to forecast potential outcomes based on possible variations of project or environmental variables and their relationships with other variables. Examples of analytical techniques used in projects are:
Regression analysis,

Grouping methods,

Causal analysis,

Root cause analysis,

Forecasting methods (e.g., time series, scenario building, simulation, etc.),

Failure mode and effect analysis (FMEA),

Fault tree analysis (FTA),

Reserve analysis,

Trend analysis,

Earned value management, and

Variance analysis.

Process: 12.2 Conduct Procurements
Definition: The process of obtaining seller responses, selecting a seller, and awarding a contract.
Key Benefit: The key benefit of this process is that it provides alignment of internal and external stakeholder expectations through established agreements.
Inputs
1. Procurement management plan
2. Procurement documents
3. Source selection criteria
4. Seller proposals
5. Project documents
6. Make-or-buy decisions
7. Procurement statement of work
8. Organizational process assets
Tools & Techniques
1. Bidder conference
2. Proposal evaluation techniques
3. Independent estimates
4. Expert judgment
5. Advertising
6. Analytical techniques
7. Procurement negotiations
Outputs
1. .Selected sellers
2. .Agreements
3. .Resource calendars
4. .Change requests
5. .Project management plan updates
6. .Project documents updates

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