AWS Certified BigData Specialty guarantee part overview - aws bigdata

AWS Certified BigData Specialty guarantee part overview

AWS Certified BigData Specialty guarantee part overview

1. How many questions in AWS Certified BigData Specialty guarantee part include?
85  questions
2. Is this real questions?
Yes. It’s collected in our real AWS BDS-C00 exam.
3. How much does it cost?
AWS Certified BigData Specialty guarantee part is : 40$
4. How can I make payment? 
You can use buy button for AWS BDS-C00 on homepage to make payment for this part.

5. Refund policy?
If questions in AWS Certified BigData Guarantee part not appear in your exam We will refund immediately.

My Recomend:

– Acloudguru
– Guarantee part: You should spend your time to review questions carefully. They are real questions in our AWS exam so We can make sure you can pass exam. Let us show you some questions in AWS Big Data guarantee part.

Release Notes:

  1. On 16 September 2019, We are happy announcement AWS Certified BigData Specialty were available. This part include 85 questions were collected in our AWS BDS-C00 exam.

Questions in guarantee part.

  1. Company A operates in Country X. Company A maintains a large dataset of historical purchase orders that contains personal data of their customers in the form of full names and telephone numbers. The dataset consists of 5 text files, 1TB each. Currently the dataset resides on-premises due to legal requirements of storing personal data in-country. The research and development department needs to run a clustering algorithm on the dataset and wants to use Elastic Map Reduce service in the closest AWS region. Due to geographic distance, the minimum latency between the on-premises system and the closet AWS region is 200 ms. Which option allows Company A to do clustering in the AWS Cloud and meet the legal requirement of maintaining personal data in-country?
  2. A company receives data sets coming from external providers on Amazon S3. Data sets from different providers are dependent on one another. Data sets will arrive at different times and in no particular order. A data architect needs to design a solution that enables the company to do the following: Rapidly perform cross data set analysis as soon as the data becomes available Manage dependencies between data sets that arrive at different times Which architecture strategy offers a scalable and cost-effective solution that meets these requirements?
  3. A media advertising company handles a large number of real-time messages sourced from over 200 websites in real time. Processing latency must be kept low. Based on calculations, a 60-shard Amazon Kinesis stream is more than sufficient to handle the maximum data throughput, even with traffic spikes. The company also uses an Amazon Kinesis Client Library (KCL) application running on Amazon Elastic Compute Cloud (EC2) managed by an Auto Scaling group. Amazon CloudWatch indicates an average of 25% CPU and a modest level of network traffic across all running servers. The company reports a 150% to 200% increase in latency of processing messages from Amazon Kinesis during peak times. There are NO reports of delay from the sites publishing to Amazon Kinesis. What is the appropriate solution to address the latency?
  4. An online photo album app has a key design feature to support multiple screens (e.g, desktop, mobile phone, and tablet) with high-quality displays. Multiple versions of the image must be saved in different resolutions and layouts. The image-processing Java program takes an average of five seconds per upload, depending on the image size and format. Each image upload captures the following image metadata: user, album, photo label, upload timestamp. The app should support the following requirements:    Hundreds of user image uploads per second     Maximum image upload size of 10 MB     Maximum image metadata size of 1 KB Image displayed in optimized resolution in all supported screens no later than one minute after image upload Which strategy should be used to meet these requirements?
  5. An online retailer is using Amazon DynamoDB to store data related to customer transactions. The items in the table contains several string attributes describing the transaction as well as a JSON attribute containing the shopping cart and other details corresponding to the transaction. Average item size is – 250KB, most of which is associated with the JSON attribute. The average customer generates – 3GB of data per month. Customers access the table to display their transaction history and review transaction details as needed. Ninety percent of the queries against the table are executed when building the transaction history view, with the other 10% retrieving transaction details. The table is partitioned on CustomerID and sorted on transaction date. The client has very high read capacity provisioned for the table and experiences very even utilization, but complains about the cost of Amazon DynamoDB compared to other NoSQL solutions. Which strategy will reduce the cost associated with the client’s read queries while not degrading quality?





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