AWS SAA Certification Prep – 2018

I’m getting ready to take the AWS Solutions Architect Associate 2018 test.  Below are some final items I need to review before the exam.

AWS FAQ’s:

Specific Items:

  • Spot vs Spot Block
  • Application ELB vs Classic ELB
  • Convertible vs. Standard Reserved Instances
  • EBS Cost vs Performance
  • EC2 reverse proxy (link) (link)
  • Glacier Retrieval Options
  • Beanstalk vs. NGINX
  • Cross-region snapshots for databases

SSDT 2017 and Backwards Compatibility

If you are working with SSDT 2017 and creating reports for earlier versions of SSRS, please read the following article!

I really think MS did a great job here with the “TargetServerVersion” property functionality, which allows reports to convert to previous SSRS formats during deployment.

aplusd.png

AWS Developer Certification – My Plan

Below is my plan to obtain the AWS Developer Certification.

For each of the following areas listed further down, I am trying to do the following:

  • Read the FAQ’s
  • Practice in the console
  • Practice with the CLI and understand the functions\parameters
  • Review all HTTP codes
  • Review all defaults and limits
  • Review uniqueness of each area

Here are the areas I am covering in preparation for the exam.

  • EC2
  • S3
  • DynamoDB
  • SNS
  • SQS
  • VPC
  • ELB
  • Lambda
  • Route 53
  • RDS
  • SWF
  • Cloudformation
  • Elastic Beanstalk
  • API Gateway
  • Storage Gateway
  • EFS
  • CloudWatch
  • CloudTrail
  • IAM

The exam is only 55 questions, so i’m not sure how in depth the exam will go on each of these.  Regardless, its a good to review all of the areas!

Python – Tesseract – OCR – IMAGE

You can do some pretty cool things with tesseract-ocr.  Using PyOCR, which is a wrapper for Tesseract, you can generate text from an image using Tesseract.

Example Image:

aws_.jpg

Example Output:

Tesseract.png

Example Code:

from wand.image import Image
from PIL import Image as PI
import pyocr
import pyocr.builders
import io
import sys

reload(sys) 
sys.setdefaultencoding('utf8')
 
tool = pyocr.get_available_tools()[0]
lang = tool.get_available_languages()[1]
 
txt_list = tool.image_to_string(
 PI.open('/home/build/aws.jpg'),
 lang=lang,
 builder=pyocr.builders.TextBuilder())

outputFile = open('output.txt', 'w')
for item in txt_list:
 outputFile.write("%s" % item)
outputFile.close()

Another use case I was working on today was rendering the text in a PDF file using Tesseract.  I was converting the PDF to an image file first, then performing the above actions to read the text from the new image.

Here are a couple valuable resources I used to complete this little test.

  • Installing Tesseract on a RHEL system – http://www.keienberg.com/install-tesseract-3-04-centos-7/ (link)
  • Installing PyOCR and other image conversion tools – https://pythontips.com/2016/02/25/ocr-on-pdf-files-using-python/ (link)

Getting all the prerequisites installed was by far the hardest part on this effort.