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Amazon BDS-C00 Exam - Topic 7 Question 104 Discussion

An organization would like to run analytics on their Elastic Load Balancing logs stored in Amazon S3 and join this data with other tables in Amazon S3. The users are currently using a BI tool connecting with JDBC and would like to keep using this BI tool.Which solution would result in the LEAST operational overhead?
D) Launch a transient Amazon EMR cluster every night that transforms new log files and loads them into Amazon Redshift.
A) Trigger a Lambda function when a new log file is added to the bucket to transform and load it into Amazon Redshift. Run the VACUUM command on the Amazon Redshift cluster every night.
B) Launch a long-running Amazon EMR cluster that continuously downloads and transforms new files from Amazon S3 into its HDFS storage. Use Presto to expose the data through JDBC.
C) Trigger a Lambda function when a new log file is added to the bucket to transform and move it to another bucket with an optimized data structure. Use Amazon Athena to query the optimized bucket.

Amazon BDS-C00 Exam - Topic 7 Question 104 Discussion

Actual exam question for Amazon's BDS-C00 exam
Question #: 104
Topic #: 7
[All BDS-C00 Questions]

An organization would like to run analytics on their Elastic Load Balancing logs stored in Amazon S3 and join this data with other tables in Amazon S3. The users are currently using a BI tool connecting with JDBC and would like to keep using this BI tool.

Which solution would result in the LEAST operational overhead?

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Suggested Answer: D

by Ozell at Sep 18, 2024, 05:44 AM

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Viola
6 months ago
C is definitely the way to go, less hassle overall!
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Fletcher
6 months ago
I think D might be better for more control over the data.
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Martin
7 months ago
Wait, can Lambda really handle that much data?
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Edda
7 months ago
I agree, using Athena is super efficient!
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Darrel
7 months ago
Option C seems like the best choice for low overhead.
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Shawnee
7 months ago
I practiced a similar question, and I think option C is the best because it uses Athena, which is serverless and easier to manage.
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Brice
7 months ago
I feel like option A could lead to issues with the VACUUM command in Redshift, which might add more maintenance work.
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Royal
8 months ago
I'm not entirely sure, but I think option B with EMR could be more complex to manage since it involves a long-running cluster.
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Rachael
8 months ago
I remember we discussed how using Lambda functions can help reduce operational overhead, so option C might be a good choice.
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Fannie
8 months ago
I'm leaning towards option C as well. Keeping the data optimized in S3 and using a serverless service like Athena feels like the cleanest approach here.
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Dexter
8 months ago
Option A with Redshift seems straightforward, but I'm worried about the operational overhead of running VACUUM commands every night. That could get messy.
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Emmanuel
8 months ago
I'm a bit confused by the differences between the EMR options. Option B seems to have a long-running cluster, while option D has a transient one. I'll need to think through the tradeoffs there.
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Brittney
8 months ago
I think option C looks the most promising. Using a Lambda function to transform and optimize the data, then querying it with Athena, seems like it would have the least operational overhead compared to the other options.
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Tina
8 months ago
Hmm, I'm a little unsure about this one. The wording is a bit tricky, and I want to make sure I understand the context before selecting an answer.
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Erasmo
1 year ago
I'd go with option E: hire a team of monkeys to manually transcribe the logs into a spreadsheet. Guaranteed zero operational overhead!
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Juan
12 months ago
Yeah, using Lambda to transform and move the logs to an optimized bucket for querying with Athena sounds efficient.
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Malika
1 year ago
I agree, option C seems like the best choice to minimize operational overhead.
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Kenneth
1 year ago
That's a creative solution, but I think we should stick with the options provided.
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Belen
1 year ago
I'm not a fan of long-running clusters, so B is out for me. I think I'll go with C - the optimized data structure and Athena seem like the way to go.
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Tamekia
11 months ago
Definitely, it seems like the most efficient choice.
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Louvenia
12 months ago
C sounds like a good option with the optimized data structure and Athena.
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Dominque
12 months ago
Yeah, I prefer a more streamlined solution.
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Darrin
1 year ago
I agree, long-running clusters can be a hassle.
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Sarah
1 year ago
Haha, I remember this kind of question from my AWS certification prep! Personally, I'd go for C. Athena is just so darn convenient.
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Son
1 year ago
Hmm, I'm leaning towards D. The transient EMR cluster seems like the simplest solution, and Redshift integration should work well with the BI tool.
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Gussie
1 year ago
This is a tricky one! I'm torn between C and D, but I think C might have the edge in terms of operational overhead.
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Edison
1 year ago
Yeah, I agree. Using Lambda to transform and move the files to an optimized bucket seems like a good solution.
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Luisa
1 year ago
I think C is the way to go, it seems like it would be less overhead.
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Nu
1 year ago
I prefer option D. Launching a transient EMR cluster every night for transformation and loading seems like a good approach.
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Altha
1 year ago
I agree with King. Using Lambda to transform and move files to an optimized bucket seems efficient.
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King
1 year ago
I think option C would result in the least operational overhead.
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