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Splunk SPLK-3001 Exam - Topic 7 Question 84 Discussion

Actual exam question for Splunk's SPLK-3001 exam

Question #: 84
Topic #: 7

[All SPLK-3001 Questions]

After data is ingested, which data management step is essential to ensure raw data can be accelerated by a Data Model and used by ES?

AApplying Tags.

BNormalization to Customer Standard.

CNormalization to the Splunk Common Information Model.

DExtracting Fields.

Show Suggested Answer

Suggested Answer: C

by Garry at Apr 11, 2024, 05:05 PM

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Mozelle

7 months ago

I had no idea normalization was that crucial!

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Fernanda

8 months ago

Nah, it's definitely B for customer standards.

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Sina

8 months ago

Wait, isn't extracting fields also super important?

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Gianna

8 months ago

Totally agree, C is the way to go!

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Fernanda

8 months ago

I think it's C, normalization to the Splunk Common Information Model.

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Mertie

9 months ago

I’m leaning towards normalization to the Splunk Common Information Model, but I could be mixing it up with another topic we covered.

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Desire

9 months ago

Applying tags sounds familiar, but I feel like normalization is the key step here.

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Val

9 months ago

I remember practicing a question similar to this, and I think extracting fields was important for making data usable.

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Jesusa

9 months ago

I think it's about normalizing data, but I'm not sure if it's to the customer standard or the Splunk Common Information Model.

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Dominga

9 months ago

I'm a bit confused on the difference between normalizing to a customer standard versus the Splunk Common Information Model. I'll need to review those concepts before deciding.

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Louvenia

9 months ago

Okay, let's see. I'm pretty sure the key here is ensuring the raw data can be used by the Data Model and ES. That probably means normalizing the data in some way.

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Herman

9 months ago

Hmm, this seems like a tricky one. I'll need to think carefully about the different data management steps and how they relate to the Data Model and ES.

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Helga

9 months ago

Extracting fields seems like it could be important for getting the data into a format that can be used by the Data Model and ES. I'll make sure to consider that option as well.

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Myrtie

9 months ago

Option D looks promising with the AWS Application Discovery Agent and Amazon Athena. That seems like a more automated approach compared to manually creating scripts. I'll make sure to understand how the agent and Athena work together.

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Kathrine

9 months ago

I think the key here is to focus on what field needs to be completed for the HR Service to be accessible on the Employee Service Center. The options seem to be Checklist, Fulfiller Instructions, Lifecycle Event type, and Record Producer. I'll need to think carefully about which one of these is the most relevant.

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Felice

10 months ago

Okay, let's see here. The question mentions URI dialing and routing between clusters, so I'm thinking the intercluster trunk and the calling search space/partition would be the two areas to investigate.

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Lashon

2 years ago

I think applying Tags can also help organize the data, so my answer is A).

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Zona

2 years ago

I agree with Candidate 1. Extracting Fields would make the raw data more structured and usable.

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Juan

2 years ago

I'm not sure. I believe it could also be C) Normalization to the Splunk Common Information Model.

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Mike

2 years ago

I think the answer is D) Extracting Fields.

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Glenna

2 years ago

Exactly! Extracting the fields is like putting the wheels on the car. It's the essential first step to getting everything else working.

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Izetta

2 years ago

Haha, I'm just imagining someone trying to use raw data without extracting the fields. It'd be like trying to drive a car without wheels!

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Salome

2 years ago

Good point, but I think extracting the fields is the foundation. Without that, the normalization won't matter much.

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Adell

2 years ago

Applying Tags is also essential for organization.

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Clare

2 years ago

I think normalization to the Splunk Common Information Model is the way to go.

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Norah

2 years ago

But isn't normalization important for consistency?

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Delsie

2 years ago

I agree, extracting fields is crucial.

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Buddy

2 years ago

Hmm, I'm not so sure. Wouldn't normalizing the data to the Splunk Common Information Model be important too? That would help ensure consistency and compatibility with ES.

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Leonora

2 years ago

I agree. Extracting the fields seems like the most essential step to ensure the raw data can be accelerated by the Data Model and used by ES.

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Francine

2 years ago

This question is a bit tricky, but I think the key is understanding the Data Model and how it interacts with Elasticsearch (ES). If the raw data isn't properly extracted and normalized, it won't be usable by the Data Model or ES.

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