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Salesforce Marketing Cloud Intelligence Accredited Professional (AP-215) Exam - Topic 1 Question 17 Discussion

What are two potential reasons for performance issues (when loading a dashboard) when using the CRM data stream type?
D) The data is stored at the workspace level.
A) When a data stream type ''CRM - Leads' is created, another complementary 'CRM - Opportunity' is created automatically.
B) Pacing - daily rows are being created for every lead and opportunity keys
C) No mappable measurements - all measurements are calculated

Salesforce Marketing Cloud Intelligence Accredited Professional (AP-215) Exam - Topic 1 Question 17 Discussion

Actual exam question for Salesforce's Marketing Cloud Intelligence Accredited Professional (AP-215) exam
Question #: 17
Topic #: 1
[All Marketing Cloud Intelligence Accredited Professional (AP-215) Questions]

What are two potential reasons for performance issues (when loading a dashboard) when using the CRM data stream type?

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Suggested Answer: D

by Kerry at Sep 12, 2024, 03:02 AM

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Geraldo
9 months ago
Totally agree with B, too many daily rows can be a nightmare!
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Salley
10 months ago
Wait, is it really D? I thought data was stored differently.
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Lashonda
10 months ago
C makes sense, calculated measurements can be a hassle.
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Arlette
10 months ago
A seems off, I don't think they create automatically.
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Antonio
10 months ago
Definitely B, pacing can slow things down a lot.
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Cristal
10 months ago
I’m a bit confused about option A. I don’t recall if the automatic creation of 'CRM - Opportunity' impacts loading times, but it seems possible.
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Oren
11 months ago
I practiced a similar question, and I think the workspace level storage mentioned in option D might also contribute to performance issues.
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Linsey
11 months ago
I'm not entirely sure, but I feel like option C could be a factor too. If all measurements are calculated, that might slow things down.
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Letha
11 months ago
I think one reason could be related to pacing, like option B. I remember something about daily rows affecting performance.
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Shaquana
11 months ago
Hmm, I'm not sure about the workspace-level data storage. Does that mean the data is less accessible or optimized for dashboard loading? I'll have to think about that one.
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Rose
11 months ago
And the fact that all the measurements are calculated rather than mapped could also be a factor. That extra processing might be causing performance problems when loading the dashboard.
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Tracey
11 months ago
Ah, I think I know one of the reasons - the pacing of the data creation. If it's generating daily rows for every lead and opportunity, that could really slow things down, especially for large datasets.
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Regenia
11 months ago
Okay, let's see. I'm guessing it has something to do with the way the data is structured or processed. Maybe the automatic creation of the 'CRM - Opportunity' data stream is causing issues?
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Francene
11 months ago
Hmm, this looks like it could be a tricky one. I'll need to think carefully about the potential reasons for performance issues with the CRM data stream type.
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Aleta
11 months ago
Hmm, I'm a bit unsure about this one. The options seem to be focused on different aspects of communication, but I'm not sure which one specifically indicates the behavioral tier. I'll have to think this through carefully.
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Lashon
1 year ago
Wait, so the CRM data stream is creating a row for every lead and opportunity, daily? That's like a million rows a day! No wonder the dashboard is sluggish - the poor server must be gasping for air!
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Robt
1 year ago
C) No mappable measurements - all measurements are calculated
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Pedro
1 year ago
B) Pacing - daily rows are being created for every lead and opportunity keys
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Ty
1 year ago
A) When a data stream type ''CRM - Leads' is created, another complementary 'CRM - Opportunity' is created automatically.
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Nicolette
1 year ago
D) The data is stored at the workspace level - oh, that's a good one. If the data is not partitioned or optimized for querying, that could definitely slow things down. Workspace-level storage is probably not the best choice for a CRM data stream.
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Melvin
1 year ago
I'm surprised option A) is even an option. Creating a complementary data stream type automatically? That sounds like a recipe for disaster, not a reason for performance issues!
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Francesco
1 year ago
C) No mappable measurements - all measurements are calculated - that's an interesting point. If there are no pre-calculated metrics and everything needs to be computed on the fly, that could definitely impact performance.
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Glendora
1 year ago
C) No mappable measurements - all measurements are calculated - that's an interesting point. If there are no pre-calculated metrics and everything needs to be computed on the fly, that could definitely impact performance.
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Carey
1 year ago
B) Pacing - daily rows are being created for every lead and opportunity keys
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Sheridan
1 year ago
A) When a data stream type 'CRM - Leads' is created, another complementary 'CRM - Opportunity' is created automatically.
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Nelida
1 year ago
B) Pacing - daily rows are being created for every lead and opportunity keys - that seems like a reasonable explanation for performance issues. The constantly growing data volume could definitely slow down the dashboard loading.
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Edna
1 year ago
B) Pacing - daily rows are being created for every lead and opportunity keys - that seems like a reasonable explanation for performance issues.
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Lonny
1 year ago
A) When a data stream type 'CRM - Leads' is created, another complementary 'CRM - Opportunity' is created automatically.
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Tamra
1 year ago
I believe the data being stored at the workspace level could also contribute to performance issues.
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Reid
1 year ago
I agree with Nan. Pacing and no mappable measurements can definitely cause performance issues.
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Nan
1 year ago
I think the potential reasons could be pacing and no mappable measurements.
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