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Salesforce AI Associate Exam - Topic 2 Question 20 Discussion

What are the potential consequences of an organization suffering from poor data quality?
B) Revenue loss, poor customer service, and reputational damage
A) Low employee morale, stock devaluation, and inability to attract top talent
C) Technical debt, monolithic system architecture, and slow ETL throughput

Salesforce AI Associate Exam - Topic 2 Question 20 Discussion

Actual exam question for Salesforce's Salesforce AI Associate exam
Question #: 20
Topic #: 2
[All Salesforce AI Associate Questions]

What are the potential consequences of an organization suffering from poor data quality?

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Suggested Answer: B

The potential consequences of an organization suffering from poor data quality include revenue loss, poor customer service, and reputational damage. Poor data quality can lead to inaccurate analytics and decision-making, impacting customer interactions, marketing strategies, and financial forecasting. These issues ultimately affect customer satisfaction and could lead to financial losses and a damaged brand reputation. Salesforce highlights the importance of maintaining high data quality for effective CRM and AI applications, offering various tools and best practices to enhance data integrity. For guidance on managing and improving data quality in Salesforce, see the Salesforce documentation on data quality at Salesforce Data Quality.


by Clay at May 31, 2024, 03:50 PM

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Royal
9 months ago
Totally agree with B! Customer service is crucial.
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Edgar
10 months ago
Wait, can poor data really cause stock devaluation?
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Kami
10 months ago
A is spot on! Morale really takes a hit.
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Tashia
10 months ago
I think C is more relevant for tech teams.
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Effie
10 months ago
Definitely B! Revenue loss can hit hard.
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Nathalie
10 months ago
I definitely recall that poor data quality can hurt a company's reputation, which aligns with option B. It just makes sense!
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Annelle
11 months ago
I think I saw a practice question that mentioned technical debt, but I’m not convinced that’s the main issue here. Option C seems less relevant to me.
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Avery
11 months ago
I'm not entirely sure, but I feel like low employee morale could be a factor too. Maybe option A has some merit?
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Ethan
11 months ago
I remember discussing how poor data quality can lead to revenue loss and damage to customer relationships, so I think option B might be the right choice.
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Carman
11 months ago
Ugh, I hate questions about data quality. It's such a broad topic, I'm worried I'll miss something important. I'll try to hit the high-level stuff like revenue loss and reputational damage, but I'm not feeling super confident on this one.
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Joana
11 months ago
Ah, this is a good one. I remember discussing this in class. The key is to hit the main points - financial impact, operational challenges, and brand/reputation concerns. I've got a solid strategy to tackle this.
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Val
11 months ago
Hmm, I'm a bit unsure about this one. I know poor data quality can cause issues, but I'm not sure I can confidently identify all the potential consequences. I'll have to think this through carefully.
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Bernardine
11 months ago
This question seems pretty straightforward. I'll focus on the key consequences like revenue loss, reputational damage, and poor customer service.
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Flo
11 months ago
Ah, I remember learning about this in class. I think the answer is B, Load Balancing. That sounds like a common feature for network ports.
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Denae
11 months ago
SIP is the way to go here. It's the standard protocol for communication between SIP servers and other VoIP components. I'm confident that C is the correct answer.
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Ashleigh
11 months ago
Increasing the server HBA LUN queue depth to 64 could be a good idea, but I'm not sure if that's the most effective solution for this specific problem.
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Dante
11 months ago
I'm pretty sure the EIGRP metric is based on bandwidth and delay, while OSPF uses bandwidth and delay as well, so I'll go with option C.
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Jose
11 months ago
I'm pretty sure the answer is Keystore, since that's not part of the interface package.
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Maxima
11 months ago
I think this is about the incremental budget since it involves adjustments for growth and inflation, right?
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Slyvia
2 years ago
Option B all the way! If my data's not up to snuff, I might as well just pack up my desk and go home. Ain't nobody got time for that!
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Francine
2 years ago
Hold up, what about option C? Technical debt and slow ETL? That's a recipe for a real IT nightmare if you ask me.
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Lenna
2 years ago
We need to address these issues before they snowball into a full-blown IT nightmare.
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Sheldon
2 years ago
Slow ETL throughput can cause delays in processing data, impacting the organization's efficiency.
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Danilo
2 years ago
Option C is definitely a concern. Technical debt can really pile up if data quality is poor.
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Jules
2 years ago
Hmm, I'm leaning towards B as well. I mean, who wants to lose money and end up with a bad rep? Not this girl!
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Adelina
2 years ago
Hmm, I'm leaning towards B as well. I mean, who wants to lose money and end up with a bad rep? Not this girl!
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Eve
2 years ago
B) Revenue loss, poor customer service, and reputational damage
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Gianna
2 years ago
A) Low employee morale, stock devaluation, and inability to attract top talent
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Garry
2 years ago
I agree with you, Avery. It can also result in low employee morale and stock devaluation.
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Emilio
2 years ago
Hah, I'm pretty sure the answer is B. Who wants to deal with angry customers and a tarnished reputation? Not me!
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Avery
2 years ago
I think poor data quality can lead to revenue loss and reputational damage.
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Yasuko
2 years ago
Option B is definitely the correct answer. Poor data quality can lead to all sorts of nasty consequences for the organization.
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Lashaunda
2 years ago
Yes, revenue loss, poor customer service, and reputational damage can really hurt a company in the long run.
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Hui
2 years ago
Option B is definitely the correct answer. Poor data quality can lead to all sorts of nasty consequences for the organization.
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Olga
2 years ago
Yes, revenue loss, poor customer service, and reputational damage can really hurt a company in the long run.
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Tamar
2 years ago
Option B is definitely the correct answer. Poor data quality can lead to all sorts of nasty consequences for the organization.
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Eric
2 years ago
Yes, revenue loss, poor customer service, and reputational damage are definitely things to watch out for when data quality is lacking.
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Wade
2 years ago
Option B is definitely the correct answer. Poor data quality can lead to all sorts of nasty consequences for the organization.
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