SAS A00-260 Exam - Topic 9 Question 91 Discussion

Actual exam question for SAS's A00-260 exam

Question #: 91
Topic #: 9

You want to create a job that checks conditions for your data before that data is loaded into a data warehouse. The job should be able to detect error conditions such as missing data or duplicate data and take appropriate actions like registering error conditions in log, etc. Which SAS Data Integration Studio transformation should you use?

AData Transfer

BData Validation

CData Quality

DData Extract

Show Suggested Answer

Suggested Answer: B

by Lawrence at Jul 10, 2025, 12:51 PM

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Freeman

1 month ago

I agree, Data Quality is the best option for error checks!

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Deonna

2 months ago

Wait, are we sure Data Quality is the right choice?

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Delmy

2 months ago

Data Transfer? Nah, that’s not it.

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Bettina

2 months ago

I vaguely recall that Data Extract is used for pulling data, not for validating it, so it seems like a wrong choice here.

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Karl

2 months ago

I feel like the Data Transfer option is more about moving data rather than checking it, so I don't think that's the answer.

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Olive

2 months ago

I'm not entirely sure, but I remember practicing with the Data Quality transformation for similar questions. It might also handle error conditions.

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Solange

2 months ago

I think Data Validation makes more sense here.

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Tegan

3 months ago

I think the Data Validation transformation could be the right choice since it checks for errors like missing or duplicate data.

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Herminia

3 months ago

Definitely go with Data Quality for this!

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Iesha

3 months ago

Based on the details provided, I think the Data Validation transformation is the way to go. It's designed for this exact use case of validating data before loading into a data warehouse and handling any issues that come up.

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Justine

3 months ago

I'm a little confused - the question talks about "appropriate actions" like logging errors, but I'm not sure if the Data Validation transformation can handle that kind of custom logic. Maybe the Data Quality transformation would give me more flexibility there.

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Ernie

4 months ago

The Data Validation transformation sounds like the most straightforward option to me. It should allow me to set up the necessary checks and error handling without too much complexity.

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Meghan

4 months ago

Hmm, I'm a bit unsure on this one. The question mentions detecting missing data and duplicates, so I'm wondering if the Data Quality transformation might be a better fit since it has more advanced data profiling and cleansing capabilities.

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Ben

4 months ago

I think the Data Validation transformation is the way to go here. It's designed specifically for checking data quality and handling errors before loading into the data warehouse.

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Julianna

4 months ago

The Data Validation transformation sounds like it's the closest match to what the question is asking for. It allows you to check for missing or duplicate data, which is a key requirement. I'm feeling pretty confident about this one.

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Jacqueline

4 months ago

I'm a bit confused by the different transformation options. They all sound like they could potentially work for this scenario. I'll need to review the details of each one to make sure I select the most appropriate one.

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Maybelle

5 months ago

Okay, I think I've got this. The Data Quality transformation seems like the best choice here. It allows you to define data quality rules and take actions like logging errors when those rules are violated. That's exactly what the question is asking for.

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Zana

5 months ago

Hmm, I'm a bit unsure here. The question mentions detecting error conditions and taking appropriate actions, so I'm not sure if the Data Validation transformation is the best fit. I might need to look into the other options more closely.

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Serita

5 months ago

This looks like a straightforward data validation question. I'd go with option B - Data Validation to check for missing or duplicate data before loading into the data warehouse.

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