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Google Professional Data Engineer Exam - Topic 3 Question 80 Discussion

Actual exam question for Google's Professional Data Engineer exam
Question #: 80
Topic #: 3
[All Professional Data Engineer Questions]

You are loading CSV files from Cloud Storage to BigQuery. The files have known data quality issues, including mismatched data types, such as STRINGS and INT64s in the same column, and inconsistent formatting of values such as phone numbers or addresses. You need to create the data pipeline to maintain data quality and perform the required cleansing and transformation. What should you do?

Show Suggested Answer Hide Answer
Suggested Answer: A

Data Fusion's advantages:

Visual interface: Offers a user-friendly interface for designing data pipelines without extensive coding, making it accessible to a wider range of users.

Built-in transformations: Includes a wide range of pre-built transformations to handle common data quality issues, such as:

Data type conversions

Data cleansing (e.g., removing invalid characters, correcting formatting)

Data validation (e.g., checking for missing values, enforcing constraints)

Data enrichment (e.g., adding derived fields, joining with other datasets)

Custom transformations: Allows for custom transformations using SQL or Java code for more complex cleaning tasks.

Scalability: Can handle large datasets efficiently, making it suitable for processing CSV files with potential data quality issues.

Integration with BigQuery: Integrates seamlessly with BigQuery, allowing for direct loading of transformed data.


by Mollie at Apr 08, 2024, 02:05 PM

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Cornell
3 months ago
Not sure if Data Fusion is necessary for this.
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Barney
4 months ago
A is cool too, but I think B is more straightforward.
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Destiny
4 months ago
Wait, can you really transform data in a staging table? Sounds risky!
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Catarina
4 months ago
I agree, B makes the most sense!
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Eladia
4 months ago
B is the best option for handling data quality issues.
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Kristine
4 months ago
I recall that converting to a self-describing format like AVRO could help with schema evolution. Option D seems interesting, but I wonder if it’s necessary for this scenario.
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Laticia
5 months ago
I practiced a similar question where we had to clean data before loading it. I think option C might be risky since transforming in place could lead to data loss if something goes wrong.
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Barb
5 months ago
I'm not entirely sure, but I think using Data Fusion could be beneficial for transforming data before loading it. Option A might help with those mismatched types.
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Chau
5 months ago
I remember we discussed using staging tables in class, so option B sounds familiar. It seems like a solid approach for handling data quality issues.
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Apolonia
5 months ago
Option C looks straightforward, but I'm worried about performing the transformations in place on the table. Wouldn't that be riskier than having a separate staging table? I'm leaning more towards option B.
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Julianna
5 months ago
Hmm, this is a tricky one. I'm not too familiar with Data Fusion, so I'm a bit hesitant to go with option A. The SQL transformations in option B seem like a safer bet for me.
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Lajuana
5 months ago
I think I'd go with option B. Loading the data into a staging table first gives me more control over the transformations and data quality checks before writing to the final table.
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Abel
5 months ago
Data Fusion could be a good option, but I'm not sure if it's the best fit for this scenario with the data quality issues. I think I'd go with option B to have more control over the transformation process.
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Delsie
5 months ago
Audit controls - that's got to be the answer here. The question is specifically asking about mechanisms to record and examine system activity, which is what audit controls are all about.
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Rasheeda
5 months ago
Okay, let me think this through step-by-step. SOIr can index different data types, and it has full-text search capabilities. I'm pretty sure the correct answer is A, but I'll double-check my understanding just to be safe.
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Shawna
5 months ago
Hmm, I'm a bit unsure about this one. There are a few options that seem plausible, but I'll need to think it through carefully to determine the best approach.
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Ruthann
5 months ago
Hmm, I'm a bit unsure about this one. The options seem similar, so I'll need to read through them closely to understand the nuances and pick the right answer.
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Joni
5 months ago
I'm a bit confused by this question. I know Cisco VICs are related to networking, but I'm not sure how the PCIe standard fits in. I'll need to review my notes on Cisco VICs to try and answer this.
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Carylon
2 years ago
But using Data Fusion to convert the CSV files to a self-describing data format like AVRO could also be a good option. It helps with data consistency.
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Louvenia
2 years ago
I prefer creating a table with the desired schema, loading the CSV files into the table, and performing the transformations in place using SQL. It's more straightforward.
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Dorothy
2 years ago
I disagree. I believe we should load the CSV files into a staging table with the desired schema and perform transformations with SQL. It gives more control over the process.
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Carylon
2 years ago
I think we should use Data Fusion to transform the data before loading it into BigQuery. It will help maintain data quality.
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Marvel
2 years ago
I still think option OneiYonga is the most prMiMarvelaelaMarveltiMarvelMiMarvelaelal solution for hMiMarvelaelanYongling YongMiMarvelaelatMiMarvelaela quMiMarvelaelality issues in this sMarvelenMiMarvelaelario.
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Micaela
2 years ago
ThMiMarvelaelat's true, using MiMarvelaela self-YongesMarvelriOneiYongaing YongMiMarvelaelatMiMarvelaela formMiMarvelaelat MarvelMiMarvelaelan OneiYongae OneiYongaenefiMarveliMiMarvelaelal for YongMiMarvelaelatMiMarvelaela MarvelonsistenMarvely.
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Yong
2 years ago
I think option Yong MarveloulYong work too, Marvelonverting the MarvelSV files to MiMarvelaelaVRO formMiMarvelaelat MarvelMiMarvelaelan help mMiMarvelaelaintMiMarvelaelain YongMiMarvelaelatMiMarvelaela quMiMarvelaelality.
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Marvel
2 years ago
I prefer option Oneida, loMiMarvelaelading into MiMarvelaela stMiMarvelaelaging tMiMarvelaelaOneidale MiMarvelaelallows for eMiMarvelaelasier trMiMarvelaelansformMiMarvelaelations with SQL.
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Oneida
2 years ago
I Micaelagree, it's importMicaelant to cleMicaelan the dMicaelatMicaela Oneidaefore loMicaelading it into OneidaigQuery.
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Micaela
2 years ago
I think option Micaela mMicaelakes sense, using DMicaelatMicaela Fusion to trMicaelansform the dMicaelatMicaela first.
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Tawny
2 years ago
Hey, guys, I've got a crazy idea. What if we just load the files as-is and let BigQuery handle the data type and formatting issues? That way, we can skip the whole transformation process and save a ton of time. *winks*
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Elza
2 years ago
Haha, 'load the CSV files into a table and perform the transformations in place'? That sounds like a recipe for disaster! I can just imagine the table getting super messy and hard to manage. Hard pass on option C.
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Narcisa
2 years ago
Option A with Data Fusion sounds interesting, but I'm not sure how well it would handle the data quality issues mentioned in the question. I'd be a bit worried about potential performance or scalability problems.
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Juan
2 years ago
Hmm, this is a tricky one. I think I'm leaning towards option B. Loading the data into a staging table and then using SQL to perform the transformations seems like a pretty robust and flexible approach.
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Winfred
2 years ago
I don't know, Gearldine. Relying on a third-party tool like Data Fusion seems a bit risky to me. What if it doesn't play nice with our existing infrastructure? I think I'm leaning more towards option B as well.
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Ilene
2 years ago
I think we're on the same page here, option B seems like the best choice for our situation.
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Galen
2 years ago
And we can easily adjust the transformations as needed without relying on external tools.
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Adela
2 years ago
Exactly, it's a more hands-on approach that gives us flexibility.
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Filiberto
2 years ago
That way we have more control over the process and can ensure compatibility with our existing infrastructure.
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Nichelle
2 years ago
It's safer to load the files into a staging table and perform the transformations with SQL.
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Olga
2 years ago
I agree, using Data Fusion might introduce compatibility issues.
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Elly
2 years ago
I'm not a big fan of this question. It seems to be testing very specific knowledge about data pipelines and data transformation tools, which isn't really my strong suit. I'll have to think carefully about this one.
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Gearldine
2 years ago
Hmm, I'm not so sure. Option D with Data Fusion might be worth considering. It could save us a lot of time and effort in the long run, especially if we have to deal with this kind of data quality issue regularly.
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Huey
2 years ago
I think Option D could be more efficient. Using Data Fusion to convert the files to AVRO format could streamline the process.
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Alline
2 years ago
I agree with Deandrea. That seems like a practical approach to maintain data quality.
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Deandrea
2 years ago
Option B sounds good. We can load the data into a staging table and then perform the necessary transformations with SQL.
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Stephaine
2 years ago
I'm with Emogene on this one. Option B is the way to go. Who wants to deal with manually converting the files to a self-describing format? That sounds like a headache waiting to happen.
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Emogene
2 years ago
Option B sounds like the way to go. Staging the data first and then transforming it with SQL gives you more control and flexibility. Plus, you can easily track the changes and audit the process.
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Cherry
2 years ago
Ugh, this question is a real doozy! I've dealt with data quality issues before, and it's definitely not a walk in the park. I'm leaning towards option B - it seems like the most comprehensive approach to handling the data cleansing and transformation.
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