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Google Professional Data Engineer Exam - Topic 3 Question 85 Discussion

You operate a database that stores stock trades and an application that retrieves average stock price for a given company over an adjustable window of time. The data is stored in Cloud Bigtable where the datetime of the stock trade is the beginning of the row key. Your application has thousands of concurrent users, and you notice that performance is starting to degrade as more stocks are added. What should you do to improve the performance of your application?
A) Change the row key syntax in your Cloud Bigtable table to begin with the stock symbol.
B) Change the row key syntax in your Cloud Bigtable table to begin with a random number per second.
C) Change the data pipeline to use BigQuery for storing stock trades, and update your application.
D) Use Cloud Dataflow to write summary of each day's stock trades to an Avro file on Cloud Storage. Update your application to read from Cloud Storage and Cloud Bigtable to compute the responses.

Google Professional Data Engineer Exam - Topic 3 Question 85 Discussion

Actual exam question for Google's Professional Data Engineer exam
Question #: 85
Topic #: 3
[All Professional Data Engineer Questions]

You operate a database that stores stock trades and an application that retrieves average stock price for a given company over an adjustable window of time. The data is stored in Cloud Bigtable where the datetime of the stock trade is the beginning of the row key. Your application has thousands of concurrent users, and you notice that performance is starting to degrade as more stocks are added. What should you do to improve the performance of your application?

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 Yolando at Jun 02, 2024, 11:48 AM

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Nicolette
9 months ago
Wait, why would we switch to Cloud Storage? That seems unnecessary.
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Douglass
9 months ago
Definitely agree on needing to optimize the row key structure!
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Felicitas
10 months ago
Not sure about that random number idea, sounds risky.
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Anabel
10 months ago
I think using BigQuery might be a better long-term solution.
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Irene
10 months ago
Changing the row key to stock symbol could help with performance.
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Hildegarde
10 months ago
I vaguely recall that using Cloud Dataflow for summarizing data might improve efficiency, but I'm not entirely clear on how it integrates with Bigtable for real-time queries.
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Makeda
11 months ago
I practiced a similar question about optimizing database performance, and I feel like using BigQuery could be a solid choice for handling large datasets.
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Britt
11 months ago
I think changing the row key to a random number could help with write performance by avoiding hotspots, but it might complicate querying.
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Ollie
11 months ago
I remember we discussed row key design in Cloud Bigtable, and starting with the stock symbol could help with read performance, but I'm not sure if it's the best option here.
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Darrin
11 months ago
I'm a bit confused by all the options, but I think I'll start by sketching out the pros and cons of each one to help me decide.
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Mable
11 months ago
The Dataflow and Cloud Storage approach in option D seems like it could work, but I'm worried about the added complexity. I'll have to weigh the pros and cons.
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Julieta
11 months ago
Option C looks promising, using BigQuery could scale better, but I'll need to research the migration process and impact on the application.
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Ellsworth
11 months ago
Hmm, changing the row key syntax could help with querying, but I'm not sure if that's the best long-term solution. I'll have to think this through.
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Ellsworth
11 months ago
This seems like a tricky performance issue. I'll need to carefully consider the trade-offs of each option.
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Kaycee
11 months ago
Hmm, I'm a bit unsure about this one. I'm not entirely familiar with CPV codes and their purpose in the tender process. I'll have to think it through carefully.
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Katheryn
11 months ago
Hmm, I'm a bit unsure about this one. I know we covered resource controls in class, but I can't quite remember the specific command that would fetch the information we need here. I'll have to think this through carefully.
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Chan
11 months ago
Okay, let's see. According to NIST, the two elements missing to calculate the risk assessment are asset vulnerability assessment and malware analysis report. I'm confident in this answer.
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Mireya
1 year ago
Wait, you're telling me the row key isn't already 'STONKS'? I'm shocked, shocked I tell you!
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Mari
1 year ago
D) Use Cloud Dataflow to write summary of each day's stock trades to an Avro file on Cloud Storage. Update your application to read from Cloud Storage and Cloud Bigtable to compute the responses.
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Audra
1 year ago
C) Change the data pipeline to use BigQuery for storing stock trades, and update your application.
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Taryn
1 year ago
B) Change the row key syntax in your Cloud Bigtable table to begin with a random number per second.
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Ahmed
1 year ago
A) Change the row key syntax in your Cloud Bigtable table to begin with the stock symbol.
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Whitney
1 year ago
D) Combining Cloud Dataflow, Cloud Storage, and Cloud Bigtable sounds like a lot of moving parts. Might be overkill for this use case.
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Clarence
1 year ago
A) Change the row key syntax in your Cloud Bigtable table to begin with the stock symbol.
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Stephanie
1 year ago
D) Combining Cloud Dataflow, Cloud Storage, and Cloud Bigtable sounds like a lot of moving parts. Might be overkill for this use case.
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Delpha
1 year ago
A) Change the row key syntax in your Cloud Bigtable table to begin with the stock symbol.
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Kassandra
1 year ago
C) Using BigQuery for storing the stock trades and updating the application seems like a reasonable approach. It could help handle the increased data volume and concurrency.
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Arlen
1 year ago
D) Use Cloud Dataflow to write summary of each day's stock trades to an Avro file on Cloud Storage. Update your application to read from Cloud Storage and Cloud Bigtable to compute the responses.
upvoted 0 times
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Coral
1 year ago
C) Using BigQuery for storing the stock trades and updating the application seems like a reasonable approach. It could help handle the increased data volume and concurrency.
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Alva
1 year ago
A) Change the row key syntax in your Cloud Bigtable table to begin with the stock symbol.
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Elke
1 year ago
I see both points, but I think option C) changing the data pipeline to use BigQuery might be the most efficient solution in the long run.
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Adelina
1 year ago
B) A random number per second? That's just crazy! How are we supposed to query that efficiently? Definitely not the way to go.
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Emeline
1 year ago
User 2: That sounds like a good idea, it would definitely help with querying efficiently.
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Regenia
1 year ago
User 1: I think we should change the row key syntax to begin with the stock symbol.
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Ammie
1 year ago
A) Changing the row key syntax to start with the stock symbol sounds like a good idea. That way, we can group all the trades for a single stock together and improve query performance.
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Jaime
1 year ago
I disagree, I believe option D) using Cloud Dataflow to write summary of each day's stock trades to an Avro file is the best approach.
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Tiera
1 year ago
I think we should go with option A) Change the row key syntax to begin with the stock symbol.
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