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Databricks Exam Databricks Certified Data Analyst Associate Topic 5 Question 21 Discussion

Actual exam question for Databricks's Databricks Certified Data Analyst Associate exam
Question #: 21
Topic #: 5
[All Databricks Certified Data Analyst Associate Questions]

A data engineering team has created a Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables. The microbatches are triggered every minute.

A data analyst has created a dashboard based on this gold-level data. The project stakeholders want to see the results in the dashboard updated within one minute or less of new data becoming available within the gold-level tables.

Which of the following cautions should the data analyst share prior to setting up the dashboard to complete this task?

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Suggested Answer: A

A Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables every minute requires a high level of compute resources to handle the frequent data ingestion, processing, and writing. This could result in a significant cost for the organization, especially if the data volume and velocity are large. Therefore, the data analyst should share this caution with the project stakeholders before setting up the dashboard and evaluate the trade-offs between the desired refresh rate and the available budget. The other options are not valid cautions because:

B) The gold-level tables are assumed to be appropriately clean for business reporting, as they are the final output of the data engineering pipeline. If the data quality is not satisfactory, the issue should be addressed at the source or silver level, not at the gold level.

C) The streaming data is an appropriate data source for a dashboard, as it can provide near real-time insights and analytics for the business users. Structured Streaming supports various sources and sinks for streaming data, including Delta Lake, which can enable both batch and streaming queries on the same data.

D) The streaming cluster is fault tolerant, as Structured Streaming provides end-to-end exactly-once fault-tolerance guarantees through checkpointing and write-ahead logs. If a query fails, it can be restarted from the last checkpoint and resume processing.

E) The dashboard can be refreshed within one minute or less of new data becoming available in the gold-level tables, as Structured Streaming can trigger micro-batches as fast as possible (every few seconds) and update the results incrementally. However, this may not be necessary or optimal for the business use case, as it could cause frequent changes in the dashboard and consume more resources.Reference:Streaming on Databricks,Monitoring Structured Streaming queries on Databricks,A look at the new Structured Streaming UI in Apache Spark 3.0,Run your first Structured Streaming workload


by Lindsey at Aug 26, 2024, 09:12 AM

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Fletcher
2 days ago
Totally agree, that can add up fast!
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Paulina
8 days ago
The required compute resources could be costly.
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Daryl
14 days ago
I vaguely recall someone mentioning that dashboards might have refresh rate limitations. If they can't refresh quickly enough, it could be a problem for stakeholders expecting real-time data.
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German
19 days ago
I feel like we went over the limitations of using streaming data for dashboards. It might not always be the best choice, especially if the data isn't stable.
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Elina
24 days ago
I'm not entirely sure, but I think we talked about the importance of data cleanliness for reporting. If the gold-level tables aren't clean, it could lead to misleading insights.
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Rosamond
1 month ago
I remember we discussed the cost implications of compute resources in one of our practice sessions. It could definitely be a concern if the dashboard needs real-time updates.
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Latrice
1 month ago
The one-minute refresh requirement for the dashboard is really tight. I'm not sure if that's actually feasible, even with a well-designed streaming pipeline. I'll need to research the technical limitations around that.
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Lashonda
1 month ago
The streaming data seems like an appropriate source for the dashboard, but the fault tolerance of the streaming cluster is an interesting point. I'll need to assess the reliability and resilience of the pipeline.
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Nada
1 month ago
I'm not sure about the gold-level tables being "not appropriately clean." That seems like a vague concern. I'd want to understand more about what the data quality issues might be.
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Aliza
1 month ago
This seems like a tricky question. I'll need to carefully consider the options and think through the potential issues with the streaming pipeline and dashboard requirements.
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Terry
1 month ago
The compute resources could definitely be a concern if the dashboard needs to be updated that quickly. I'll need to look into the cost implications and see if there are any ways to optimize the infrastructure.
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Cory
1 month ago
Okay, let me walk through this step-by-step. Schedule 3 drugs are controlled substances, so there are likely some extra precautions needed when ordering them. The options mention a standard invoice, credit memo, and guaranteed funds, but the DEA Form 222 seems like the most likely answer since it's the official form for these types of orders.
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Raymon
1 year ago
Ah, the classic 'dashboard can't refresh that fast' dilemma. Option E is the obvious choice, but who wants to be the bearer of bad news?
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Veronika
1 year ago
E) The dashboard cannot be refreshed that quickly
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Arminda
1 year ago
B) The gold-level tables are not appropriately clean for business reporting
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Janae
1 year ago
A) The required compute resources could be costly
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Skye
1 year ago
Yeah, it's important to manage expectations with the stakeholders.
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Judy
1 year ago
We should consider the fact that the dashboard cannot be refreshed that quickly.
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Kattie
1 year ago
That's a good point, Bev. It's important to ensure the data source is suitable for the dashboard's requirements.
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Lashaunda
1 year ago
Option D has got to be the winner. Fault tolerance is key when you're dealing with mission-critical data.
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Desiree
1 year ago
Definitely, we can't afford to lose data or have downtime in a streaming pipeline.
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Amber
1 year ago
Option D has got to be the winner. Fault tolerance is key when you're dealing with mission-critical data.
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Bev
1 year ago
But what about the streaming data not being an appropriate source for a dashboard? Could that also be a caution to consider?
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Lorrine
1 year ago
I agree with Kattie. It's important to consider the cost implications before setting up the dashboard.
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Kattie
1 year ago
I think the caution the data analyst should share is that the required compute resources could be costly.
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Anjelica
1 year ago
Hold up, Option C is making a lot of sense. Streaming data for a dashboard? Sounds like a recipe for disaster to me.
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Justine
1 year ago
I'm not sure the gold-level tables are ready for prime time. Option B might be the prudent choice here.
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Gail
1 year ago
C: Maybe we should also check if the gold-level tables are clean enough for business reporting
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Ashanti
1 year ago
B: I agree, we should consider the cost implications before proceeding
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Glendora
1 year ago
A: The required compute resources could be costly
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Aaron
1 year ago
Option A is the way to go. Costly compute resources are a small price to pay for real-time business insights, am I right?
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Hailey
1 year ago
Absolutely, we want to provide timely updates to the stakeholders, but we also need to consider the financial implications.
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Delpha
1 year ago
It's a balance between the value of real-time data and the cost of resources. We need to make sure it's worth it for the stakeholders.
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Gilberto
1 year ago
I agree, we need to ensure that the benefits of real-time updates outweigh the potential costs of compute resources.
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Leonor
1 year ago
Option A is definitely important to consider. Real-time insights are valuable, but we need to be mindful of costs.
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