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Databricks Machine Learning Professional Exam - Topic 1 Question 1 Discussion

Actual exam question for Databricks's Databricks Machine Learning Professional exam
Question #: 1
Topic #: 1
[All Databricks Machine Learning Professional Questions]

A machine learning engineer has developed a model and registered it using the FeatureStoreClient fs. The model has model URI model_uri. The engineer now needs to perform batch inference on customer-level Spark DataFrame spark_df, but it is missing a few of the static features that were used when training the model. The customer_id column is the primary key of spark_df and the training set used when training and logging the model.

Which of the following code blocks can be used to compute predictions for spark_df when the missing feature values can be found in the Feature Store by searching for features by customer_id?

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Suggested Answer: E

by Raina at Dec 12, 2023, 01:24 AM

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Skye
6 months ago
E looks good, but I'm not sure if it handles the missing data properly.
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Val
6 months ago
D is definitely wrong, it doesn't address the missing features.
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Alida
6 months ago
Wait, can we really use C? It looks like it calls get_missing_features twice.
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Filiberto
7 months ago
I think B is too simple, it won't work without the missing features.
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Elouise
7 months ago
Option A seems right, it fetches missing features first.
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Jesus
7 months ago
I lean towards option E because it directly scores the DataFrame, but I'm not completely confident about whether it handles missing features correctly.
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Kristofer
7 months ago
I feel like we practiced something similar, but I can't recall if `fs.score_model` or `fs.score_batch` is the right function to use here.
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Zena
7 months ago
I think option A sounds familiar because it mentions getting missing features first, which seems like a logical step.
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Phillip
8 months ago
I remember we discussed how to handle missing features in our training sessions, but I'm not sure if we should use `get_missing_features` before scoring.
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Omega
8 months ago
This is a tricky one. I'm not sure if I should be using `spark_df` or the DataFrame returned from `fs.get_missing_features()`. I'll need to think this through carefully before selecting an answer.
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Lettie
8 months ago
Okay, I've got a plan. I'll use `fs.get_missing_features()` to fetch the missing features from the Feature Store, then pass that DataFrame to `fs.score_batch()` to get the predictions. Seems like the most robust way to handle this.
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Alaine
8 months ago
Hmm, I'm a bit confused about the difference between `fs.score_model()` and `fs.score_batch()`. I'll need to double-check the documentation to make sure I understand which one is appropriate for this scenario.
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Frank
8 months ago
This question seems straightforward. I think I'll go with option C - it looks like the most comprehensive approach to handling the missing features.
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Gabriele
8 months ago
I think option C is the way to go. Fetching the missing features and then scoring the batch seems like the most complete solution to the problem. I'm feeling confident about this one.
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Georgeanna
8 months ago
Option C sounds promising - attaching QoS settings directly to device profiles could be an efficient way to prioritize voice traffic.
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Mozell
8 months ago
I'm a bit stuck on this one. The question is asking about the relationship between vendor-specific security and service autonomy, but I'm not totally clear on the implications. I'll have to review my notes.
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Elouise
8 months ago
I think it makes sense to add it in the Primary zone since it's where the registration failures happened. That's what we practiced in our last session.
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