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?

Adf = fs.get_missing_features(spark_df, model_uri)
fs.score_model(model_uri, df)

Bfs.score_model(model_uri, spark_df)

Cdf = fs.get_missing_features(spark_df, model_uri)
fs.score_batch(model_uri, df)
df = fs.get_missing_features(spark_df)

Dfs.score_batch(model_uri, df)

Efs.score_batch(model_uri, spark_df)

Show Suggested Answer

Suggested Answer: E

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

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Skye

3 months ago

E looks good, but I'm not sure if it handles the missing data properly.

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Val

3 months ago

D is definitely wrong, it doesn't address the missing features.

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Alida

3 months ago

Wait, can we really use C? It looks like it calls get_missing_features twice.

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Filiberto

4 months ago

I think B is too simple, it won't work without the missing features.

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Elouise

4 months ago

Option A seems right, it fetches missing features first.

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Jesus

4 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

4 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

4 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

5 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

5 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

5 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

5 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

5 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

5 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

5 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

5 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

5 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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