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Databricks Machine Learning Professional Exam - Topic 1 Question 3 Discussion

A machine learning engineer has developed a random forest model using scikit-learn, logged the model using MLflow as random_forest_model, and stored its run ID in the run_id Python variable. They now want to deploy that model by performing batch inference on a Spark DataFrame spark_df.Which of the following code blocks can they use to create a function called predict that they can use to complete the task?A)B)It is not possible to deploy a scikit-learn model on a Spark DataFrame.C)D)E)
D) Option D
A) Option A
B) Option B
C) Option C
E) Option E

Databricks Machine Learning Professional Exam - Topic 1 Question 3 Discussion

Actual exam question for Databricks's Databricks Machine Learning Professional exam
Question #: 3
Topic #: 1
[All Databricks Machine Learning Professional Questions]

A machine learning engineer has developed a random forest model using scikit-learn, logged the model using MLflow as random_forest_model, and stored its run ID in the run_id Python variable. They now want to deploy that model by performing batch inference on a Spark DataFrame spark_df.

Which of the following code blocks can they use to create a function called predict that they can use to complete the task?

A)

B)

It is not possible to deploy a scikit-learn model on a Spark DataFrame.

C)

D)

E)

Show Suggested Answer Hide Answer
Suggested Answer: D

by Jerry at Dec 14, 2023, 02:02 PM

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Myra
9 months ago
Not sure about D, looks a bit complicated for this task.
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Leeann
9 months ago
I prefer Option C, seems more straightforward.
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Val
9 months ago
Surprised to see B as an option, that can't be right!
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Joni
10 months ago
I think Option B is incorrect, scikit-learn models can work with Spark.
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Alva
10 months ago
Option A looks solid for batch inference!
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Kirk
10 months ago
I’m a bit confused about the images in options C, D, and E. I wish I could recall what they contained!
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Antonio
10 months ago
I practiced a similar question where we had to load a model from MLflow. I feel like option A might be the right choice here.
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Audrie
10 months ago
I think option B is wrong because we can deploy scikit-learn models on Spark DataFrames, right?
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Royal
11 months ago
I remember we discussed how to deploy scikit-learn models with Spark, but I’m not sure if it was specifically about random forests.
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Celestine
11 months ago
I'm leaning towards Option D, but I want to make sure I understand the process of converting the scikit-learn model to a Spark UDF. That might be the key to solving this problem.
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Denny
11 months ago
I'm a bit confused here. Option B says it's not possible, but the question seems to imply that we can deploy the scikit-learn model on Spark. I'll need to think this through carefully.
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Kristal
11 months ago
Okay, let's see. Option A looks promising, but I'm not sure if that's the right way to do it. I'll need to double-check the syntax and make sure it's compatible with the given setup.
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Gail
11 months ago
Hmm, this looks like a tricky one. I'll need to carefully read through the options and think about how to deploy a scikit-learn model on a Spark DataFrame.
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Renay
11 months ago
Aha, Option C looks like it might be the solution! I'll need to verify that the MLflow integration and the Spark DataFrame usage are correct, but this seems like the most straightforward approach.
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Daron
11 months ago
I'm not sure about this one. I'll need to eliminate the options that don't sound right and then make an educated guess.
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Shawna
11 months ago
I'm leaning towards option B being the wrong one. My notes mentioned that HDHPs might actually have lower premiums compared to traditional plans!
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Nobuko
11 months ago
Okay, I think I've got this. Assigning to the OU is the recommended method because it saves the admin a ton of time and effort compared to manually adding the app to each user.
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