Exam Certified Machine Learning Professional topic 1 question 29 discussion

Actual exam question from Databricks's Certified Machine Learning Professional

Question #: 29
Topic #: 1

[All Certified Machine Learning Professional Questions]

A data scientist has created a Python function compute_features that returns a Spark DataFrame with the following schema:

The resulting DataFrame is assigned to the features_df variable. The data scientist wants to create a Feature Store table using features_df.
Which of the following code blocks can they use to create and populate the Feature Store table using the Feature Store Client fs?

A.
B.
C. features_df.write.mode("fs").path("new_table")
D.
E. features_df.write.mode("feature").path("new_table")

Show Suggested Answer

Suggested Answer: D 🗳️

by BokNinja at Dec. 19, 2023, 2:22 a.m.

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jn_uk

2 weeks, 3 days ago

Selected Answer: A

D is wrong because it doesn't reference the DataFrame schema. If you provide the df itself (like in answer A) you don't need to provide a schema.

upvoted 1 times

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Joy999

11 months, 3 weeks ago

create_table(name: str, primary_keys: Union[str, List[str]], df: Optional[pyspark.sql.dataframe.DataFrame] = None, *, timestamp_keys: Union[str, List[str], None] = None, partition_columns: Union[str, List[str], None] = None, schema: Optional[pyspark.sql.types.StructType] = None, description: Optional[str] = None, tags: Optional[Dict[str, str]] = None, **kwargs) → databricks.feature_store.entities.feature_table.FeatureTable Right Answer A

upvoted 2 times

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victorcolome

1 year, 5 months ago

Selected Answer: A

A is correct according to documentation https://docs.databricks.com/en/machine-learning/feature-store/workspace-feature-store/feature-tables.html#create-a-feature-table-in-databricks-feature-store.

upvoted 1 times

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BokNinja

1 year, 6 months ago

A is correct

upvoted 3 times

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