DSA-C02 무료 덤프문제 온라인 액세스

Consider a data frame df with 10 rows and index [ 'r1', 'r2', 'r3', 'row4', 'row5', 'row6', 'r7', 'r8', 'r9', 'row10'].
What does the expression g = df.groupby(df.index.str.len()) do?

• A.Data frames cannot be grouped by index values. Hence it results in Error.
• B.Groups df based on length of each index value
• C.Groups df based on index strings
• D.Groups df based on index values

You previously trained a model using a training dataset. You want to detect any data drift in the new data collected since the model was trained.
What should you do?

• A.Retrained your training dataset after correcting data outliers & no need to introduce new data.
• B.Create a new version of the dataset using only the new data and retrain the model.
• C.Add the new data to the existing dataset and enable Application Insights for the service where the model is deployed.
• D.Create a new dataset using the new data and a timestamp column and create a data drift monitor that uses the training dataset as a baseline and the new dataset as a target.

Which type of Python UDFs let you define Python functions that receive batches of input rows as Pandas DataFrames and return batches of results as Pandas arrays or Series?

• A.Hybrid Python UDFs
• B.Scaler Python UDFs
• C.MPP Python UDFs
• D.Vectorized Python UDFs

Data Scientist used streams in ELT (extract, load, transform) processes where new data inserted in-to a staging table is tracked by a stream. A set of SQL statements transform and insert the stream contents into a set of production tables. Raw data is coming in the JSON format, but for analysis he needs to transform it into relational columns in the production tables. which of the following Data transformation SQL function he can used to achieve the same?

• A.lateral flatten()
• B.METADATA$ACTION ()
• C.Transpose()
• D.He could not apply Transformation on Stream table data.

Select the correct mappings:
I. W Weights or Coefficients of independent variables in the Linear regression model --> Model Pa-rameter II. K in the K-Nearest Neighbour algorithm --> Model Hyperparameter III. Learning rate for training a neural network --> Model Hyperparameter IV. Batch Size --> Model Parameter

• A.I,II
• B.I,II,III
• C.III,IV
• D.II,III,IV