Snowflake
DSA-C02
Q1:
Which tools helps data scientist to manage ML lifecycle & Model versioning?
☐
A
MLFlow☐
B
Pachyderm☐
C
Albert☐
D
CRUX
Snowflake
DSA-C02
Q2:
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
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.○
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
Retrained your training dataset after correcting data outliers & no need to introduce new data.
Snowflake
DSA-C02
Q3:
Which metric is not used for evaluating classification models?
○
A
Recall○
B
Accuracy○
C
Mean absolute error○
D
Precision
Snowflake
DSA-C02
Q4:
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
MPP Python UDFs○
B
Scaler Python UDFs○
C
Vectorized Python UDFs○
D
Hybrid Python UDFs
Snowflake
DSA-C02
Q5:
Performance metrics are a part of every machine learning pipeline, Which ones are not the performance metrics used in the Machine learning?
○
A
R (R-Squared)○
B
Root Mean Squared Error (RMSE)○
C
AU-ROC○
D
AUM