Snowflake SnowPro Advanced Data Scientist - SnowPro Advanced Data Scientist DSA-C03 Exam
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Total 87 questions
Question #11 (Topic: Exam A)
How can the use of confidence intervals in data analysis help a Data Scientist interpret a model? (Choose three.)
A. They identify unimportant or redundant columns that can be dropped.
B. They show how each independent variable is related to other independent variables.
C. They are most often constructed using confidence levels of 5% or 95%.
D. They are used in hypothesis testing and regression analysis.
E. They measure the degree of uncertainty or certainty in a sampling method.
F. They display the probability that a parameter will fall between a pair of values around the mean.
Answer: DEF
Question #12 (Topic: Exam A)
A Data Scientist has trained a scikit-learn model called cf.
Which snowflake.ml.registry Python code will register the model into the Snowpark Model Registry?
Which snowflake.ml.registry Python code will register the model into the Snowpark Model Registry?
A. mv = reg.1og_model(model_name=“my_model”, version_name=“v1”, conda_dependencies = [“scikit-learn”], comment=“My awesome ML model”, metrics= (“score”: 96}, sample_input_data=train_features)
B. mv = reg.1og_model(clf, model_name="my model", version_name="v1", conda_dependencies=[“scikit-learn"], comment="My awesome ML model", metrics=("score": 96})
C. mv = reg.log_model(clf, version_name="v1", conda_dependencies=["scikit-learn"], comment= “My awesome ML model”, metrics={"score": 96}, sample_input_data=train_features)
D. m v= reg.1og_model(clf, model_name=“my_model”, version_name=“v1”, conda_dependencies= [“scikit-learn”], comment=“My awesome ML model”, metrics={“score”: 96}, sample_input_data=train features)
Answer: C
Question #13 (Topic: Exam A)
A Data Scientist is creating a Jupyter notebook to do model development. The Data Scientist wants to connect to Snowflake using the Python Connector and create a pandas DataFrame using data queried from a table called SALES.
Which code will successfully execute and populate the DataFrame?
Which code will successfully execute and populate the DataFrame?
A. 
B. 
C. 
D. 
B.
C.
D.
Answer: A
Question #14 (Topic: Exam A)
Which use case is MOST suitable for supervised learning?
A. Creating new art and music for a marketing campaign.
B. Creating a propensity to spend model with historical data.
C. Identifying customer segmentation for a marketing strategy.
D. Identifying defective machine parts for preventative maintenance.
Answer: B
Question #15 (Topic: Exam A)
A Data Scientist needs to make an initial exploration of new data using pandas.
What is the MOST efficient way to load the data into a pandas DataFrame?
What is the MOST efficient way to load the data into a pandas DataFrame?
A. Unload the data to a file on a local machine and use Snowpark to analyze the data with pandas.
B. Unload the data to a file on a local machine and use a Jupyter Notebook running on a local machine to analyze the data with pandas.
C. Unload the data to a file in an external stage and access the file through a third-party tool on the external stage.
D. Use a Snowflake Notebook in Snowsight to analyze the data with pandas.
Answer: D
