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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A data science team is evaluating different methods for summarizing lengthy customer support tickets using Snowflake Cortex. The goal is to generate concise summaries that capture the key issues and resolutions. Which of the following approaches is/are appropriate for achieving this goal within Snowflake, considering the need for efficiency, cost-effectiveness, and scalability? (Select all that apply)

A) Using the 'SNOWFLAKE.ML.PREDICT' function with a summarization task-specific model provided by Snowflake Cortex, passing the full ticket text as input to generate a summary.
B) Developing a Python UDF that leverages a pre-trained summarization model from a library like 'transformers' and deploying it in Snowflake. Managing the model loading and inference within the UDF.
C) Employing a SQL-based approach using string manipulation functions and keyword extraction techniques to identify important sentences and concatenate them to form a summary.
D) Creating a custom summarization model using a transformer-based architecture like BART or T5, training it on a large dataset of support tickets and summaries within Snowflake using Snowpark ML, and then deploying this custom model for generating summaries via a UDF.
E) Calling the Snowflake Cortex 'COMPLETE' endpoint with a detailed prompt that instructs the model to summarize the support ticket, explicitly specifying the desired summary length and format.

2. You have a Snowflake Model Registry set up and are managing multiple versions of a machine learning model. You want to programmatically retrieve a specific version of the model and load it for inference within a Snowflake Snowpark Python UDE Assume your registry name is 'my_registry', the model name is 'credit risk_model', and you want to retrieve version 'v2'. How would you achieve this using Snowpark Python?

A) Option B
B) Option A
C) Option E
D) Option D
E) Option C

3. You are working with a Snowflake table named 'sensor readingS containing IoT sensor data'. The table has columns 'sensor id' , 'timestamp' , and 'reading value'. You observe that the 'reading value' column contains a significant number of missing values (represented as NULL). To prepare this data for a time series analysis, you need to impute these missing values. You have decided to use the 'LOCF' (Last Observation Carried Forward) method, filling the NULL values with the most recent non-NULL value for each sensor. In addition to LOCF, you also want to handle the scenario where a sensor has NULL values at the beginning of its data stream (i.e., no previous observation to carry forward). For these initial NULLs, you want to use a fixed default value of 0. Which of the following approaches, using either Snowpark for Python or a combination of Snowpark and SQL, correctly implements this LOCF imputation with a default value?

A) All of the above
B)

C)

D)

E)

4. A data scientist needs to analyze website session data stored in a Snowflake table named 'WEB SESSIONS'. The table contains columns like 'SESSION D', 'USER_ID, 'PAGE_VIEWS', 'TIME SPENT_SECONDS', and 'TIMESTAMP. They want to identify potential bot traffic by analyzing the correlation between 'PAGE VIEWS' and 'TIME SPENT SECONDS'. Which of the following Snowflake SQL queries is the MOST efficient and statistically sound way to calculate the Pearson correlation coefficient between these two columns, handling potential NULL values appropriately?

A) Option B
B) Option A
C) Option E
D) Option D
E) Option C

5. You are building a fraud detection model in Snowflake using Snowpark Python. You want to evaluate the model's performance, particularly focusing on identifying instances of fraud (minority class). Which combination of metrics provides the most comprehensive assessment for this imbalanced classification problem within the Snowflake environment, considering the need to minimize both false positives (legitimate transactions flagged as fraudulent) and false negatives (fraudulent transactions missed)?

A) Precision, Recall, and Fl-score.
B) Precision and Fl-score.
C) Accuracy and ROC AUC.
D) Accuracy and Recall.
E) ROC AUC and Recall.

Solutions: