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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A data scientist needs to calculate the cumulative moving average of sales for each product in a table. The table contains columns: (INT), (DATE), and (NUMBER). The desired output should include the product_id', 'sale_date', and Which of the following Snowflake SQL statements correctly calculates the cumulative moving average for each product using window functions?

A) SELECT product_id, sale_date, daily_sales, OVER (PARTITION BY product_id ORDER BY sale_date ASC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS cumulative_average FROM
B) SELECT product_id, sale_date, daily_sales, OVER (PARTITION BY product_id ORDER BY sale_date ASC) / OVER (PARTITION BY product_id ORDER BY sale_date ASC) AS cumulative_average FROM sales_by_day;
C) SELECT product_id, sale_date, daily_sales, AVG(daily_sales) OVER (ORDER BY sale_date ASC) AS cumulative_average FROM sales_by_day;
D) SELECT product_id, sale_date, daily_sales, AVG(daily_sales) OVER (PARTITION BY product_id) AS cumulative_average FROM sales_by_day;
E) SELECT product_id, sale_date, daily_sales, OVER (PARTITION BY product_id ORDER BY sale_date AS cumulative_average FROM sales_by_day;

2. You are developing a Snowflake Native App that leverages Snowflake Cortex for text summarization. The app needs to process user-provided text input in real-time and return a summarized version. You want to expose this functionality as a secure and scalable REST API endpoint within the Snowflake environment. Which of the following strategies are MOST suitable for achieving this, considering best practices for security and performance?

A) Develop a Snowflake Native App that includes a Java UDF that calls 'SNOWFLAKE.CORTEX.SUMMARIZE and expose a REST API using Snowflake's built-in REST API capabilities within the Native App framework.
B) Create a Snowflake External Function using Python that directly calls the 'SNOWFLAKE.CORTEX.SUMMARIZE' function and expose this function via a REST API gateway outside of Snowflake.
C) Utilize a Snowflake Stored Procedure written in SQL that invokes the 'SNOWFLAKE.CORTEX.SUMMARIZE' function, and then create a Snowflake API Integration to expose the stored procedure as a REST endpoint.
D) Develop a Snowflake Native App containing a Python UDF that calls 'SNOWFLAKCORTEX.SUMMARIZE function, and expose it as a REST API endpoint using Snowflake's API Integration feature within the app package.
E) Write a Snowflake Stored Procedure using Javascript to invoke the 'SNOWFLAKE.CORTEX.SUMMARIZE function, deploy the procedure to a Snowflake stage, and then trigger it via an AWS Lambda function integrated with Snowflake.

3. You are using Snowflake Cortex to perform sentiment analysis on customer reviews stored in a table called 'CUSTOMER REVIEWS' The table has a column containing the text of each review. You want to create a user-defined function (UDF) to extract sentiment score between the range of -1 to 1 using the 'snowflake_cortex.sentiment' function in Snowflake Cortex. Which of the following UDF definitions would correctly implement this, allowing it to be called directly on the column?

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

4. You are using Snowflake ML to train a binary classification model. After training, you need to evaluate the model's performance. Which of the following metrics are most appropriate to evaluate your trained model, and how do they differ in their interpretation, especially when dealing with imbalanced datasets?

A) Precision, Recall, F I-score, AUC-ROC, and Log Loss: Precision focuses on the accuracy of positive predictions; Recall focuses on the completeness of positive predictions; Fl-score balances Precision and Recall; AUC-ROC evaluates the separability of classes and Log Loss quantifies the accuracy of probabilities, especially valuable for imbalanced datasets because they provide a more nuanced view of performance than accuracy alone.
B) Accuracy: It measures the overall correctness of the model. Precision: It measures the proportion of positive identifications that were actually correct. Recall: It measures the proportion of actual positives that were identified correctly. Fl-score: It is the harmonic mean of precision and recall.
C) Confusion Matrix: A table that describes the performance of a classification model by showing the counts of true positive, true negative, false positive, and false negative predictions. This isnt a metric but representation of the metrics.
D) Mean Squared Error (MSE): The average squared difference between the predicted and actual values. R-squared: Represents the proportion of variance in the dependent variable that is predictable from the independent variables. These are great for regression tasks.
E) AUC-ROC: Measures the ability of the model to distinguish between classes. It is less sensitive to class imbalance than accuracy. Log Loss: Measures the performance of a classification model where the prediction input is a probability value between 0 and 1.

5. A data scientist is building a churn prediction model using Snowflake data'. They want to load a large dataset (50 million rows) from a Snowflake table 'customer_data' into a Pandas DataFrame for feature engineering. They are using the Snowflake Python connector. Given the code snippet below and considering performance and memory usage, which approach would be the most efficient for loading the data into the Pandas DataFrame? Assume you have a properly configured connection and cursor 'cur'. Furthermore, assume that the 'customer id' column is the primary key and uniquely identifies each customer. You are also aware that network bandwidth limitations exist within your environment. ```python import snowflake.connector import pandas as pd # Assume conn and cur are already initialized # conn = snowflake.connector.connect(...) # cur = conn.cursor() query = "SELECT FROM customer data```

A) ```python with conn.cursor(snowflake.connector.DictCursor) as cur: cur.execute(query) df = pd.DataFrame(cur.fetchall())
B) ```python cur.execute(query) df = pd.read_sql(query, conn)
C) ```python cur.execute(query) df = pd.DataFrame(cur.fetchall(), columns=[col[0] for col in cur.description])
D) ```python cur.execute(query) results = cur.fetchmany(size=1000000) df_list = 0 while results: df_list.append(pd.DataFrame(results, for col in cur.description])) results = cur.fetchmany(size=1000000) df = pd.concat(df_list, ignore_index=True)
E) ```python import snowflake.connector import pandas as pd import pyarrow import pyarrow.parquet # Enable Arrow result format conn.cursor().execute("ALTER SESSION SET PYTHON USE ARROW RESULT FORMAT-TRUE") cur.execute(query) df =

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