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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q89-Q94):

NEW QUESTION # 89
You are using Snowpark to build a collaborative filtering model for product recommendations. You have a table 'USER_ITEM INTERACTIONS with columns 'USER ID', 'ITEM ID', and 'INTERACTION TYPE'. You want to create a sparse matrix representation of this data using Snowpark, suitable for input into a matrix factorization algorithm. Which of the following code snippets best achieves this while efficiently handling large datasets within Snowflake?

Answer: D

Explanation:
Option B is the most efficient and scalable approach. It utilizes Snowpark's 'pivot' function for efficient aggregation and sparse matrix creation directly within Snowflake's engine. Option A pulls the entire dataset into pandas, which is inefficient for large datasets. Options C and D are not complete as option C does not correctly populate values in the sparse matrix while creating empty matrix without any values , and D attempts a cross join which can lead to extreme performance issues. Option E, although similar to B, is incorrect because the pivot() function does not accept the values parameter. It's already present in agg function.

NEW QUESTION # 90
You have built a customer churn prediction model using Snowflake ML and deployed it as a Python stored procedure. The model outputs a churn probability for each customer. To assess the model's stability and potential business impact, you need to estimate confidence intervals for the average churn probability across different customer segments. Which of the following approaches is MOST appropriate for calculating these confidence intervals, considering the complexities of deploying and monitoring models within Snowflake?

A. Pre-calculate confidence intervals during model training and store them as metadata alongside the model in Snowflake. This avoids runtime computation.
B. Use a separate SQL query to extract the churn probabilities and customer segment information from the table where the stored procedure writes its output. Then, use a statistical programming language like Python (outside of Snowflake) to calculate the confidence intervals for each segment.
C. Calculate confidence intervals directly within the Python stored procedure using bootstrapping techniques and appropriate libraries (e.g., scikit-learn) before returning the churn probability.
D. Calculate a single confidence interval for the overall average churn probability across all customers. Customer segmentation confidence intervals are statistically invalid and not applicable for Snowflake ML models.
E. Implement a custom SQL function to approximate confidence intervals based on the Central Limit Theorem, assuming the churn probabilities are normally distributed.

Answer: B

Explanation:
The most appropriate approach is to extract the data and perform the confidence interval calculations outside of the stored procedure using a dedicated statistical environment. Options A and D are less scalable and efficient within the stored procedure. Option B provides insufficient information. Option E is not feasible for dynamic calculation based on changing data.

NEW QUESTION # 91
You are building a fraud detection model using transaction data stored in Snowflake. The dataset includes features like transaction amount, merchant category, location, and time. Due to regulatory requirements, you need to ensure personally identifiable information (PII) is handled securely and compliantly during the data collection and preprocessing phases. Which of the following combinations of Snowflake features and techniques would be MOST suitable for achieving this goal?

A. Apply differential privacy techniques on aggregated data derived from the transaction data, before using it for model training. Combine this with Snowflake's row access policies to restrict access to sensitive transaction records based on user roles and data attributes.
B. Encrypt the entire database containing the transaction data to protect PII from unauthorized access.
C. Use Snowflake's masking policies to redact PII columns before any data is accessed for model training. Ensure role-based access control is configured so that only authorized personnel can access the unmasked data for specific purposes.
D. Create a view that selects only the non-PII columns for model training. Grant access to this view to the data science team.
E. Use Snowflake's data sharing capabilities to share the transaction data with a third-party machine learning platform for model development, without any PII masking or redaction.

Answer: A,C

Explanation:
Options A and E are the MOST suitable. Option A directly addresses PII protection by leveraging Snowflake's masking policies to redact sensitive data before it is used for model training. Role-based access control provides an additional layer of security by limiting access to the unmasked data. Option E applies differential privacy to protect individual transaction data while still enabling useful model training and combines it with Row Access policies to restrict access to sensitive transaction records. Option B is partially correct but insufficient, as it only addresses which columns are seen, not protection within those columns. Option C protects the entire database but doesn't address PII handling during model training. Option D is highly risky and non-compliant, as it exposes PII to a third party without adequate protection.

NEW QUESTION # 92
You have a table 'PRODUCT SALES in Snowflake with columns: 'PRODUCT (INT), 'SALE_DATE (DATE), 'SALES_AMOUNT (FLOAT), and 'PROMOTION FLAG' (BOOLEAN). You need to perform the following data preparation steps using Snowpark SQLAPI:

A. All of the above.
B. Creating a feature that returns 1 if there is a PROMOTION_FLAG of True and SALES_AMOUNT > 1000, and zero otherwise
C. Handling missing 'SALES_AMOUNT values by imputing them with the average 'SALES_AMOUNT' for the same 'PRODUCT_ID during the previous month. If there's no data for the previous month, use the overall average for that
D. Converting 'SALE_DATE to a quarterly representation (e.g., '2023-QI').
E. Creating a new feature representing the percentage change in 'SALES_AMOUNT compared to the previous day for the same 'PRODUCT_ID. Handle the first day of each 'PRODUCT by setting 'SALES_GROWTH' to O.

Answer: A

Explanation:
All the described data preparation steps (A, B, C, and D) are common and relevant in feature engineering for time-series or sales data analysis. Imputing missing values using rolling averages, converting dates to categorical representations, calculating growth rates, and using flag-based transformations are all standard practices. The use of 'LEAD or 'LAG' window functions is essential for calculating , and handling edge cases (like the first day of a product's sales) is crucial for data integrity. A 'CASE statement or similar construct would be needed for the PROMOTION FLAG logic.

NEW QUESTION # 93
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 cur.execute(query) df = pd.DataFrame(cur.fetchall(), columns=[col[0] for col in cur.description])
B. ```python cur.execute(query) df = pd.read_sql(query, conn)
C. ```python with conn.cursor(snowflake.connector.DictCursor) as cur: cur.execute(query) df = pd.DataFrame(cur.fetchall())
D. ```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 =
E. ```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)

Answer: D

Explanation:
Option E, utilizing Arrow result format and , is the most efficient for large datasets. Snowflake's Arrow integration leverages columnar data transfer, significantly speeding up data retrieval compared to row-based methods (fetchall, fetchmany). Also its optimized for Pandas. Options A, B, C, and D retrieve data row by row (or in chunks) and construct the DataFrame iteratively, which is slower and consumes more memory. The DictCursor in D, while useful, doesn't fundamentally change the data transfer efficiency compared to using the Arrow format.

NEW QUESTION # 94
......

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