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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q206-Q211):

NEW QUESTION # 206
You are tasked with building a model to predict customer churn. You have a table named in Snowflake with the following relevant columns: 'customer_id', 'login_date', , 'orders_placed', , and 'churned' (binary indicator). You want to engineer features that capture customer engagement over time using Snowpark for Python. Which of the following feature engineering steps, applied sequentially, are MOST effective in creating features indicative of churn risk?

A. 1. Calculate the average 'page_views' per day for each customer. 2. Calculate the total number of for each customer. 3. Create a feature indicating whether the customer has a premium subscription ('subscription_type' = 'premium').
B. 1. Calculate the number of days since the customer's last login, and use nulls instead of negative numbers to indicate inactivity. 2. Calculate the rolling 7-day average of 'orders_placed' using a window function, partitioning by 'customer_id' and ordering by 'login_date'. 3. Calculate the slope of a linear regression of page_views' over time for each customer, indicating the trend in engagement using Snowpark ML. 4. Calculate the percentage of weeks the customer logged in. 5. Create a feature showing standard deviation of page_views per customer over the last 90 days.
C. 1. Calculate the average 'page_views' per week for each customer over the last 3 months using a window function. 2. Calculate the recency of the last order (days since last order) for each customer. 3. Create a feature indicating the change in average daily page views over the last month compared to the previous month. 4. Create a feature showing standard deviation of page_views per customer over the last 90 days.
D. 1. Calculate the total 'page_views' and 'orders_placed' for each customer without considering time. 2. Use one-hot encoding for the 'subscription_type' column.
E. 1. Calculate the maximum 'page_views' in a single day for each customer. 2. Calculate the total number of days with no 'login_date' for each customer. 3. Create a feature indicating if a customer has ever placed an order. 4. Use a simple boolean for the 'subscription_type' column.

Answer: B,C

Explanation:
Options B and E are the MOST effective because they incorporate time-based features and indicators of engagement trends. Recency (days since last order) captures the time elapsed since the customer's last interaction. Calculating changes in page views, the number of login days and linear regression slope identifies trends in engagement. Rolling averages smooth out daily fluctuations and capture longer-term patterns. Standard deviation of page views indicates a trend in page view variance, and thus overall customer engagement variance. Option A lacks recency and trend information. Option C misses temporal analysis. Option D has less relevance features and can be used however it is more useful to compare how well a customer is engaged with previous activity.

NEW QUESTION # 207
You are working with a dataset containing timestamps representing website user activity. The timestamps are stored as strings in the format 'YYYY-MM-DD HH:MI:SS.SSSSSS' in a Snowflake table named 'website_activity'. You need to extract the hour of the day from these timestamps and encode it as a cyclical feature using sine and cosine transformations. This is to capture the cyclical nature of user activity throughout the day (e.g., 23:00 and 00:00 are close in time). Which of the following Snowflake SQL code snippets correctly implements this cyclical encoding and creates the 'hour_sin' and 'hour_cos' columns?

Answer: C

Explanation:
Option A is correct. It properly casts the timestamp string to a TIMESTAMP data type using 'CAST(activity_timestamp AS TIMESTAMP) , extracts the hour using 'EXTRACT(HOUR FROM ... y , and applies the sine and cosine transformations to create the cyclical features. Options B, C, and D might contain syntax errors or incorrect functions. Using SUBSTRING to extract can be prone to errors as it doesn't perform data validation. Option E also works but it uses a MOD function which is redundant. Therefore, it is less preferable to Option A.

NEW QUESTION # 208
A marketing analyst at 'NovaRetail' suspects that a new advertising campaign has increased the average purchase amount. They have historical purchase data in a Snowflake table called 'purchase_historf. To validate their hypothesis using the Central Limit Theorem (CLT), they perform the following steps: 1. Calculate the population mean (?) of purchase amounts from the historical data'. 2. Draw 500 random samples of size 50 from the table. 3. Calculate the sample mean (x?) for each sample. Which of the following steps are essential for correctly applying the Central Limit Theorem to perform a z-test to determine whether the new advertising campaign has significantly increased the average purchase amount?

A. Calculate the standard deviation of the sample means and use it as an estimate for the standard error of the mean.
B. Calculate the standard deviation of the population (?) from the historical data and estimate the standard error of the mean as ? / sqrt(50).
C. Ensure that the samples are drawn independently and randomly.
D. Check if the original population distribution (purchase amounts) is approximately normally distributed.
E. Verify that the sample size (n=50) is sufficiently large to approximate normality of the sample mean distribution based on the CLT. This implicitly assumes population size is significantly larger than the sample size.

Answer: A,B,C,E

Explanation:
The Central Limit Theorem (CLT) allows us to perform a z-test to determine whether the mean of a sample is significantly different from the population mean. The essential steps are: A: Calculate the standard deviation of the population (?) and estimate the standard error. This is necessary to calculate the z-statistic. C: Ensure that samples are drawn independently and randomly. This is a key assumption for the CLT to hold. D: This step uses the samples to estimate the standard error of the mean directly from the 500 calculated sample means. Both A and D are correct, and the analyst could choose either approach depending on the computational efficiency and availability of population data. If population standard deviation is known or easily calculated, that's preferred. However, an estimate from the standard deviation of the sampling distribution is also valid, especially when population standard deviation calculation is not feasible. E: The CLT is applicable only if the sample size is large enough. For many distributions, n=50 is sufficient. We assume replacement, such that population size N >> n.

NEW QUESTION # 209
You are building a real-time fraud detection system using Snowpark ML and Dynamic Tables. The raw transaction data arrives continuously in a Snowflake stream. You need to create a data science pipeline that continuously transforms the data, trains a model, and scores new transactions in near real-time. Which combination of Snowflake features provides the BEST solution for achieving low latency and high throughput for this fraud detection system? Select all that apply:

A. Snowpark ML User-Defined Functions (UDFs) to apply the fraud detection model to incoming transactions, executed using Snowflake's vectorized engine for optimal performance.
B. Dynamic Tables to continuously transform the raw transaction data into features required by the model, with 'WAREHOUSE SIZE set to 'X-LARGE to ensure sufficient compute resources.
C. Snowpipe with Auto-Ingest to load the raw transaction data into a staging table before processing it with Dynamic Tables.
D. Snowflake Tasks with a 'WHEN SYSTEM$STREAM HAS clause to incrementally process new transactions from the stream and update feature tables.
E. Scheduled Snowflake tasks to retrain the model every hour based on the most recent transaction data.

Answer: A,B,D

Explanation:
Options A, B, and C are the best choices for low-latency, high-throughput fraud detection. Option A: Using Snowflake Tasks with the 'WHEN SYSTEM$STREAM HAS DATA()' clause ensures that tasks only run when there is new data in the stream, enabling incremental processing and reducing unnecessary computations. Option B: Dynamic Tables automatically update as the underlying data changes, providing continuously transformed features. Increasing the 'WAREHOUSE SIZE ensures enough compute resources are available. Option C: Snowpark ML UDFs allow you to score incoming transactions in near real-time, leveraging Snowflake's vectorized engine for fast performance. Option D introduces a delay of one hour for model retraining, which is not ideal for a real-time system. Furthermore, regularly scheduling retrain tasks and rerunning them when data is available from stream is not most efficient processing paradigm. While option E is relevant, the question focuses on the transformation, model scoring and data processing parts of a real time data sciecne pipeline, for which A, B, and C are more directly connected.

NEW QUESTION # 210
You're tasked with building an image classification model on Snowflake to identify defective components on a manufacturing assembly line using images captured by high-resolution cameras. The images are stored in a Snowflake table named 'ASSEMBLY LINE IMAGES', with columns including 'image_id' (INT), 'image_data' (VARIANT containing binary image data), and 'timestamp' (TIMESTAMP NTZ). You have a pre-trained image classification model (TensorFlow/PyTorch) saved in Snowflake's internal stage. To improve inference speed and reduce data transfer overhead, which approach provides the MOST efficient way to classify these images using Snowpark Python and UDFs?

A. Use Snowflake's external function feature to offload the image classification task to a serverless function hosted on AWS Lambda, passing the and 'image_icf to the function for processing.
B. Create a vectorized Python UDF that takes a batch of 'image_id' values as input, retrieves the corresponding 'image_data' from the 'ASSEMBLY LINE IMAGES table using a JOIN, preprocesses the images in a vectorized manner, loads the pre-trained model once at the beginning, performs classification on the batch, and returns the results.
C. Create a Python UDF that takes a single 'image_id' as input, retrieves the corresponding 'image_data' from the table, preprocesses the image, loads the pre-trained model, performs classification, and returns the result. This UDF will be called for each image individually.
D. Create a Java UDF that loads the pre-trained model and preprocesses the images. Call this Java UDF from a Python UDF to perform the image classification. Since Java is faster than Python, this will optimize performance.
E. Create a Python UDF that loads the entire table into memory, preprocesses the images, loads the pre-trained model, and performs classification for all images in a single execution.

Answer: B

Explanation:
Option C offers the most efficient solution. Vectorized UDFs allow processing batches of data at once, significantly reducing overhead compared to processing each image individually (Option B). Loading the model once per batch avoids redundant model loading. Option A is highly inefficient as it attempts to load the entire table into memory. While Java can be faster in certain scenarios, the complexity of calling a Java UDF from a Python UDF (Option D) will likely introduce more overhead than benefits. External functions (Option E) introduce network latency and are generally less efficient than in-database processing, unless there's a specific need for external resources or specialized hardware that Snowflake doesn't offer.

NEW QUESTION # 211
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