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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q12-Q17):

NEW QUESTION # 12
A data analyst is investigating a decline in the conversion rate on an e-commerce website. They have access to the following tables in Snowflake: 'sessions': 'session id', 'user id', 'start time', 'end_time' 'page views': 'session id', 'page_urr, 'view time' 'transactions': 'session_id', 'transaction id', 'amount', 'transaction_time' Which of the following approaches, using Snowflake features, would be MOST effective for identifying potential bottlenecks or drop-off points in the user journey?

A. Create a funnel analysis by defining key stages in the user journey (e.g., homepage visit, product page view, add to cart, checkout, purchase). Use window functions to track users as they progress through the funnel, calculating conversion rates between each stage. Visualize the funnel using a BI tool for easy identification of drop-off points.
B. Use Snowflake's 'SHOW TABLES' command to identify the most frequently updated tables. Then, create a dashboard on these tables to monitor the rate of changes.
C. Implement a data lineage tool to trace the flow of data from raw sources to the transaction table. This will reveal any data quality issues that may be impacting conversion rates.
D. Perform a cohort analysis by grouping users based on their sign-up date or initial website visit date. Track their conversion rates over time. Use a data visualization tool to see if any group exhibits an unusual drop in the conversion rate.
E. Use recursive SQL common table expressions (CTEs) to reconstruct the entire user journey for each session, from the entry page to either a successful transaction or session termination. Analyze path completion rates at each step to identify the pages where users are most likely to abandon the session.

Answer: A,D

Explanation:
Options B and C provide useful diagnostic insights. B offers direct information about conversion at each stage of the funnel. Option C enables discovery of unusual drops over time. Option A might be a difficult, resource intensive solution for complex user journeys. Option D is a poor approach as it identifies the rate of change in tables instead of the main objective - bottlenecks or drop-off points. Option E, while helpful for data governance, doesn't directly pinpoint user journey issues.

NEW QUESTION # 13
A financial institution needs to build a dashboard to monitor fraudulent transactions. They have transaction data (amount, timestamp, location, merchant category), customer data (age, income, credit score), and fraud flags. They want to identify patterns and correlations that indicate potentially fraudulent activity. Which of the following visualizations and Snowflake features, when used in combination, would be MOST effective for this purpose? (Select TWO)

A. Create a static report showing the total number of fraudulent transactions per month, using Snowflake's aggregate functions.
B. Use Snowflake's secure data sharing to share the raw transaction data with a third-party analytics vendor.
C. Utilize a heatmap visualization in a Tableau dashboard to show the correlation between merchant category, transaction amount, and fraud flag. Implement interactive filters for customer age, income, and location. Implement Snowflake Row Access Policy to restrict data to specific users.
D. Export the transaction data to a data lake and use Spark to perform fraud detection.
E. Develop an interactive dashboard using Power BI connected to Snowflake, incorporating a geographical map showing transaction locations, a time series chart of transaction volume, and a scatter plot of transaction amount vs. customer credit score. Use anomaly detection algorithms in Snowflake (via Snowpark or stored procedures using Python) to highlight unusual transactions. Implement User defined table Function in Power BI to generate more data.

Answer: C,E

Explanation:
Options B and D are the most effective. Option B utilizes an interactive dashboard with Power BI, incorporating geographical, temporal, and scatter plot visualizations to identify patterns. Anomaly detection algorithms in Snowflake further enhance the detection capabilities. Option D uses a heatmap to show correlations between key variables and implements interactive filters for detailed exploration. Implementing Row Access Policy would control data access, adding a security layer on top of sensitive financial data. Option A is not related to visualization or identifying correlations. Option C provides only a high-level summary. Option E involves exporting data outside Snowflake, which is less efficient and potentially less secure than using Snowflake's built-in capabilities.

NEW QUESTION # 14
When managing Snowsight dashboards, what role do subscriptions and updates play in meeting business requirements?

A. Managing subscriptions and updates complicates dashboard usage.
B. Subscriptions and updates ensure timely information delivery.
C. They enhance dashboard usability without impacting data updates.
D. Subscriptions and updates don't impact dashboard management.

Answer: B

Explanation:
Subscriptions and updates ensure timely information delivery, meeting business requirements.

NEW QUESTION # 15
A logistics company needs to determine which warehouses are within a 50km radius of a new distribution center. The warehouse locations are stored in a table 'WAREHOUSES' with columns 'WAREHOUSE ID' ONT), (GEOGRAPHY) and the distribution center's location is stored in a variable of type GEOGRAPHY. Which query will efficiently identify all warehouses within the specified radius, returning the 'WAREHOUSE ID and distance in kilometers?

A. Option E
B. Option A
C. Option C
D. Option D
E. Option B

Answer: A

Explanation:
The correct answer uses 'ST DWITHIN' with the correct parameters and unit. 'ST DWITHIN(LOCATION, @distribution_center, 50000)' correctly filters the warehouses based on the 50km (50000 meters) radius. ST_DISTANCE calculates the distance in meters, which is then converted to kilometers by dividing by 1000. The warehouse location should come first followed by the distribution centre in the DWITHIN' Function.

NEW QUESTION # 16
You are tasked with analyzing sensor data collected from industrial machines to predict potential failures. The data includes timestamps (EVENT TIMESTAMP), machine IDs ('MACHINE ID'), and various sensor readings ('SENSORI', 'SENSOR? , etc.). You want to use Snowflake's forecasting capabilities to predict when a machine might exceed a critical threshold for 'SENSORI based on historical data'. Which of the following approaches would be the MOST effective in preparing the data and creating the forecasting model?

A. Use the raw sensor data, but convert 'EVENT _ TIMESTAMP to seconds since the epoch and use that as the timestamp input. No aggregation is required.
B. Aggregate the sensor data into hourly averages for each machine ID. Create features such as rolling averages and lagged values of 'SENSORI to capture trends and dependencies. Then, build the forecasting model using these aggregated and engineered features.
C. Perform complex feature engineering by generating polynomial features from all available sensor readings and use a highly complex model such as a deep neural network instead of Snowflake's native forecasting. This ensures maximum accuracy.
D. Create a forecasting model using 'SENSORI ' as the target and use only 'MACHINE_ID' as input features. The forecasting is solely based on Machine ID for predicting sensor data
E. Directly create a forecasting model using the raw sensor data, using 'EVENT _ TIMESTAMP' as the timestamp input and 'SENSORI as the target variable. No data aggregation or feature engineering is required.

Answer: B

Explanation:
Option B is the most effective. Aggregating the data into hourly averages reduces noise and makes the data more manageable for forecasting. Feature engineering, such as creating rolling averages and lagged values, helps capture trends and dependencies in the data, improving the accuracy of the forecasting model. A more refined approach is almost always better than feeding in raw data.

NEW QUESTION # 17
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