Features Of Web-based Snowflake DAA-C01 Practice Exam

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q122-Q127):

NEW QUESTION # 122
A company stores sensor data, including timestamps (ts), sensor ID (sensor_id), and readings (reading_value), in a Snowflake table named 'sensor_data'. Due to sensor malfunctions, some readings are significantly higher or lower than expected (outliers). Which of the following approaches are suitable in Snowflake to calculate the average reading value for each sensor, EXCLUDING readings that fall outside of two standard deviations from the mean for that sensor?

A. Using a HAVING clause after grouping by sensor_id to filter out groups where the range of reading_value exceeds a certain threshold.
B. Using a LATERAL FLATTEN function to transform reading values into an array, calculate the mean and standard deviation in a JavaScript UDF, then use ARRAY SLICE to remove outliers before calculating the average.
C. Using window functions to calculate the mean and standard deviation for each sensor, then filtering the results to exclude outliers using a WHERE clause.
D. Using a QUALIFY clause with window functions to filter out the outlier readings based on their distance from the mean, prior to calculating the final average.
E. Calculating the mean and standard deviation for each sensor in a subquery, then joining the results with the original data and filtering based on the calculated values.

Answer: C,D,E

Explanation:
Options A, B and C provides valid ways to determine outliers. A is based on direct filtering based on standard deviation on the original table using window function. B uses Sub query approach and filtering. C allows to use QUALIFY clause with window functions for filtering before aggregation. D attempts to filter groups based on range which is not the intent of the original question to filter on a per reading basis if its an outlier or not. Option E, although technically possible, introduces significant complexity and performance overhead with the use of UDF and array manipulation for a task achievable with standard SQL.

NEW QUESTION # 123
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. 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.
B. 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.
C. 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.
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: A

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 # 124
A large retail company is migrating its transaction data to Snowflake and wants to build a consumption layer for BI reporting. They have historical data with frequent updates and require both point-in-time analysis and trend analysis. Which modeling technique(s) would be MOST suitable for this scenario, considering performance, storage efficiency, and the need for both historical tracking and current state views?

A. Star Schema with Slowly Changing Dimension Type 1 (SCDI) for all dimensions.
B. Star Schema with Slowly Changing Dimension Type 0 (SCDO) for critical dimensions.
C. A wide, denormalized table created using CREATE TABLE AS SELECT (CTAS) statement, refreshed nightly.
D. Data Vault modeling, with point-in-time tables built on top of the Data Vault for Bl reporting.
E. Snowflake Schema with Slowly Changing Dimension Type 2 (SCD2) for relevant dimensions.

Answer: D,E

Explanation:
Data Vault provides the historical tracking and auditability needed, while point-in-time tables on top optimize for BI queries. Snowflake schema with SCD2 also allows historical tracking within a dimensional model. SCDI alone will not preserve history, and SCDO locks in attributes indefinitely. A wide denormalized table is not suitable for historical analysis or frequent updates and can lead to redundancy. Therefore options B and C are suitable for BI reporting.

NEW QUESTION # 125
You are preparing data for a machine learning model that predicts customer churn. You have a table 'CUSTOMER TRANSACTIONS with columns 'CUSTOMER ID', 'TRANSACTION DATE, and 'TRANSACTION AMOUNT'. Your model requires a feature representing the recency of the customer's last transaction (days since last transaction) calculated as of today. Which of the following SQL snippets in Snowflake best achieves this, considering potential performance implications on a large dataset?

Answer: D

Explanation:
The best approach is E because it explicitly casts 'CURRENT _ to a DATE data type using 'CAST(CURRENT This ensures consistency and avoids potential issues if the TRANSACTION DATE' column is also of DATE data type. provides a date only, while provides date and time. Using 'CURRENT directly in 'DATEDIFF with a DATE field could lead to unexpected results due to the time component. Also other options are incorrect because 'GETDATE()' does not exist in snowflake, and because is redundant since CURRENT TIMESTAMP returns a timestamp already

NEW QUESTION # 126
You have a Snowflake table 'CUSTOMER DATA' containing customer information. You want to enrich this data using two separate data shares from the Snowflake Marketplace. Share A provides demographic information, and Share B provides credit risk scores. Both shares contain views named 'CUSTOMER ENRICHMENT with a common column 'CUSTOMER ID'. Due to compliance requirements, you need to ensure that only customers with a credit risk score above a certain threshold (e.g., 700) are enriched with demographic data'. Which of the following approaches ensures that the customer data is enriched securely, efficiently, and in compliance with the credit risk threshold?

A. Create a stored procedure that iterates through the 'CUSTOMER_DATA' table, retrieves demographic and credit risk information for each customer, applies the credit risk threshold, and inserts the enriched data into a new table.
B. Create a task that periodically runs a query that joins 'CUSTOMER DATA with both shared views (CUSTOMER ENRICHMENT from Share A and Share B), filtering based on the credit risk threshold. Insert the results into a new enriched table.
C. Create two separate views, one for each data share, and then join them based on in a final view, filtering for the credit risk threshold in the final view.
D. Replicate data from both data shares and perform enrichment and credit risk filtering on the replicated data.
E. Create a single view that joins 'CUSTOMER_DATA' with both shared views ('CUSTOMER_ENRICHMENT from Share A and Share B) using a common table expression (CTE) to filter records from Share B to include only customers with a credit risk score above the defined threshold.

Answer: E

Explanation:
Option C is the most secure, efficient, and compliant approach. Using a single view with a CTE allows you to encapsulate the credit risk filtering logic within the view definition, ensuring that only customers meeting the threshold are enriched. This avoids exposing sensitive credit risk information to unauthorized users. Views provide row-level security for the table. Options A requires two steps and is less efficient, Option B is less efficient and harder to maintain than a view, and Options D is not recommended for sharing scenarios. Option E will require an additional task creation. Using single view with CTE will simplify the query to implement it with minimum code.

NEW QUESTION # 127
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FEATURES OF WEB-BASED SNOWFLAKE DAA-C01 PRACTICE EXAM

Features Of Web-based Snowflake DAA-C01 Practice Exam