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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q215-Q220):

NEW QUESTION # 215
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. 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.
B. 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.
C. 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
D. 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.
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: D

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 # 216
A retail company wants to visualize sales performance across different product categories and regions. The business stakeholders need to identify both overall sales trends and granular insights into the performance of specific products in specific regions. They require a dashboard that allows for easy comparison of sales across categories and regions, highlighting best and worst performers. Which combination of chart types would be MOST effective for this dashboard, considering scalability and the need to avoid over-plotting?

A. A geographical map visualizing sales by region with color-coded regions, a time series chart for overall sales trends, and a detail table for viewing sales by product categories.
B. A stacked bar chart for sales by category, a line chart for overall sales trend over time, and a pie chart for regional sales distribution.
C. A heat grid showing sales by category and region, a time series chart for overall sales trends, and a treemap representing the contribution of each category to total sales.
D. A combination of bullet charts to show sales performance against targets for each region and category, a time series chart for overall sales trend, and a scatter plot showing discount vs quantity.
E. A scatter plot comparing sales volume and profit margin for each product, a bar chart for sales by region, and a gauge chart indicating overall sales target achievement.

Answer: C

Explanation:
A heat grid effectively visualizes the relationship between two categorical variables (category and region) using color intensity, making it easy to identify high and low sales areas. A time series chart is appropriate for displaying trends over time. A treemap shows the proportional size of each category contributing to total sales. Stacked bar charts can become difficult to read with many categories and pie charts are not ideal for precise comparisons. Scatter plots are useful for correlation analysis (Sales vs Profit). A map would be good for high level visualization but not for specific numbers or precise details. Bullet charts are more suitable for target vs actual comparisons than a regional overview.

NEW QUESTION # 217
You are tasked with creating a dashboard that displays the average transaction amount for each customer segment. However, sensitive customer information, such as credit card numbers, is stored in the 'TRANSACTIONS table and protected by Dynamic Data Masking. The masking policy replaces the credit card number with 'XXXX-XXXX-XXXX-XXXX'. The dashboard needs to allow analysts to drill down into individual transactions to identify fraud patterns, but without exposing the actual credit card numbers. Which of the following approaches is the MOST secure and efficient way to achieve this?

A. Create a role hierarchy where the 'ANALYST ROLE inherits from a ROLE' that has the 'APPLY MASKING POLICY privilege on the column. Grant the 'ANALYST_ROLE 'SELECT privilege on the 'TRANSACTIONS' table. This allows drill-down while preserving the masked credit card numbers.
B. Create a view that selects all columns from the 'TRANSACTIONS table. Grant the 'ANALYST ROLE 'SELECT privilege on the view. The masking policy will automatically apply, preventing analysts from seeing the actual credit card numbers. Allow drill-down on all available fields.
C. Create a tokenization service outside of Snowflake. Replace the credit card numbers in the 'TRANSACTIONS table with tokens. Store the mapping between tokens and credit card numbers securely. Provide the analysts with access to the tokenization service to de-tokenize the credit card numbers only when absolutely necessary for fraud investigation. The dashboard displays the tokenized values, and drill-down leads to a request to the tokenization service.
D. Create a stored procedure that executes with 'CALLER rights. The stored procedure queries the 'TRANSACTIONS' table and returns the data. Grant the 'ANALYST ROLE execute privilege on the stored procedure. This approach bypasses the masking policy but provides more control over which data is displayed during drill-down.
E. Create a UDF (User-Defined Function) that partially unmasks the credit card number, revealing only the last four digits. Apply this UDF in the dashboard when drilling down. This allows analysts to identify patterns while protecting most of the sensitive data.

Answer: B

Explanation:
Option A is the most secure and efficient approach. Dynamic Data Masking is designed to automatically apply masking policies to columns when they are queried, regardless of whether the query is executed directly or through a view. This ensures that analysts will always see the masked credit card numbers, preventing unauthorized access to sensitive data. Options C and E introduce external services, while Option D weakens masking and option B does not apply the masking by itself. Role hierarchy is an authorization concept, not a masking concept.

NEW QUESTION # 218
You are loading data from a series of CSV files into Snowflake using Snowsight. The files have varying column orders and some files are missing certain columns. You need to ensure that all data is loaded into a consistent table schema, handling missing columns gracefully.
Which of the following strategies is MOST effective in Snowsight to achieve this?

A. Load the data into a staging table with a single VARIANT column. Then, use SQL with 'CASE statements and 'GET ' function to extract data from the VARIANT column into the target table with the desired schema.
B. Create multiple file formats, one for each unique CSV file structure. Use Snowsight's 'Load Data' wizard to load each set of files with the corresponding file format. Use UNION ALL to combine the data from multiple tables into a single view.
C. Define a file format with 'SKIP HEADER = 1', "FIELD OPTIONALLY ENCLOSED BY = and "NULL _ IF = (", 'NULL')'. Create a single table with all columns defined and use Snowsight's 'Load Data' wizard to load the CSV files. Columns not present in a given CSV file will automatically be populated with NULL.
D. Define a file format with 'SKIP_HEADER = 1 ' and load all CSV files into a single table with all possible columns defined as VARCHAR. After loading, use SQL queries with and to convert the data to the appropriate data types.
E. Pre-process the CSV files before loading using a scripting language (e.g., Python) to standardize the column order and add missing columns with NULL values. Then, load the pre-processed files into Snowflake using Snowsight.

Answer: C

Explanation:
Option D is the most effective strategy. By defining the file format with 'SKIP_HEADER = , FIELD_OPTIONALLY_ENCLOSED_BY ' and 'NULL_IF = (", 'NULL')', Snowflake can handle missing columns by populating them with NULL values during the load process. Creating a single table with all columns defined ensures data consistency. Option A works, but the type conversion after loading is less efficient and more error-prone. Option B requires managing multiple file formats and using UNION ALL, which can be complex. Option C using VARIANT will work, but adds extra complexity. Option E requires an external preprocessing step, which is less desirable.

NEW QUESTION # 219
A retail company has data about their products, sales, and inventory. They need a dashboard to visualize key metrics, including total sales, average order value, inventory levels, and product performance across different regions. The data is stored in the following tables: 'PRODUCTS (PRODUCT ID, PRODUCT NAME, CATEGORY, PRICE) 'SALES' (SALE_ID, PRODUCT_ID, SALE_DATE, QUANTITY, REGION) 'INVENTORY (PRODUCT ID, REGION, QUANTITY ON_HAND) Which of the following strategies will result in an efficient dashboard that allows users to quickly filter and drill down into the data by region, product category, and time period while minimizing query execution time? (Select all that apply.)

A. Create materialized views that pre-aggregate sales data by region, product category, and time period (e.g., daily, weekly, monthly). Join these materialized views with product and inventory data in the dashboard queries.
B. Utilize Snowflake's search optimization service on relevant columns (e.g., PRODUCT ID, REGION) in the base tables and use standard JOINs and aggregations within views used by the dashboard.
C. Implement dynamic data masking policies to filter out sensitive data from the base tables, ensuring data governance.
D. Create a single, wide denormalized table containing all the necessary data from the 'PRODUCTS, 'SALES, and 'INVENTORY tables using JOINs. Build the dashboard directly on this table.
E. Create separate views for sales, inventory, and product information, then use the dashboard tool to join these views and perform aggregations.

Answer: A,B

Explanation:
Search optimization (C) can significantly speed up queries on large tables by creating a search index on frequently used filter columns. Materialized views (D) are also beneficial because they pre-aggregate the data, reducing the amount of computation required at query time. Creating a single, wide denormalized table (A) can lead to data redundancy and increased storage costs. Joining separate views in the dashboard tool (B) can be inefficient, as the joins are performed at query time. Data masking policies (E) are important for security but don't directly optimize query performance for dashboards.

NEW QUESTION # 220
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