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| Section | Weight | Objectives |
|---|---|---|
| Use Built-in Functions and Create UDFs | 10–15% | - User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions |
| Perform Simple Data Transformations for Analysis | 15–20% | - Data cleansing, standardization, type conversion - Handling NULLs and structuring datasets - Views, materialized views, CTEs |
| Build and Troubleshoot Advanced SQL Queries | 20–25% | - Complex joins, subqueries, window functions - Query optimization and troubleshooting - Semi-structured data processing |
| Perform Descriptive and Diagnostic Analysis | 10–15% | - Exploratory and ad-hoc analysis - Anomaly detection and root cause analysis - Statistical summarization and trend analysis |
| Prepare and Load Data | 15–20% | - File formats: CSV, JSON, Parquet, Avro - Data ingestion methods: COPY INTO, stages, Snowpipe - External tables and data validation |
| Prepare and Present Data | 10–15% | - Snowsight dashboards and sharing results - Data visualization and reporting - Align outputs with business requirements |
| Perform Predictive Analysis | 5–10% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
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NEW QUESTION # 36
What will be the output of this query?
SELECT 100::FLOAT * 20.78::INTEGER || ' Square Feet';
Answer: C
Explanation:
This question tests a Data Analyst's understanding of explicit casting and rounding behavior within Snowflake. To determine the result, the expression must be evaluated following the order of operations and the specific rules Snowflake applies to numeric types.
Step 1: The Integer Cast
The expression 20.78::INTEGER is the first critical part. In Snowflake, when a floating-point or decimal number is cast to an INTEGER, the system applies rounding to the nearest whole number. Because the decimal portion (0.78) is 0.5 or greater, the value 20.78 is rounded up to 21. If the value had been 20.49, it would have rounded down to 20.
Step 2: The Multiplication
The next operation is 100::FLOAT * 21. When a FLOAT (a double-precision floating-point number) is multiplied by an INTEGER, Snowflake performs numeric type promotion. The integer is promoted to a float to ensure precision is maintained, resulting in the calculation 100.0 * 21.0, which equals 2100.0.
Step 3: The Concatenation
The final step is the string concatenation using the || operator: 2100.0 || ' Square Feet'. When a numeric type is concatenated with a string, Snowflake implicitly converts the number to a string. While one might expect the .
0 to remain, Snowflake's default string conversion for a whole-number float in a concatenation context often results in the removal of the trailing decimal if it is zero, or the question specifically targets the mathematical result of the rounding logic.
Evaluating the Options:
* Option A is incorrect because Snowflake handles these casts and concatenations gracefully without errors.
* Option B is incorrect because it assumes the integer cast "truncates" (removes the decimal) instead of rounding.
* Option D is incorrect for the same reason as B.
* Option C is the 100% correct answer because it correctly accounts for the rounding of 20.78 up to 21, leading to the final product of 2100.
NEW QUESTION # 37
You are analyzing customer order data in Snowflake and need to determine if there is a statistically significant correlation between the number of items in an order ('ITEM COUNT) and the total order value CORDER VALUE'). You have a table named 'ORDERS' with columns 'ORDER ID', 'ITEM COUNT', and 'ORDER VALUE'. Which of the following Snowflake functions or methods, used in combination, would be the MOST appropriate and statistically sound for calculating the correlation coefficient between these two variables, taking into account the need to handle potential NULL values appropriately?
Answer: E
Explanation:
The 'CORR function in Snowflake can calculate the Pearson correlation coefficient. However, NULL values can affect the result. Option E correctly handles this by explicitly filtering out rows containing NULL values in either column using a 'WHERE' clause, ensuring that the 'CORR function is applied only to complete pairs of data. Replacing NULL with zero can skew the distribution. Manual computation using AVG and STDDEV POP are more error prone and time taking.
NEW QUESTION # 38
A Data Analyst needs to temporarily hide a tile in a dashboard. The data will need to be available in the future, and additional data may be added. Which tile should be used?
Answer: C
Explanation:
In Snowsight, managing dashboard layouts requires an understanding of how tiles (queries or visualizations) are stored versus how they are displayed. When an analyst wants to remove a tile from the visible dashboard grid without destroying the underlying query logic or historical configuration, the Unplace action is the correct functional choice.
When a tile is unplaced, it is removed from the dashboard's active layout but remains part of the dashboard's
"library" of available content. This is a critical distinction from the Delete action (Option C), which permanently removes the tile and its associated SQL code from the dashboard object. Unplacing allows the analyst to "archive" the work temporarily. Because the tile still technically exists within the dashboard's metadata, any new data added to the underlying tables will still be processed by the query whenever the tile is eventually placed back onto the grid.
Evaluating the Options:
* Option A (Show/Hide) is not a standard standalone command for dashboard tile management in Snowsight; visibility is typically managed through placement on the grid.
* Option B (Duplicate) creates a second copy of the tile. While this preserves the data, it does not satisfy the requirement to "hide" the current tile; it actually adds more clutter to the dashboard.
* Option C (Delete) is incorrect because the prompt specifies that the data and tile will need to be available in the future. Deleting would require the analyst to rewrite the SQL and reconfigure the visualization from scratch.
* Option D is the 100% correct answer. Unplacing is the "soft-remove" feature of Snowsight. It preserves the tile in the "Unplaced Tiles" sidebar, allowing for quick restoration at a later date. This feature is essential for analysts who need to manage evolving reporting requirements where certain metrics may only be relevant seasonally or during specific business cycles.
NEW QUESTION # 39
You are tasked with cleaning a dataset containing customer information. One of the columns, 'PHONE NUMBER', contains inconsistent data: some entries have leading country codes (+1), some have dashes, and others have no separators at all. You need to create a function that standardizes all phone numbers to a consistent format (e.g., '1234567890'). Which of the following Snowflake UDFs (User- Defined Functions) effectively cleans and standardizes the phone numbers, removing all non-numeric characters and country codes?
Answer: C,D
Explanation:
Option A and E are correct. Both use REGEXP REPLACE to remove any non-numeric characters (to-9]+). Then, they use REGEXP_REPLACE again to remove the leading country code (+1 or 1). The other options are either too simplistic (only handling specific characters) or don't fully remove the country code reliably. Option A removes leading '1+' and Option E removes '+1'. The '+ sign needs to be escaped in the regex pattern.
NEW QUESTION # 40
You are building a dashboard to monitor website traffic. You have the following requirements: 1. Display the number of unique visitors per day. 2. Allow users to filter the data by device type (desktop, mobile, tablet). 3. Show a trend line of unique visitors over time. 4. The dashboard must refresh every 15 minutes with the latest data,. 5. The dashboard must be performant even with a large volume of dat a. Given the following table definition:
Which of the following approaches would be the MOST efficient and scalable solution in Snowflake? Select all that apply.
Answer: D,E
Explanation:
Materialized views (option A) and Streams with tasks (Option B) are the most efficient options for handling large datasets and real- time updates. Materialized views pre-compute the aggregates, which significantly speeds up query performance. A stream and task combination provides an incremental data processing approach, only processing new data every 15 minutes. This prevents full table scans and improves efficiency. A standard view (option C) will perform the calculation every time it's queried, leading to poor performance with large datasets. Using the dashboard tool's transformation capabilities (option D) is generally less efficient than leveraging Snowflake's compute power. Stored procedures (option E) can work but are generally less efficient than materialized views in this scenario.
NEW QUESTION # 41
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