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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Perform Predictive Analysis | 5–10% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
| Topic 2: Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
| Topic 3: Prepare and Present Data | 10–15% | - Data visualization and reporting - Snowsight dashboards and sharing results - Align outputs with business requirements |
| Topic 4: Prepare and Load Data | 15–20% | - Data ingestion methods: COPY INTO, stages, Snowpipe - File formats: CSV, JSON, Parquet, Avro - External tables and data validation |
| Topic 5: Perform Simple Data Transformations for Analysis | 15–20% | - Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion - Views, materialized views, CTEs |
| Topic 6: Build and Troubleshoot Advanced SQL Queries | 20–25% | - Semi-structured data processing - Complex joins, subqueries, window functions - Query optimization and troubleshooting |
| Topic 7: Perform Descriptive and Diagnostic Analysis | 10–15% | - Exploratory and ad-hoc analysis - Statistical summarization and trend analysis - Anomaly detection and root cause analysis |
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NEW QUESTION # 57
Consider a Snowflake table 'USER EVENTS' with a 'VARIANT' column named 'event_data' containing JSON objects representing user activity. The JSON structure varies significantly across rows. You need to extract all the distinct event types from this data'. Which of the following Snowflake queries is the most efficient way to achieve this, handling potential null or missing 'event_type' fields gracefully and avoiding errors? Assume the volume of data is very large.
Answer: E
Explanation:
Option C, using , is the most efficient and robust solution. attempts to convert the JSON value to a string and returns NULL if the conversion fails (e.g., if is an object or array, not a string or a value that can be cast to a string). This avoids errors and simplifies the query. Using 'DISTINCT on the result then gives the distinct event types. Options A, B, D and E have the overhead of IS NULL or NVL functions, that make processing slower and inefficeint compared to C. While these options handle nulls, they are more verbose and potentially less performant due to the explicit null checks. Option A will also exclude rows where event_data:event_type is actually NULL, which might be undesirable.
NEW QUESTION # 58
You have a table 'CUSTOMER DATA' with a column 'phone_number" (VARCHAR) that contains phone numbers in various formats (e.g., '123-456-7890', '1234567890', '+11234567890'). You need to standardize the phone numbers to a format of '1234567890' (no hyphens or country code). Which Snowflake SQL statement, using scalar string functions, will achieve this standardization while gracefully handling potentially invalid phone numbers (e.g., too short or containing letters) by returning NULL for invalid entries?
Answer: D
Explanation:
Option C is the correct answer. It first uses 'REGEXP REPLACE(phone_number, '[AO-91+', to remove all non-numeric characters from the phone number. Then, it uses a 'CASE statement to check if the resulting string has a length of 10 (a valid phone number length after standardization). If it does, the standardized phone number is returned; otherwise, NULL is returned. Option A only removes non-numeric characters but doesn't handle invalid lengths. Option B and D is doing same ,using 'IFF and adds unnecessary complexity by using 'IS NUMERIC' which is redundant since 'REGEXP REPLACE ensures only numbers exist. Option E filters on 'WHERE' Clause that reduces the record which are having length of 10, But Question needs to return NULL for invalid entries .
NEW QUESTION # 59
Consider a scenario where you are building a dashboard to monitor the performance of a marketing campaign. The data includes daily ad spend, website conversions, and cost per acquisition (CPA). The stakeholders need to quickly assess whether the campaign is meeting its target CPA. What visualization type would be MOST appropriate to display the current CPA compared to the target CPA, providing a clear and concise view of performance?
Answer: A
Explanation:
A gauge chart is specifically designed to display a single value (the current CPA) in relation to a target value (the target CPA). The color-coded zones provide an immediate indication of whether the campaign is performing well, needs improvement, or is failing. Line charts show trends, bar charts compare averages, and scatter plots show relationships. Pie chart showing CPA percentage against target CPA percentage do not accurately show CPA against target CPA, for better visualization gauge charts would be the preffered option
NEW QUESTION # 60
You have developed a Snowsight dashboard for your marketing team that contains sensitive customer data'. You need to share this dashboard with a specific group of users, but ensure that they can only view the data and cannot modify the dashboard itself or the underlying queries. Which of the following steps should you take to securely share the dashboard?
Answer: C
Explanation:
Sharing with 'Can view' (B) ensures the users cannot modify the dashboard itself. Granting 'SELECT on the underlying tables/views and USAGE' on the database/schema allows them to see the data when the dashboard executes queries on their behalf. 'Can edit' (A) would allow them to modify the dashboard, which is not desired. 'MONITOR (C) is for monitoring warehouse usage, not data access. Exporting as PDF (D) is a workaround, but it loses interactivity and isn't a direct sharing method. E isn't correct because permissions on the data sources must be granted separately.
NEW QUESTION # 61
What will be the output of this query?
SELECT 100::FLOAT * 20.78::INTEGER || ' Square Feet';
Answer: B
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 # 62
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