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| Section | Weight | Objectives |
|---|---|---|
| Perform Simple Data Transformations for Analysis | 15–20% | - Views, materialized views, CTEs - Data cleansing, standardization, type conversion - Handling NULLs and structuring datasets |
| Use Built-in Functions and Create UDFs | 10–15% | - User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions |
| Prepare and Present Data | 10–15% | - Snowsight dashboards and sharing results - Data visualization and reporting - Align outputs with business requirements |
| Perform Descriptive and Diagnostic Analysis | 10–15% | - Statistical summarization and trend analysis - Anomaly detection and root cause analysis - Exploratory and ad-hoc analysis |
| Prepare and Load Data | 15–20% | - File formats: CSV, JSON, Parquet, Avro - External tables and data validation - Data ingestion methods: COPY INTO, stages, Snowpipe |
| Perform Predictive Analysis | 5–10% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
| Build and Troubleshoot Advanced SQL Queries | 20–25% | - Query optimization and troubleshooting - Semi-structured data processing - Complex joins, subqueries, window functions |
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NEW QUESTION # 42
A Directed Acyclic Graph (DAG) of 12 tasks (ETLDAILY) failed because one of the tasks failed (STEP10), therefore the dependent tasks (STEP11 and STEP12) did not run. Which command is needed to re-run task STEP10 and the two dependent tasks?
Answer: B
Explanation:
Managing complex pipelines involving multiple dependent tasks requires an understanding of Task Graphs (DAGs). In Snowflake, a DAG is a series of tasks with parent-child relationships. If a parent task (like STEP10) fails, Snowflake automatically cancels the execution of all child tasks (like STEP11 and STEP12) to prevent data corruption or inconsistent states.
To recover from such a failure, Snowflake provides the RETRY LAST clause within the EXECUTE TASK command. The critical nuance here is that you do not target the individual failed task; instead, you target the root task of the graph (in this case, ETLDAILY). When you run EXECUTE TASK <root_task> RETRY LAST;, Snowflake identifies the most recent failed run of that graph and intelligently resumes execution starting from the failed task (STEP10) and continues through all subsequent dependent tasks that were previously skipped.
Evaluating the Options:
* Option A is incorrect because attempting to execute just STEP10 would treat it as a standalone run and would not automatically trigger the dependent logic of the ETLDAILY graph structure.
* Options C and D are incorrect because RESUME is a DDL command used to change a task's state from SUSPENDED to STARTED (enabled). It does not trigger an immediate execution or a retry of a failed run; it merely allows the task to run on its next scheduled interval.
* Option B is the 100% correct answer. It utilizes the dedicated Snowflake mechanism for graph recovery, ensuring that the entire pipeline is completed starting from the point of failure without re- running successfully completed earlier steps.
NEW QUESTION # 43
You are tasked with cleaning a 'COMMENTS table that contains user-generated comments in a column (VARCHAR). The comments often contain HTML tags, excessive whitespace, and potentially malicious scripts. Your goal is to remove all HTML tags, trim leading and trailing whitespace, and escape any remaining HTML entities to prevent script injection vulnerabilities. Which combination of Snowflake scalar functions provides the most robust and secure way to achieve this data cleaning?
Answer: D
Explanation:
Option B is the most robust and secure method. Here's why: 'REGEXP REPLACE(comment_text, Y', "Y: This removes HTML tags. This attempts to parse the remaining text as XML. If there are still any unescaped or malformed HTML entities, this step will help to isolate them and get rid of the tags. If the text cannot be parsed as XML, PARSE_XML returns NULL. '$').$: This extracts the text content of the XML. Crucially, 'XMLGET' inherently performs HTML entity decoding, effectively escaping potentially dangerous characters (e.g., becomes This prevents script injection. This removes leading and trailing whitespace. Option A only removes the HTML tags and trims the text, but doesn't handle HTML entity encoding, and thus it is vulnerable to script injection. Option C is not correct as HTML ENTITY DECODE' is not an existing function in Snowflake. Option D is not correct as the text needs to be cleaned irrespective of whether it contains XML or not. Option E - if parsing the XML returns null then original value gets returned , which we don't want , we would need to make the value NULL.
NEW QUESTION # 44
Your company uses Snowflake to store sales data'. A dashboard reporting weekly sales trends is performing poorly. The underlying table, 'SALES DATA, contains billions of rows with columns like 'SALE DATE, 'PRODUCT ID', 'CUSTOMER D', and 'SALE AMOUNT. The dashboard queries use 'SALE DATE for filtering and grouping. The query execution plan shows full table scans. You need to optimize the dashboard's performance with minimal impact on data loading processes. Which of the following strategies should you implement FIRST to improve query performance?
Answer: E
Explanation:
Clustering the table on 'SALE DATE' is the most effective initial strategy. It physically organizes the data based on 'SALE DATE, which the dashboard queries use for filtering, thus reducing the amount of data scanned during query execution. Materialized views require ongoing maintenance and may not be the most efficient starting point. Increasing the warehouse size will increase the resource, but doesn't solve the underlying problem of full table scans, and search optimization is less efficient than clustering for date-based filtering. Snowflake does not support user-defined partitioning. Hence option A is the most appropriate choice.
NEW QUESTION # 45
You have a Snowflake table 'order details' with columns 'order id', 'customer id', 'order date', and 'order amount'. You need to calculate the 3-month moving average of 'order_amount' for each customer, but only for those customers who have placed at least 5 orders. Which of the following SQL statements will correctly achieve this? (Assume the current date is '2024-01-01 ')
Answer: C
Explanation:
Option E is the correct and most clear solution. It calculates the 3-month moving average, filters customers who have placed at least 5 orders, and leverages the power and clarity of Snowflake syntax. The QUALIFY clause effectively filters for customers with at least 5 orders. The 'RANGE BETWEEN INTERVAL '3 MONTH' PRECEDING AND CURRENT ROW accurately calculates the moving average over a 3- month window based on A, B and C calculate a simple moving average of the last 3 rows regardless of date, while D is syntactically invalid as HAVING cannot be used with window function in this way.
NEW QUESTION # 46
How do secure views enhance data analysis practices?
Answer: D
Explanation:
Secure views offer enhanced data security by allowing selective data access, benefiting analysis while maintaining security.
NEW QUESTION # 47
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