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| Section | Objectives |
|---|---|
| Topic 1: Data Transformation and Analysis | - SQL-based transformations
|
| Topic 2: Security, Governance, and Data Sharing | - Data sharing and governance
|
| Topic 3: Snowflake Architecture and Data Platform Fundamentals | - Data platform fundamentals
|
| Topic 4: Data Modeling and Performance Optimization | - Modeling approaches in Snowflake
|
| Topic 5: Data Loading and Unloading | - Data ingestion methods
|
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NEW QUESTION # 57
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: E
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 # 58
You are tasked with migrating data from an existing relational database to Snowflake. The database contains a 'Customers' table (CustomerlD, Name, City, State) and an 'OrderS table (OrderlD, CustomerlD, OrderDate, TotalAmount). A critical requirement is to maintain data integrity and quickly identify any orphaned records after the initial load. Which approach provides the MOST efficient and robust method to identify orphaned 'Orders' (orders where the CustomerlD does not exist in the 'Customers' table) in Snowflake, considering large data volumes and performance?
Answer: B
Explanation:
A 'LEFT OUTER JOIN' (or 'LEFT JOIN') is the most efficient and direct method for identifying orphaned records in the 'Orders' table. By joining 'Orders' to 'Customers' on 'CustomerlD, and then filtering for records where 'Customers.CustomerlD' is NULL, you directly isolate the orders with no corresponding customer. 'NOT EXISTS' (option B) can also work, but it is often less performant than a left join with a NULL check, especially with large datasets. A full outer join (option A) would show both orphaned orders and orphaned customers, which isn't the specific goal. Using a stored procedure with a cursor (option D) is highly inefficient and not scalable. The UDF fuzzy match approach (option E) is unnecessarily complex and not designed for direct identification of orphaned foreign keys.
NEW QUESTION # 59
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: D
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 # 60
You are responsible for maintaining a dashboard displaying real-time website traffic data'. The data is ingested into a Snowflake table named 'WEB EVENTS using Snowpipe from cloud storage. The 'WEB EVENTS' table includes 'EVENT TIMESTAMP' , 'PAGE URL' , and 'USER ID columns. The dashboard requires near real-time updates, but you are noticing significant latency. Which of the following actions, performed in isolation, is LEAST likely to improve the dashboard update frequency?
Answer: C
Explanation:
Reducing the frequency of micro-batch data loading (option E) is LEAST likely to improve dashboard update frequency; in fact, it will decrease it. The dashboard needs near real-time updates, so reducing how often data is loaded will directly conflict with that requirement. All the other options are designed to help with optimization. Optimizing Snowpipe configuration, creating materialized views for pre-aggregation, and increasing warehouse size for Snowpipe can help, too. Reducing the amount of aggregations that dashboard needs will help since less processing is necessary.
NEW QUESTION # 61
Which considerations are part of best practice for ensuring data integrity structures in Snowflake?
(Select all that apply)
Answer: B,C
Explanation:
Data integrity practices in Snowflake involve using primary keys for tables and establishing effective parent-child table joins.
NEW QUESTION # 62
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