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| Section | Objectives |
|---|---|
| Data Transformation and Analysis | - SQL-based transformations
|
| Snowflake Architecture and Data Platform Fundamentals | - Snowflake architecture concepts
|
| Security, Governance, and Data Sharing | - Access control and security
|
| Data Loading and Unloading | - Data export
|
| Data Modeling and Performance Optimization | - Modeling approaches in Snowflake
|
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NEW QUESTION # 20
A Data Analyst executes a complex query. Which query will allow the Analyst to access the results a second time?
Answer: B
Explanation:
Snowflake provides a powerful feature called Result Caching. When a query is executed, the results are persisted for 24 hours. To access the result set of a previously executed query without re-running the heavy computation (and thus saving credits), a Data Analyst can use the RESULT_SCAN table function.
The function RESULT_SCAN requires a Query ID as its input. The most common way to programmatically retrieve the ID of the most recently executed statement in the current session is by using the LAST_QUERY_ID() function. By wrapping this in the TABLE() operator, Snowflake treats the cached results as a virtual table that can be queried, filtered, or even joined with other tables.
Evaluating the Options:
* Option B is incorrect because QUERY_HISTORY returns metadata about queries (like start time, user, and status) but does not return the actual data records produced by those queries.
* Option C is incorrect as DESC (Describe) is used to see the columns and data types of an object, not to fetch its row data.
* Option D is incorrect; while LAST_QUERY_ID(-1) might successfully retrieve a string representing a previous ID in some contexts, it does not actually "access the results" or display the data.
* Option A is the 100% correct syntax. It is the standard method used by Data Analysts to perform post- processing on a large result set or to recover a result set if the worksheet was accidentally cleared. This is a key part of the Data Analysis domain, specifically regarding performance optimization and the utilization of Snowflake's unique caching layers.
NEW QUESTION # 21
You have two tables in Snowflake: 'ORDERS and 'CUSTOMERS. The 'ORDERS' table contains information about customer orders, including 'ORDER ID', 'CUSTOMER ID', 'ORDER DATE, and 'ORDER AMOUNT. The 'CUSTOMERS table contains information about customers, including 'CUSTOMER ID', 'CUSTOMER NAME' , and 'CUSTOMER ADDRESS'. You need to create a view that joins these two tables based on 'CUSTOMER ID and includes only orders placed in the last 30 days. You also want to ensure that the view leverages the primary key information defined on the 'CUSTOMERS' table (even though Snowflake doesn't enforce it) for potential query optimizations. Which of the following SQL statements is the MOST efficient and best practice approach, considering Snowflake's optimizer?
Answer: C
Explanation:
Using 'CREATE OR REPLACE SECURE VIEW' is the best practice. 'CREATE OR REPLACE' allows you to update the view definition if needed without dropping and recreating it, minimizing disruption. A 'SECURE VIEW' prevents users from seeing the underlying query logic or accessing the base tables directly, enhancing security. The explicit JOIN syntax is preferred over the older implicit join syntax (option B). Snowflake's optimizer will leverage defined primary key relationships to optimize the join, even though it doesn't enforce them. Explicitly referencing schema isn't needed unless dealing with ambiguous names across schemas. The WHERE clause correctly filters for orders in the last 30 days.
NEW QUESTION # 22
You are analyzing sales data from different regions stored in a Snowflake table named 'sales_data'. The table includes columns: 'transaction_id' (VARCHAR), 'region' (VARCHAR), 'sale_date' (DATE), and 'sale_amount' (NUMBER). You discover the following data quality issues: The 'region' column contains inconsistent entries such as 'North', 'north', 'NOrth ', and ' South'. The 'sale_amount' column has some values that are stored as strings (e.g., '100.50') instead of numbers, causing errors in aggregation. There are duplicate records identified by the same 'transaction id'. Which set of SQL statements, executed in the given order, provides the MOST effective and efficient way to address these data quality issues in Snowflake?
Answer: A
Explanation:
Option E presents the most efficient and effective solution. It combines all three data cleaning steps into a single operation using a CTE. First, standardizes the region name with trim and lowercase. Second, remove duplicate records based on transaction ID. And most important, it correctly handles the 'sale_amount' conversion using TRY_TO_NUMBER inside the CTE to avoid errors and ensures accurate aggregations down stream. This approach minimizes the number of table scans and UPDATE operations, improving performance. Option A fails on how to remove duplicates correctly using TRY_TO_NUMBER to convert the sale amount correctly and data type changes are not possible via ALTER statements if strings are present. Options B, C, and D does not combine all in one single CTE operations and are slower.
NEW QUESTION # 23
You're working with a 'WEB EVENTS' table in Snowflake that stores user activity data'. The table includes columns like 'USER ID' , 'EVENT TIMESTAMP', 'EVENT TYPE (e.g., 'page_view', 'button_click', 'form_submission'), and 'EVENT DETAILS' (a VARIANT column containing JSON data specific to each event type). You need to identify users who submitted a specific form ('contact_us') more than 3 times within a 24-hour period. However, you are concerned about data quality, and the 'EVENT TIMESTAMP' column might contain duplicate entries for the same user and event. Which of the following SQL queries is the MOST robust and efficient way to achieve this in Snowflake, ensuring that duplicate timestamps for the same user and 'contact_us' form submission are not counted multiple times?
Answer: B
Explanation:
Option B is the most robust and efficient for handling potential duplicate timestamps. Here's why: Inner Query & QUALIFY: The inner query filters for the specific event type ('form_submission') and form ID ('contact_us'). The 'QUALIFY' clause, along with OVER (PARTITION BY USER ID, EVENT TIMESTAMP ORDER BY EVENT TIMESTAMP) = 1', efficiently de-duplicates the data. It assigns a row number to each combination of USER_ID and 'EVENT_TIMESTAMP' , and only keeps the first occurrence (ROW_NUMBER = 1), thus removing duplicate timestamp entries for the same user. Outer Query & Aggregation: The outer query then groups the de-duplicated data by 'USER_ID' and uses 'HAVING COUNT( ) > to identify users who have more than 3 distinct form submissions. Here's why the other options are less suitable: A: This option doesn't handle duplicate timestamps. It will count each duplicate timestamp as a separate form submission, leading to inaccurate results. C: Option C has similar function as option B, however it's slightly less elegant using 'WHERE rn = 1 ' than using Snowflake's Qualify command D: This Option aggregates to just Date Level and thus doesn't provide a robust solution within a 24 hour Period. E: Option E uses a 'COUNT( ) OVER clause that doesn't provide de-duplication of the underlying data, thus a duplicate Event timestamp would be counted twice.
NEW QUESTION # 24
A marketing team requires a daily report showcasing website traffic, conversion rates, and cost per acquisition (CPA). They want to receive this report as a CSV file attached to an email. The data is stored in a Snowflake table 'WEB ACTIVITY with columns 'DATE , 'VISITS, , and SPEND. Which of the following steps, combined and executed in the correct order, would be the MOST efficient and secure way to automate this report delivery?
Answer: D
Explanation:
Option B is the most efficient and scalable. Using an external orchestrator provides better scheduling control and separation of concerns. Writing to S3 allows for easier integration with other external services. Python connector is preferred for data transformation and CSV generation compared to Snowflake scripting. Utilizing AWS SES for email delivery scales well and aligns with a cloud-native approach. Option A is viable but can be resource-intensive within Snowflake. Options C, D and E are less efficient due to relying on event-based triggers or third party tools for scheduling and email delivery. Option D exposes the data to a third-party tool. Option E requires complex setup and is not the best choice for a simple daily report.
NEW QUESTION # 25
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