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| Section | Objectives |
|---|---|
| Data Loading and Unloading | - Data export
|
| Snowflake Architecture and Data Platform Fundamentals | - Data platform fundamentals
|
| Security, Governance, and Data Sharing | - Data sharing and governance
|
| Data Transformation and Analysis | - Analytical workloads
|
| Data Modeling and Performance Optimization | - Performance tuning
|
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NEW QUESTION # 52
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 # 53
A company ingests sensor data into a Snowflake table named READINGS with columns (VARCHAR), 'reading_time' (TIMESTAMP NTZ), and 'raw_value' (VARCHAR). The 'raw_value' column contains numeric data represented as strings, but sometimes includes non-numeric characters (e.g., '123.45', 'N/A', '500'). You need to calculate the average of the numeric raw_value' readings for each within the last hour, excluding invalid readings. Which of the following Snowflake SQL statements will correctly accomplish this, handling potential conversion errors and filtering for valid data?
Answer: D
Explanation:
Option B is the correct answer because 'TRY TO NUMBER attempts to convert the 'raw_value' to a number, returning NULL if the conversion fails. The 'AND TRY_TO_NUMBER(raw_value) IS NOT NULL' clause then filters out these NULL values, ensuring only valid numeric readings are included in the average calculation. Option A will throw an error if it encounters a non-numeric value. Option C, while functionally correct, utilizes which can be less reliable for specific locale formats compared to Option D is unnecessarily complex and less readable. Option E only handles 'N/A', not other potential invalid values.
NEW QUESTION # 54
How do row access policies and Dynamic Data Masking affect the creation and maintenance of reports and dashboards?
Answer: A
Explanation:
Row access policies restrict data visibility based on user privileges, ensuring better security in creation and maintenance of reports and dashboards.
NEW QUESTION # 55
You are tasked with ingesting clickstream data from a website into Snowflake for real-time analytics. The website generates approximately 100,000 events per minute. The business requires insights into user behavior with a maximum latency of 5 minutes. Which data collection strategy would be MOST appropriate to meet these requirements, considering both cost and near real-time needs?
Answer: A
Explanation:
Near real-time ingestion using Snowpipe with auto-ingest is the most appropriate choice. It provides low latency (minutes), scales well with high data volume, and is cost-effective compared to maintaining a custom solution or paying for a full-fledged ETL tool for this specific scenario. Batch processing introduces too much latency (15 minutes or more), scheduled tasks can become resource intensive and JDBC inserts directly from the application can create performance bottlenecks and security concerns.
NEW QUESTION # 56
You are building a Snowsight dashboard that visualizes website traffic data'. The data includes a column 'visit_timestamp' (TIMESTAMP NTZ) and you need to display the number of unique visitors per hour for the last 24 hours. You also want to allow users to filter the data by country. You plan to use a chart to visualize the trend. Which of the following approaches are the MOST efficient and accurate for achieving this?
Answer: B,E
Explanation:
Option A is a good approach because 'DATE _ TRUNC is efficient for truncating timestamps, and applying the filter directly in the SQL query can optimize data retrieval. Option D is also very efficient, as it pre-aggregates the data, which improves dashboard performance. Option B is less efficient because the 'HOUR' function only returns the hour value without the date, making it harder to filter for last 24 hours. Also, dashboard-level filters can sometimes be less performant than SQL-level filters for large datasets. Option C introduces a calculated field, which is generally less efficient than performing the transformation directly in SQL. Option E is technically correct, it only applies to TEXT stored visit timestamp. Thus, pre-aggregation and 'DATE_TRUNC' are superior.
NEW QUESTION # 57
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