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| Section | Objectives |
|---|---|
| Data Modeling and Performance Optimization | - Performance tuning
|
| Security, Governance, and Data Sharing | - Access control and security
|
| Data Loading and Unloading | - Data ingestion methods
|
| Snowflake Architecture and Data Platform Fundamentals | - Snowflake architecture concepts
|
| Data Transformation and Analysis | - Analytical workloads
|
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NEW QUESTION # 26
What key considerations should be addressed while selecting data sources for retrieval in a collection system? (Select all that apply)
Answer: A,B,C
Explanation:
When selecting data sources, ensuring data consistency, retrieval speed, and format compatibility are crucial to maintain accuracy and efficiency in data retrieval.
NEW QUESTION # 27
A data analyst is tasked with loading data into a Snowflake table 'ORDERS' with the following structure: 'CREATE TABLE ORDERS ( ORDER ID INT, CUSTOMER ID INT, ORDER DATE DATE, TOTAL_AMOUNT The data analyst needs to ensure that 'ORDER ID' is unique and not null, 'CUSTOMER ID' references a valid customer in the 'CUSTOMERS' table (column name 'CUSTOMER ID'), and 'ORDER DATE' is not in the future. Which of the following combination of constraints is the most efficient and appropriate way to enforce these Fules in Snowflake? 'CREATE TABLE CUSTOMERS ( CUSTOMER ID INT PRIMARY KEY, CUSTOMER_NAME VARCHAR(255));'.
Answer: C
Explanation:
Option E is the most appropriate and efficient. Using a PRIMARY KEY implies an index which while beneficial for joins isn't necessary if simple uniqueness and not null are the primary requirement. A CHECK constraint 'ORDER_DATE <= is the best way to prevent future dates as TRIGGER is not available and view doesn't prevent data from being ingested.
NEW QUESTION # 28
A Data Analyst runs this query:
The Analyst men runs this query:
What will be the output?
Answer: C
Explanation:
Understanding how Snowflake aggregate functions like MIN() and MAX() handle numerical data and NULL values is fundamental for accurate Data Analysis. In this scenario, we have a table with five records distributed across two departments.
The MIN() function returns the smallest non-null value in the specified column across all rows in the group (or the entire table, if no GROUP BY is present). Looking at the salary column in the employees table, the values are: 10000, 9000, 8000, 15000, and NULL. The NULL value is ignored by the calculation. Among the remaining numerical values, 8000 is the smallest. Therefore, MIN_VAL will be 8000.
The MAX() function operates similarly, returning the largest non-null value from the set. Comparing the same list of numerical values-10000, 9000, 8000, and 15000-the largest value is clearly 15000. Consequently, MAX_VAL will be 15000.
Evaluating the Options based on the exhibit (image_8ca0df.png):
* Option A incorrectly identifies the minimum and maximum based on the employee_id column (where
2000 is max and 900 is min), rather than the requested salary column.
* Option B is incorrect as it seems to mix values from different columns or specific rows.
* Option C is incorrect because it mistakenly suggests that MIN() returns NULL if a NULL value is present in the column. In Snowflake, standard aggregate functions (except for specialized ones like ARRAY_AGG or certain window functions with specific clauses) skip NULL values entirely.
* Option D is the 100% correct output. It displays MIN_VAL as 8000 and MAX_VAL as 15000, which accurately reflects the mathematical minimum and maximum of the non-null entries in the salary column.
This behavior is consistent across all relational databases adhering to ANSI SQL standards, which Snowflake follows for these core aggregate operations.
NEW QUESTION # 29
You are working with a Snowflake environment where data analysts need to query Parquet files located in AWS S3. The analysts require the ability to perform complex aggregations and joins with other Snowflake tables. Some of the Parquet files have evolved their schema over time, adding new columns. Given these requirements, which set of actions would provide the MOST efficient and maintainable solution? Choose the THREE most appropriate actions.
Answer: C,E,F
Explanation:
'AUTO REFRESH = TRUE' handles schema evolution in external tables. Materialized views on the top of external tables provides precomputed data, which is good for aggregations. The VALIDATE command allows analysts to check the stage data integrity and avoid errors in the data pipeline. Ingesting all data into a single VARIANT column is not efficient for aggregations and joins. Extracting to temporary tables is not maintainable. Periodically creating new tables is complex to manage. Adjusting can help improve performance.
NEW QUESTION # 30
What actions are involved in performing general DML (Data Manipulation Language) operations in Snowflake? (Select all that apply)
Answer: B,C,D
Explanation:
General DML operations in Snowflake include inserting, updating, and deleting data.
NEW QUESTION # 31
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