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| Section | Objectives |
|---|---|
| Data Transformation and Analysis | - Analytical workloads
|
| Snowflake Architecture and Data Platform Fundamentals | - Data platform fundamentals
|
| Security, Governance, and Data Sharing | - Access control and security
|
| Data Modeling and Performance Optimization | - Modeling approaches in Snowflake
|
| Data Loading and Unloading | - Data export
|
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NEW QUESTION # 16
You have identified a valuable dataset on the Snowflake Marketplace related to weather patterns. To consume this data, you perform the following actions: 1. You request and receive the data share from the provider. 2. You create a database named 'WEATHER DB' from the share. Now you want to create a secure view named 'DAILY WEATHER SUMMARY in your own database 'ANALYTICS DB.PUBLIC', which joins your internal sales data C ANALYTICS DB.PUBLIC.SALES) with the weather data from the provider's 'WEATHER DB.WEATHER SCHEMA.DAILY WEATHER' table. You only want to expose specific columns from both tables in your view to minimize data exposure. Which of the following steps are required to ensure this secure and functional integration?
Answer: D
Explanation:
Option D is the correct answer. When consuming data from a Snowflake Marketplace data share, you do not need to grant privileges to the provider on your internal data (Option A is incorrect). Option B is incorrect because creating views and acessing tables requires proper schema names. Option C is incorrect because outbound shares are not needed to consume marketplace data and access control is managed within the consumer account. Option E is incorrect because enabling access control policies on a warehouse is irrelevant to the task of creating a secure view that joins internal data with shared data.
NEW QUESTION # 17
You have a Snowflake table containing order data'. You need to calculate the shipping cost for each order based on the order amount and the destination country. You decide to use a Java UDF for this calculation, as the logic is complex and involves external APIs (simulated here). The UDF should take the order amount (FLOAT) and destination country (VARCHAR) as input and return the calculated shipping cost (FLOAT). The Java code requires external JAR files to be imported. Which of the following options correctly defines and calls the Java UDF in Snowflake, assuming the necessary JAR file has been uploaded to a stage named 'my_stage'?
Answer: E
Explanation:
Option E is the most correct because the function definition does not require the definition of the class 'com.example.ShippingCalculator' within the function body. Since the jar file is defined within the imports section, snowflake does not need the explicit definition. Option A, C, and D requires the function and class definition which is already defined in the jar, and defining it again will lead to conflicts. Option B doesn't correctly define the class. All the rest of the options either try to define the Java code inline (which is incorrect when using IMPORTS) or have syntax errors in the UDF definition.
NEW QUESTION # 18
A Data Analyst needs to write a query that will return all projects from a project table and all employees from an employee table. What type of join should be used in this query?
Answer: C
Explanation:
In Data Transformation and Data Modeling, selecting the correct join type determines how the query handles unmatched records from the participating tables. The requirement here is to return all records from both the projects table and the employee table.
A FULL OUTER JOIN (often shortened to FULL JOIN) is designed specifically for this purpose. It combines the results of both a LEFT OUTER JOIN and a RIGHT OUTER JOIN. It returns:
* Rows where there is a match between the project and the employee.
* Rows from the projects table that have no matching employees (padded with NULL in the employee columns).
* Rows from the employees table that are not assigned to any projects (padded with NULL in the project columns).
Evaluating the Options:
* Option A (INNER JOIN) only returns rows where there is a match in both tables. Any projects without employees or employees without projects would be excluded.
* Option C (LEFT OUTER JOIN) would return all projects, but would exclude employees who are not assigned to a project.
* Option D (CROSS JOIN) creates a Cartesian product, matching every single employee with every single project regardless of actual relationships. This would create a massive, redundant dataset and is not what is requested.
* Option B is the 100% correct answer. It ensures total data visibility from both entities, which is often required in data quality audits or comprehensive resource allocation reporting where the analyst needs to see "unlinked" data on both sides of the relationship.
NEW QUESTION # 19
Which aggregate functions can be used to provide values for a given bucket in Snowsight charts? (Select THREE).
Answer: C,E,F
Explanation:
In Snowsight, when you create a chart, you define a bucket (the X-axis or a grouping dimension) and then select an aggregate function to determine the values displayed (the Y-axis). Snowsight provides a specific subset of standard SQL aggregate functions that are optimized for rapid, interactive visualization.
* Any Value (Option A): This function (equivalent to ANY_VALUE) is often used when a dimension is functionally dependent on the bucket, and you simply need to pluck a representative value from the group.
* Average (Option B): This is a fundamental aggregation for identifying the central tendency of a bucketed dataset.
* Max (Option F): Along with SUM, MIN, and COUNT, MAX is a standard, built-in option for visualizing extremes within a data bucket.
Evaluating the Options:
* Options C and D (Standard Deviation and Mode) are common statistical measures, but they are not available as direct, one-click options in the Snowsight chart aggregation menu; they would require custom SQL logic in the worksheet first.
* Option E (HLL) is a probabilistic estimation function. While powerful in SQL, it is not a standard visualization aggregation in the UI.
* Options A, B, and F are the 100% correct options. They represent the native, out-of-the-box functions available to a Data Analyst for summarizing data in Snowsight dashboards.
NEW QUESTION # 20
You are tasked with loading JSON files into Snowflake using Snowsight. The JSON files are semi-structured and contain nested arrays and objects. You want to flatten the JSON structure during the load process to facilitate easier querying. Which of the following Snowsight-integrated features or approaches are suitable for flattening the JSON data during the load process? Select all that apply.
Answer: A,D,E
Explanation:
Options A, C, and E are all suitable approaches for flattening JSON data. Option A leverages 'LATERAL FLATTEN' in SQL, which is a common and efficient way to flatten JSON structures after loading into a VARIANT column. Option C utilizes Snowpipe with a transformation including 'LATERAL FLATTEN' , enabling automatic flattening during ingestion. Option E uses computed columns in Snowsight to extract JSON elements during loading using 'GET_PATH' or the operator. Option B is incorrect; Snowsight's 'Load Data' wizard doesn't automatically flatten JSON during the initial load. It mainly handles the basic load of the JSON file into a VARIANT column, not automatic flattening. Option D is a valid but less performant alternative; UDFs can be slower than native Snowflake SQL functions for large datasets.
NEW QUESTION # 21
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