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Snowflake SOL-C01 Exam Syllabus Topics:

Section	Objectives
Security & Data Protection	- Access control and governance 1. Role-based access control (RBAC) 2. Data encryption concepts
Data Loading & Unloading	- Data ingestion methods 1. Bulk loading with stages 2. Continuous data loading concepts
Data Transformation & Querying	- SQL and query processing 1. Query execution in Snowflake 2. Data transformation techniques
Snowflake Architecture & Data Platform	- Snowflake architecture concepts 1. Virtual warehouses 2. Separation of compute and storage
Performance & Optimization	- Performance tuning 1. Warehouse scaling strategies 2. Caching mechanisms
Account & Resource Management	- Snowflake account administration 1. Billing and usage tracking 2. Resource monitors

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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q113-Q118):

NEW QUESTION # 113
You are tasked with loading data from a series of CSV files stored in an Amazon S3 bucket into Snowflake. The CSV files contain a header row, but some files have slight variations in the number and order of columns. You want to ensure that all relevant data is loaded correctly, even if the column order differs, and that any extra columns are ignored. Which of the following approaches is the MOST appropriate and efficient?

A. Pre-process the CSV files to standardize the column order and names before loading them into Snowflake.
B. Define a single external table with a VARIANT column and use Snowflake's CSV parsing capabilities to load all files into that column. Then, extract the relevant data using JSON path expressions.
C. Create a separate external table for each CSV file with a different column structure.
D. Create a VIEW on top of the external table to ensure that column names are consistent across all files.
Then load the data into view.
E. Create a single target table with all possible columns from all CSV files, using 'SKIP_HEADER = 1' and explicitly map the columns in the 'COPY INTO' statement to the correct columns in the target table, using the 'FILE FORMAT option to specify the correct field delimiter.

Answer: E

Explanation:
Creating a single target table with all possible columns and explicitly mapping the columns in the
'COPY INTO' statement is the most appropriate. This approach handles variations in column order by explicitly mapping columns from the CSV files to the target table. It is more performant than VARIANT, and doesn't require external preprocessing. Option A is not scalable and difficult to maintain. Option B is suitable for schema evolution but is not recommended if schemas are already known. Option D loading data into view, is not direct approach, and requires external table and COPY command need a table not view to load. Option E pre-processing helps if data consistency is high priority, but adds complexity to workflow and is not part of Snowflake functionalities.

NEW QUESTION # 114
A company is experiencing inconsistent query performance in their Snowflake environment.
Some queries execute quickly, while others, seemingly similar, take significantly longer. They suspect that the virtual warehouse sizing is not optimal. Which of the following strategies would be the MOST comprehensive approach to diagnosing and resolving this issue, assuming you have access to Snowflake's monitoring tools and SQL?

A. Cluster the tables that are frequently joined together, especially those involved in slow queries.Also, monitor average query execution time based on warehouse size over time to find an optimal size.
B. Continuously monitor the warehouse's CPU utilization and memory usage during peak hours. If utilization is consistently high, immediately upgrade the warehouse to the next larger size.
C. Implement Resource Monitors to limit the credit usage of specific users or groups. This will prevent a single user from monopolizing warehouse resources and impacting other users' query performance.
D. Use the Snowflake web interface to review query history and identify queries with long execution times. Analyze the query profiles of these slow queries to pinpoint performance bottlenecks such as full table scans or inefficient joins.
E. Enable the Query Acceleration Service. This will automatically improve query performance.

Answer: A,D

Explanation:
While monitoring CPU utilization (A) is helpful, it's not the most comprehensive. Query Acceleration service (B) may help to accelerate, but will not diagnose the underlying issue.
Resource Monitors (D) address credit usage but don't directly improve query performance. The most comprehensive approach is to analyze slow queries using query profiles (C) to understand bottlenecks. Then you must improve data locality by Clustering Tables(E). Also, the sizing of a warehouse needs monitoring over time to ensure its performing at it's best. The best choice is to first identify the root cause of performance issues (C, E) before taking action.

NEW QUESTION # 115
You are tasked with creating a secure data sharing environment in Snowflake. You need to share a table named 'CUSTOMER RECORDS' from the `SALES DB' database in your account (Account A) with another Snowflake account (Account B). You want to ensure that Account B can only query the data and cannot modify it in any way. Which of the following steps are REQUIRED to achieve this securely and effectively?

A. Create a database in Account B from the share provided by Account A, and grant SELECT privilege to the roles in Account B that need access.
B. Create a share object in Account A and grant USAGE privilege on the database and SELECT privilege on the table to the share.
C. Grant OWNERSHIP on the `CUSTOMER RECORDS table to the target account (Account B).
D. Create an external function that reads the data from Account A and presents it to Account B.
E. Create a masking policy on all the columns of the 'CUSTOMER RECORDS table before creating the share object.

Answer: A,B

Explanation:
To share data securely, you need to create a share object and grant appropriate privileges in the source account (A). This includes USAGE on the database and SELECT on the table. The target account (B) then creates a database from the share, which is a read-only copy of the data.
Finally, SELECT privilege must be granted to roles within account B to allow access. Granting OWNERSHIP to the target account (C) defeats the purpose of secure sharing and isn't required or best practice. External functions (D) are not the correct approach. Masking Policies (E) might be appropriate based on data sensitivity but aren't a fundamental requirement for simple data sharing.

NEW QUESTION # 116
What Snowflake object is used to store data?

A. View
B. Virtual warehouse
C. Stored procedure
D. Table

Answer: D

Explanation:
A table is the core Snowflake object used to store structured data. Tables persist rows and columns in Snowflake's storage layer, which internally uses compressed, columnar micro-partitions managed by the platform. Tables can be permanent, transient, or temporary, but in every case, they are the primary objects that hold the actual data.
A virtual warehouse is a compute resource used to process queries and perform operations; it does not store user data. A stored procedure contains executable logic (such as complex workflows or procedural code) but does not serve as a data container. A view is a logical object that presents the results of a saved query; it references underlying tables and does not store its own data. As a result, "Table" is the correct answer.

NEW QUESTION # 117
What is the primary purpose of the CLASSIFY_TEXT function in Cortex?

A. Generating text completions based on a given prompt
B. Extracting data from documents
C. Classifying text into predefined categories
D. Translating text to different languages

Answer: C

Explanation:
TheCLASSIFY_TEXTfunction performscategory assignmentfor free-form text. Users provide a list of predefined labels, and the function predicts the most appropriate category using a Snowflake-optimized LLM.
It is ideal for automatically tagging customer feedback, routing support tickets, labeling product reviews, classifying email content, and segmenting operational logs.
The function outputs both the predicted label and an associated confidence score, enabling downstream filtering, dashboarding, and automated processing.
Other options belong to different Cortex functions:
* TRANSLATEhandles multilingual translation.
* COMPLETEgenerates natural language completions.
* PARSE_DOCUMENTextracts structured information from documents.
Thus, CLASSIFY_TEXT is task-specific and optimized specifically for classification workloads.

NEW QUESTION # 118
......

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