SOL-C01 PDF Dumps Files - SOL-C01 Related Exams

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Snowflake SOL-C01 Exam Syllabus Topics:

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

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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q146-Q151):

NEW QUESTION # 146
A Snowflake account administrator needs to configure a resource monitor to limit the credit consumption of a specific virtual warehouse named 'ANALYTICS WH'. The resource monitor should trigger a notification when the warehouse consumes 80% of its allocated credits within a calendar month and should suspend the warehouse when it reaches 100%. Which of the following SQL statements, executed in the correct order, would achieve this?

A. Option B
B. Option E
C. Option C
D. Option A
E. Option D

Answer: E

Explanation:
The correct syntax for creating a resource monitor with triggers is to use the 'TRIGGERS' clause with the 'DO' keyword followed by the action. 'SUSPEND_IMMEDIATELY ensures the warehouse stops immediately when the limit is reached, preventing further credit consumption. Other options have incorrect syntax or trigger wrong action.

NEW QUESTION # 147
What is the purpose of the USE SCHEMA command in Snowflake?

A. To set the current schema for a session
B. To take ownership of an existing schema
C. To create a new schema in a database
D. To modify the schema structure in a session

Answer: A

Explanation:
The USE SCHEMA command sets the active schema context for the current session. After it is executed, any unqualified object names (for example, SELECT * FROM my_table) are resolved within that schema. This reduces the need to fully qualify object names with database and schema each time and ensures that statements reference the expected logical container.
CREATE SCHEMA is used to create a new schema. ALTER SCHEMA and GRANT OWNERSHIP are used to modify schema properties or transfer ownership, respectively. USE SCHEMA does not alter structure or ownership; it simply changes the context in which subsequent SQL statements are interpreted.

NEW QUESTION # 148
You are loading data into a Snowflake table using the 'COPY INTO' command. The data is in JSON format, and one of the fields in the JSON data, 'timestamp_field', represents a Unix epoch timestamp in seconds. You need to convert this timestamp to a Snowflake timestamp data type during the loading process. Which of the following 'COPY INTO' statements would correctly perform this conversion?

Answer: D

Explanation:
The correct solution uses 'TO_TIMESTAMP' with the result of , 'timestamp_field')' cast to NUMBER (since epoch time is a number). `GET PATH' returns a VARIANT, which needs to be explicitly cast to the correct data type before being passed to TO_TIMESTAMP. The cast converts the VARIANT to a numeric value. The other options either don't correctly cast the VARIANT or use an incorrect TO TIMESTAMP function variation.

NEW QUESTION # 149
A data engineer needs to load JSON files containing customer reviews stored in an external stage 's3://my-bucket/reviews/'. The JSON structure varies significantly between files, but the goal is to extract 'review_id', 'customer_id', and 'review text' into a Snowflake table 'CUSTOMER REVIEWS'. The engineer is using a COPY INTO statement with a transform. Which of the following is the MOST efficient and correct way to achieve this?

A. Use a COPY INTO statement with a JSON path expression to extract the required fields during the load. If a field is missing, handle it with 'NULLIF or similar functions during the COPY INTO.
B. Create a custom file format that defines the expected JSON structure, even though it varies, and rely on Snowflake to handle the variations during the COPY INTO operation.
C. Use a VARIANT column in 'CUSTOMER_REVIEWS' and load the entire JSON structure. Then, use SQL to extract the required fields during querying.
D. Write a Python UDF to parse the JSON, extract the required fields, and then use the UDF in a COPY INTO statement.
E. Create separate tables for each JSON structure variation and then use a UNION ALL view to combine the data.

Answer: A

Explanation:
Option C is the most efficient. Using a JSON path expression in the COPY INTO statement allows extracting the desired fields directly during the load, avoiding the need to load the entire JSON and then process it. 'NULLIF or similar functions can be used to handle cases where a field might be missing in some JSON documents. Options A, B, D, and E are less efficient or impractical. Loading the entire JSON (A) requires post-processing. Creating separate tables (B) is complex and doesn't scale well. Python UDF (D) introduces overhead and performance limitations. Relying on a custom format without explicitly handling variations (E) will lead to errors.

NEW QUESTION # 150
How can you query semi-structured data (e.g., JSON) stored in a Snowflake VARIANT column?

A. Using standard SQL WHERE clauses on the entire VARIANT column.
B. Only through stored procedures.
C. Using path notation to access specific elements within the VARIANT
D. By converting the VARIANT column to a relational table first.

Answer: C

Explanation:
Semi-structured data like JSON stored in a VARIANT column can be queried directly usingpath notation, either dot notation or bracket notation.
Examples:
* Dot notation:SELECT data:customer.name FROM table;
* Bracket notation:SELECT data['customer']['name'] FROM table;
Snowflake automatically interprets the JSON structure, making relational extraction unnecessary. Complex fields can be accessed through functions such as FLATTEN, OBJECT_KEYS, ARRAY_SIZE, and TYPEOF.
Incorrect options:
* Converting JSON to a relational table is optional, not required.
* Stored procedures are not necessary for JSON querying.
* WHERE on the full VARIANT cannot precisely extract fields.
Thus, path notation is the native, efficient method.

NEW QUESTION # 151
......

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