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>> SOL-C01 Free Exam Questions <<
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NEW QUESTION # 160
A security architect is designing a role hierarchy in Snowflake for a data analytics team. They need to grant specific privileges to different user groups. 'DATA ENGINEER' role should have the ability to create and manage databases. 'DATA ANALYST' role should be able to query data from those databases. 'REPORT USER' role needs read-only access to specific views. The architect wants to ensure minimum privilege and enforce role separation. Which of the following sequences of SQL commands would correctly establish this role hierarchy and grant necessary privileges?
Answer: D
Explanation:
Option E is the most complete answer. It creates the roles, grants necessary database creation privileges to DATA_ENGINEER, allows DATA ANALYST to use and query the database, and REPORT USER to use the database and select from the specified view. The role grants establish the hierarchy. Options A and B have incorrect grant statements for select on all tables in the database and lack the creation of Roles at the begining. Option D does not grant the roles to each other, leaving the hierarchy incomplete, and incorrectly attempts to grant select on future tables directly to a view.
NEW QUESTION # 161
A data engineer needs to create a table named 'EMPLOYEES in the `PUBLIC' schema of the database 'COMPANY DATA'. The table should store employee IDs as integers, names as strings, and hire dates as dates. Which of the following SQL statements correctly creates this table?
Answer: C
Explanation:
The correct answer is E. It uses 'CREATE OR REPLACE to avoid errors if the table exists, specifies the full table name with database and schema ('COMPANY_DATA.PUBLIC.EMPLOYEES'), and uses appropriate data types: 'INT' for integers,
'VARCHAR(255)' for strings, and 'DATE for dates. Option A would cause errors if table already exists, Option B and C does not specify the database and schema. Option D uses NUMBER datatype.
NEW QUESTION # 162
You are using the 'COPY INTO' command to load data from an Amazon S3 bucket into a Snowflake table named 'employees'. The data in S3 is in CSV format, compressed with gzip, and contains a header row. You want to skip the header row during the load and handle any data conversion errors by continuing the load operation. Which of the following 'COPY INTO' commands is most appropriate?
Answer: E
Explanation:
Option A is the most appropriate. It correctly specifies the S3 bucket and file, CSV file format, skips the header row CSKIP_HEADER = and handles data conversion errors by continuing the load CON_ERROR = 'CONTINUE"). While other options might partially address the requirements, option A is complete and adheres to correct syntax. Option B uses 'SKIP_FILE' which would skip the whole file on error. Option C is closer, but adding `ERROR = FALSE is not directly related to handling data conversion errors. option D is incorrect because 'ON_ERROR = CONTINUE is not a valid syntax, it requires to be in single quotes 'ON ERROR = 'CONTINUE".
Option E is also good but 'FIELD DELIMITER = is not required here and is not adding value.
NEW QUESTION # 163
You are using Snowflake notebooks to build a data pipeline. You want to parameterize a SQL query using Python variables to filter data based on a date range provided as input to the notebook. You have and 'end_date' Python variables. Which of the following is the most secure and efficient way to incorporate these variables into a SQL query executed within a Snowflake notebook using Snowpark?
Answer: E
Explanation:
Option B is the most secure and efficient way. Snowpark's 'session.sql()' method supports parameterized queries using ':1 , `:2' , etc., to represent placeholders for the Python variables.
This prevents SQL injection vulnerabilities and allows Snowflake to optimize the query execution plan by reusing it with different parameter values. Option A is vulnerable to SQL injection. Option C is a less secure string formatting method. Option D is prone to errors and SQL injection. Option E is not a valid way to parameterize the query in Snowflake with Snowpark API.
NEW QUESTION # 164
What is the Snowsight Query Profile used for?
Answer: D
Explanation:
The Snowsight Query Profile is a powerful diagnostic tool that provides a visual breakdown of how Snowflake executed a query. Its primary purpose is to help users visualize and analyze query performance. It displays execution steps, including scan operations, join strategies, pruning results, aggregation methods, and data movement between processing nodes.
The profile shows metrics such as execution time per step, partition pruning effectiveness, bytes scanned, and operator relationships. This allows developers, analysts, and DBAs to identify bottlenecks-such as unnecessary full-table scans, non-selective filters, or inefficient joins-and tune SQL accordingly.
Query Profile does not execute queries; execution happens in worksheets or programmatic interfaces. It does not create objects or manage data loading; those tasks involve separate SQL commands and UI interfaces.
Overall, Query Profile is essential for performance tuning, helping teams reduce compute costs, optimize warehouse sizing, and improve query efficiency.
NEW QUESTION # 165
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