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| Topic | Details |
|---|
| Topic 1 | - Identity and Data Access Management: This domain focuses on Role-Based Access Control (RBAC) including role hierarchies and privileges, along with basic database administration tasks like creating objects, transferring ownership, and executing fundamental SQL commands.
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| Topic 2 | - Data Protection and Data Sharing: This domain addresses continuous data protection through Time Travel and cloning, plus data collaboration capabilities via Snowflake Marketplace and private Data Exchange sharing.
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| Topic 3 | - Interacting with Snowflake and the Architecture: This domain covers Snowflake's elastic architecture, key user interfaces like Snowsight and Notebooks, and the object hierarchy including databases, schemas, tables, and views with practical navigation and code execution skills.
|
| Topic 4 | - Data Loading and Virtual Warehouses: This domain covers loading structured, semi-structured, and unstructured data using stages and various methods, virtual warehouse configurations and scaling strategies, and Snowflake Cortex LLM functions for AI-powered operations.
|
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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q27-Q32):
NEW QUESTION # 27
Which of the following statements about External Tables in Snowflake are true?
- A. External tables require a virtual warehouse to be online for data access.
- B. Changes to the underlying files in the external stage are automatically reflected in the external table without any manual refresh.
- C. External tables can be directly updated using standard SQL DML statements (INSERT, UPDATE, DELETE).
- D. External tables store data within Snowflake's internal storage.
- E. External tables provide read-only access to data stored in external cloud storage locations.
Answer: A,E
Explanation:
Options C and D are correct. External tables provide read-only access to data residing in external cloud storage (e.g., Amazon S3, Azure Blob Storage, Google Cloud Storage), and they require a virtual warehouse to be online for querying the data. A is incorrect because external tables do not store data in Snowflake's internal storage. B is incorrect because external tables are read-only and cannot be directly updated with DML. E is incorrect because manual refreshing/metadata caching is required after changes to the source data.
NEW QUESTION # 28
What parameter is used to define how long Time Travel can be used to access a table?
- A. TIMEZONE
- B. DATE_OUTPUT_FORMAT
- C. USE_CACHED_RESULT
- D. DATA_RETENTION_TIME_IN_DAYS
Answer: D
Explanation:
The DATA_RETENTION_TIME_IN_DAYS parameter controls the Time Travel retention period for Snowflake objects such as tables, schemas, and databases. It specifies the number of days that historical data (prior versions of rows or dropped objects) is retained and accessible via Time Travel. Within this retention window, users can query data "as of" a previous time, restore dropped objects, or clone objects at a historical point.
DATE_OUTPUT_FORMAT determines the display format for date values and is unrelated to historical retention. TIMEZONE affects how timestamps are interpreted and displayed, not how long data history is preserved. USE_CACHED_RESULT governs whether Snowflake may return cached query results, not Time Travel behavior.
NEW QUESTION # 29
You have a base table called `customer data' which contains sensitive information like credit card numbers. You need to create a view called that exposes only non-sensitive columns (e.g., customer_id, customer_name, city) and hides the logic of the underlying table's structure.
Additionally, you want to ensure that no user can directly query the 'customer_data' table. Which of the following steps are necessary to achieve this?
- A. Create a materialized view `customer_summary' selecting only the desired columns from
`customer_data'. Revoke SELECT privilege on `customer_data' from all roles except the role that owns the table. - B. Create a secure view `customer_summary' selecting all columns from `customer_data'. Grant SELECT privilege on `customer_summary' to the required roles.
- C. Create a secure view `customer_summary' selecting only the desired columns from
'customer_data' .
Revoke SELECT privilege on `customer_data' from all roles except the role that owns the table. - D. Create a standard view `customer_summary' selecting only the desired columns from
'customer_data' . Grant SELECT privilege on to the required roles. Do not revoke privileges on the base table. - E. Create a standard view `customer_summary' selecting only the desired columns from
`customer_data'. Revoke SELECT privilege on `customer_data' from all roles except the role that owns the table.
Answer: C
Explanation:
Option C provides the correct solution. Creating a secure view 'customer_summary' hides the base table structure from users and ensures that they can only access the data through the view.
Revoking SELECT privileges on the `customer_data' table prevents direct access to the sensitive data, making it accessible only through the view, and only to those with privilege on the view.
Materialized views are not appropriate here because their data is physically stored, and the need is to hide the base table not improving the performance. Option D is incorrect because without revoking the base table privilege, users can query the base table directly. Option E will display all the column.s
NEW QUESTION # 30
Which SQL command is commonly used to load structured data from a stage into a Snowflake table?
- A. INSERT INTO
- B. IMPORT DATA
- C. COPY INTO
- D. LOAD DATA
Answer: C
Explanation:
The COPY INTO <table> command is Snowflake's primary and optimized mechanism for loading large volumes of structured data from either internal or external stages. Unlike INSERT INTO, which is used for row-level inserts or small datasets, COPY INTO is designed for high-throughput operations, parallel loading, and performant ingestion from files stored in S3, Azure Blob Storage, GCS, or Snowflake-managed stages.
The COPY INTO command supports detailed file formatting options (such as TYPE=CSV, FIELD_DELIMITER, SKIP_HEADER), error-handling rules (ON_ERROR), and transformations using SQL expressions inside the COPY statement. Snowflake does not support LOAD DATA or IMPORT DATA as SQL commands; these exist in other database systems but not within Snowflake. COPY INTO ensures efficient, resilient, fault-tolerant data ingestion and is heavily used for ETL/ELT, data lake ingestion, and large batch ingestion pipelines.
NEW QUESTION # 31
You are loading data from a CSV file stored in an AWS S3 bucket into a Snowflake table. The CSV file uses a custom delimiter and contains a date field that needs to be explicitly formatted during the load. Which combination of 'COPY INTO* options BEST addresses these requirements? Assume an existing stage named 's3_stage'.

- A. Option E
- B. Option C
- C. Option B
- D. Option D
- E. Option A
Answer: E
Explanation:
Option A correctly specifies the custom field delimiter (FIELD DELIMITER = and the explicit date format (DATE FORMAT = 'YYYY-MM-DD") to handle the CSV loading and date transformation requirements effectively. Option B uses 'DATE_FORMAT = 'AUTO" , which might not correctly parse the date if it's not in a standard format. Options C, D and E don't contain 'ON ERROR to ensure that, in case of error the load continues
NEW QUESTION # 32
......
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