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[General] SnowPro Advanced Architect Certification Exam Practice Torrent & ARA-C01 Rea

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【General】 SnowPro Advanced Architect Certification Exam Practice Torrent & ARA-C01 Rea

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Snowflake SnowPro Advanced Architect Certification Sample Questions (Q95-Q100):NEW QUESTION # 95
Which of the following are characteristics of how row access policies can be applied to external tables?
(Choose three.)
  • A. A row access policy can be applied to the VALUE column of an existing external table.
  • B. While cloning a database, both the row access policy and the external table will be cloned.
  • C. External tables are supported as mapping tables in a row access policy.
  • D. An external table can be created with a row access policy, and the policy can be applied to the VALUE column.
  • E. A row access policy cannot be applied to a view created on top of an external table.
  • F. A row access policy cannot be directly added to a virtual column of an external table.
Answer: A,D,F
Explanation:
Explanation
These three statements are true according to the Snowflake documentation and the web search results. A row access policy is a feature that allows filtering rows based on user-defined conditions. A row access policy can be applied to an external table, which is a table that reads data from external files in a stage. However, there are some limitations and considerations for using row access policies with external tables.
* An external table can be created with a row access policy by using the WITH ROW ACCESS POLICY clause in the CREATE EXTERNAL TABLE statement. The policy can be applied to the VALUE column, which is the column that contains the raw data from the external files in a VARIANT data type1.
* A row access policy can also be applied to the VALUE column of an existing external table by using the ALTER TABLE statement with the SET ROW ACCESS POLICY clause2.
* A row access policy cannot be directly added to a virtual column of an external table. A virtual column is a column that is derived from the VALUE column using an expression. To apply a row access policy to a virtual column, the policy must be applied to the VALUE column and the expression must be repeated in the policy definition3.
* External tables are not supported as mapping tables in a row access policy. A mapping table is a table that is used to determine the access rights of users or roles based on some criteria. Snowflake does not support using an external table as a mapping table because it may cause performance issues or errors4.
* While cloning a database, Snowflake clones the row access policy, but not the external table. Therefore, the policy in the cloned database refers to a table that is not present in the cloned database. To avoid this issue, the external table must be manually cloned or recreated in the cloned database4.
* A row access policy can be applied to a view created on top of an external table. The policy can be applied to the view itself or to the underlying external table. However, if the policy is applied to the view, the view must be a secure view, which is a view that hides the underlying data and the view definition from unauthorized users5.
References:
* CREATE EXTERNAL TABLE | Snowflake Documentation
* ALTER EXTERNAL TABLE | Snowflake Documentation
* Understanding Row Access Policies | Snowflake Documentation
* Snowflake Data Governance: Row Access Policy Overview
* Secure Views | Snowflake Documentation

NEW QUESTION # 96
What are characteristics of the use of transactions in Snowflake? (Select TWO).
  • A. Explicit transactions can contain DDL, DML, and query statements.
  • B. The autocommit setting can be changed inside a stored procedure.
  • C. Explicit transactions should contain only DML statements and query statements. All DDL statements implicitly commit active transactions.
  • D. A transaction can be started explicitly by executing a begin transaction statement and end explicitly by executing an end transaction statement.
  • E. A transaction can be started explicitly by executing a begin work statement and end explicitly by executing a commit work statement.
Answer: A,D
Explanation:
In Snowflake, a transaction is a sequence of SQL statements that are processed as an atomic unit. All statements in the transaction are either applied (i.e. committed) or undone (i.e. rolled back) together. Snowflake transactions guarantee ACID properties. A transaction can include both reads and writes1.
Explicit transactions are transactions that are started and ended explicitly by using the BEGIN TRANSACTION, COMMIT, and ROLLBACK statements. Snowflake supports the synonyms BEGIN WORK and BEGIN TRANSACTION, and COMMIT WORK and ROLLBACK WORK. Explicit transactions can contain DDL, DML, and query statements. However, explicit transactions should contain only DML statements and query statements, because DDL statements implicitly commit active transactions. This means that any changes made by the previous statements in the transaction are applied, and any changes made by the subsequent statements in the transaction are not part of the same transaction1.
The other options are not correct because:
B) The autocommit setting can be changed inside a stored procedure, but this does not affect the use of transactions in Snowflake. The autocommit setting determines whether each statement is executed in its own implicit transaction or not. If autocommit is enabled, each statement is committed automatically. If autocommit is disabled, each statement is executed in an implicit transaction until an explicit COMMIT or ROLLBACK is issued. Changing the autocommit setting inside a stored procedure only affects the statements within the stored procedure, and does not affect the statements outside the stored procedure2.
C) A transaction can be started explicitly by executing a BEGIN WORK statement and end explicitly by executing a COMMIT WORK statement, but this is not a characteristic of the use of transactions in Snowflake. This is just one way of writing the statements that start and end an explicit transaction. Snowflake also supports the synonyms BEGIN TRANSACTION and COMMIT, which are recommended over BEGIN WORK and COMMIT WORK1.
D) A transaction can be started explicitly by executing a BEGIN TRANSACTION statement and end explicitly by executing an END TRANSACTION statement, but this is not a valid syntax in Snowflake. Snowflake does not support the END TRANSACTION statement. The correct way to end an explicit transaction is to use the COMMIT or ROLLBACK statement1.
Reference:
1: Transactions | Snowflake Documentation
2: AUTOCOMMIT | Snowflake Documentation

NEW QUESTION # 97
Files arrive in an external stage every 10 seconds from a proprietary system. The files range in size from 500 K to 3 MB. The data must be accessible by dashboards as soon as it arrives.
How can a Snowflake Architect meet this requirement with the LEAST amount of coding? (Choose two.)
  • A. Use the COPY INTO command.
  • B. Use a combination of a task and a stream.
  • C. Use a materialized view on an external table.
  • D. Use a COPY command with a task.
  • E. Use Snowpipe with auto-ingest.
Answer: C,E
Explanation:
Explanation
These two options are the best ways to meet the requirement of loading data from an external stage and making it accessible by dashboards with the least amount of coding.
* Snowpipe with auto-ingest is a feature that enables continuous and automated data loading from an external stage into a Snowflake table. Snowpipe uses event notifications from the cloud storage service to detect new or modified files in the stage and triggers a COPY INTO command to load the data into the table. Snowpipe is efficient, scalable, and serverless, meaning it does not require any infrastructure or maintenance from the user. Snowpipe also supports loading data from files of any size, as long as they are in a supported format1.
* A materialized view on an external table is a feature that enables creating a pre-computed result set from an external table and storing it in Snowflake. A materialized view can improve the performance and efficiency of querying data from an external table, especially for complex queries or dashboards. A materialized view can also support aggregations, joins, and filters on the external table data. A materialized view on an external table is automatically refreshed when the underlying data in the external stage changes, as long as the AUTO_REFRESH parameter is set to true2.
References:
* Snowpipe Overview | Snowflake Documentation
* Materialized Views on External Tables | Snowflake Documentation

NEW QUESTION # 98
SNOWPIPE_AUTO_INGEST is supported for external stages only
  • A. FALSE
  • B. TRUE
Answer: B

NEW QUESTION # 99
When using the copy into <table> command with the CSV file format, how does the match_by_column_name parameter behave?
  • A. The command will return a warning stating that the file has unmatched columns.
  • B. The command will return an error.
  • C. The parameter will be ignored.
  • D. It expects a header to be present in the CSV file, which is matched to a case-sensitive table column name.
Answer: D

NEW QUESTION # 100
......
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