EXAM DUMPS ARA-C01 PDF, UPDATED ARA-C01 DUMPS

Exam Dumps ARA-C01 Pdf, Updated ARA-C01 Dumps

Exam Dumps ARA-C01 Pdf, Updated ARA-C01 Dumps

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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is a highly sought-after certification in the data world. SnowPro Advanced Architect Certification certification is designed for professionals who have a deep understanding of Snowflake's cloud data platform and have expertise in designing and implementing complex data solutions. ARA-C01 Exam Tests the candidate's knowledge of Snowflake's architecture, data warehousing concepts, security and access control, performance tuning, and other advanced features.

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Evaluate your own mistakes each time you attempt the desktop SnowPro Advanced Architect Certification (ARA-C01) practice exam. It expertly is designed ARA-C01 practice test software supervised by a team of professionals. There is 24/7 customer service to help you in any situation. You can customize your desired ARA-C01 Exam conditions like exam length and the number of questions.

Snowflake ARA-C01 exam is a rigorous and comprehensive test of a candidate's knowledge and skills. ARA-C01 exam consists of 90 multiple-choice and multiple-response questions that need to be answered in a time frame of 2 hours. ARA-C01 exam covers a wide range of topics, from Snowflake's architecture and data warehousing concepts to advanced features like Snowflake's data sharing and multi-cluster warehouses. ARA-C01 Exam also tests the candidate's ability to design and implement complex data solutions using Snowflake's platform.

Snowflake SnowPro Advanced Architect Certification Sample Questions (Q159-Q164):

NEW QUESTION # 159
Select the true statements about TASKS

  • A. TASKS can be used with STREAMS
  • B. TASKS can be used for change data capture
  • C. TASKS cannot be triggered manually
  • D. TASKS can be scheduled for SQL execution

Answer: A,C,D


NEW QUESTION # 160
The insertReport endpoint can be thought of like the UNIX command tail

  • A. TRUE
  • B. FALSE

Answer: A


NEW QUESTION # 161
Which of the following are characteristics of how row access policies can be applied to external tables?
(Choose three.)

  • A. External tables are supported as mapping tables in a row access policy.
  • B. A row access policy can be applied to the VALUE column of an existing external table.
  • C. While cloning a database, both the row access policy and the external table will be cloned.
  • D. A row access policy cannot be applied to a view created on top of an external table.
  • E. An external table can be created with a row access policy, and the policy can be applied to the VALUE column.
  • F. A row access policy cannot be directly added to a virtual column of an external table.

Answer: B,E,F

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 theVALUE 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.
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 # 162
Which Snowflake objects can be used in a data share? (Select TWO).

  • A. Standard view
  • B. Secure view
  • C. External table
  • D. Stream
  • E. Stored procedure

Answer: B,C

Explanation:
https://docs.snowflake.com/en/user-guide/data-sharing-intro


NEW QUESTION # 163
Which system functions does Snowflake provide to monitor clustering information within a table (Choose two.)

  • A. SYSTEM$CLUSTERING_KEYS
  • B. SYSTEM$CLUSTERING_USAGE
  • C. SYSTEM$CLUSTERING_INFORMATION
  • D. SYSTEM$CLUSTERING_PERCENT
  • E. SYSTEM$CLUSTERING_DEPTH

Answer: C,E

Explanation:
According to the Snowflake documentation, these two system functions are provided by Snowflake to monitor clustering information within a table. A system function is a type of function that allows executing actions or returning information about the system. A clustering key is a feature that allows organizing data across micro-partitions based on one or more columns in the table. Clustering can improve query performance by reducing the number of files to scan.
* SYSTEM$CLUSTERING_INFORMATION is a system function that returns clustering information, including average clustering depth, for a table based on one or more columns in the table. The function takes a table name and an optional column name or expression as arguments, and returns a JSON string
* with the clustering information. The clustering information includes the cluster by keys, the total partition count, the total constant partition count, the average overlaps, and the average depth1.
* SYSTEM$CLUSTERING_DEPTH is a system function that returns the clustering depth for a table based on one or more columns in the table. The function takes a table name and an optional column name or expression as arguments, and returns an integer value with the clustering depth. The clustering depth is the maximum number of overlapping micro-partitions for any micro-partition in the table. A lower clustering depth indicates a better clustering2.
References:
* SYSTEM$CLUSTERING_INFORMATION | Snowflake Documentation
* SYSTEM$CLUSTERING_DEPTH | Snowflake Documentation


NEW QUESTION # 164
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