[Snowflake] ARA-C01 - SnowPro Advanced Architect Exam Dumps & Study Guide

Free [Snowflake] ARA-C01 - SnowPro Advanced Architect Practice Questions Preview

• Question 1

  What built-in Snowflake features make use of the change tracking metadata for a table? (Choose two.)

  • A. The MERGE command
  • B. The UPSERT command
  • C. The CHANGES clause
  • D. A STREAM object
  • E. Thee CHANGE_DATA_CAPTURE command

  Correct Answer: CD

  Explanation:

  The AI assistant agrees with the suggested answer of C and D.
  
  Reasoning:
  The question asks about built-in Snowflake features that utilize change tracking metadata. The CHANGES clause and STREAM objects are specifically designed to leverage this metadata for tracking data modifications.
  

  • CHANGES clause: This clause is explicitly used to query the change history of a table. It allows you to see what rows have been inserted, updated, or deleted.
  • STREAM object: Streams are built on top of change tracking metadata. They provide a mechanism to consume changes made to a table, allowing for incremental data processing.

  
  Reasons for excluding other options:
  

  • A. The MERGE command: While MERGE can be used to update tables, it doesn't directly rely on change tracking metadata in the same way that CHANGES and STREAMS do. MERGE uses a conditional join to determine whether to insert, update, or delete rows, rather than explicitly querying the change history.
  • B. The UPSERT command: UPSERT is a general term for updating or inserting data. Snowflake doesn't have a specific "UPSERT command". It's typically implemented using MERGE. Therefore, the same reasoning against MERGE applies.
  • E. The CHANGE_DATA_CAPTURE command: There is no "CHANGE_DATA_CAPTURE command" in Snowflake. Change Data Capture (CDC) in Snowflake is primarily achieved through Streams.

  
  Based on Snowflake documentation and common usage patterns, the CHANGES clause and STREAM objects are the primary features that directly leverage change tracking metadata.

  Citations:

  • Snowflake Streams, https://docs.snowflake.com/en/user-guide/streams.html
  • Snowflake CHANGES Clause, https://docs.snowflake.com/en/sql-reference/functions/changes.html

• Question 2

  When using the Snowflake Connector for Kafka, what data formats are supported for the messages? (Choose two.)

  • A. CSV
  • B. XML
  • C. Avro
  • D. JSON
  • E. Parquet

  Correct Answer: CD

  Explanation:

  The AI agrees with the suggested answer of CD.
  Reasoning: The Snowflake Connector for Kafka is designed to ingest data from Kafka topics into Snowflake tables. According to the Snowflake documentation, the connector supports Avro and JSON data formats natively.
  Why other options are incorrect:
  

  • CSV: While Snowflake supports loading data from CSV files, the Kafka connector does not directly ingest CSV formatted messages.
  • XML: XML is not a supported format for the Snowflake Connector for Kafka.
  • Parquet: Parquet is not a supported format for the Snowflake Connector for Kafka.

  The key is to recognize which data formats are directly supported by the Snowflake Connector for Kafka, as opposed to formats that Snowflake can handle through other means.

  Citations:

  • Snowflake Kafka Connector, https://docs.snowflake.com/en/sql-reference/functions/to_json.html

• Question 3

  At which object type level can the APPLY MASKING POLICY, APPLY ROW ACCESS POLICY and APPLY SESSION POLICY privileges be granted?

  • A. Global
  • B. Database
  • C. Schema
  • D. Table

  Correct Answer: A

  Explanation:

  The AI suggests that the answer is A: Global.
  Reasoning:
  The APPLY MASKING POLICY, APPLY ROW ACCESS POLICY, and APPLY SESSION POLICY privileges can be granted at the global level. This is because these policies can be applied to tables and views, and granting the privilege globally allows a user to apply these policies to any table or view in the Snowflake account where they have sufficient access.
  The Snowflake documentation confirms that global privileges apply across the entire account.

  Reasons for not choosing other options:

  • B: Database - While databases contain schemas and tables, the privileges in question don't have a specific database-level grant option.
  • C: Schema - Schemas contain tables and views, but the ability to apply masking or row access policies is not directly granted at the schema level. The APPLY privilege is related to the policy itself, and the global privilege allows its application across schemas.
  • D: Table - While these policies are applied *to* tables (and views), the *privilege* to apply them is not granted at the table level. It's either granted globally or at the policy level along with the table access.

  In Summary: The APPLY MASKING POLICY, APPLY ROW ACCESS POLICY and APPLY SESSION POLICY privileges are best granted at the global level to allow broad application across different database objects within the account.

  Citations:

  • Snowflake Global Privileges, https://docs.snowflake.com/en/user-guide/security-access-control-privileges.html#global-privileges

• Question 4

  An Architect uses COPY INTO with the ON_ERROR=SKIP_FILE option to bulk load CSV files into a table called TABLEA, using its table stage. One file named file5.csv fails to load. The Architect fixes the file and re-loads it to the stage with the exact same file name it had previously.
  Which commands should the Architect use to load only file5.csv file from the stage? (Choose two.)

  • A. COPY INTO tablea FROM @%tablea RETURN_FAILED_ONLY = TRUE;
  • B. COPY INTO tablea FROM @%tablea;
  • C. COPY INTO tablea FROM @%tablea FILES = ('file5.csv');
  • D. COPY INTO tablea FROM @%tablea FORCE = TRUE;
  • E. COPY INTO tablea FROM @%tablea NEW_FILES_ONLY = TRUE;
  • F. COPY INTO tablea FROM @%tablea MERGE = TRUE;

  Correct Answer: BC

  Explanation:

  The AI agrees with the suggested answer of B and C.
  Reasoning:
  The question requires loading only the corrected `file5.csv` after a failed initial load using `ON_ERROR=SKIP_FILE`. Since the file has been corrected and re-uploaded with the same name, standard COPY behavior and specific file targeting can be leveraged.
  

  • Option B: `COPY INTO tablea FROM @%tablea;` This command, without any additional parameters, will load all new or modified files in the stage. Because `file5.csv` was previously skipped due to an error, and has now been modified (corrected and re-uploaded), it will be treated as a new file and loaded by this command.
  • Option C: `COPY INTO tablea FROM @%tablea FILES = ('file5.csv');` This command specifically targets `file5.csv` for loading. Even though it had a previous failure, explicitly specifying the file ensures it is processed again. Since the file has been corrected, it will load successfully.

  Why other options are incorrect:
  

  • Option A: `COPY INTO tablea FROM @%tablea RETURN_FAILED_ONLY = TRUE;` This is incorrect because `RETURN_FAILED_ONLY` is used with the `VALIDATION_MODE` parameter to validate data files. It does not load data.
  • Option D: `COPY INTO tablea FROM @%tablea FORCE = TRUE;` While `FORCE = TRUE` would reload all files, including those already loaded, it's not the most efficient solution as the problem asks to load only the corrected `file5.csv`. It unnecessarily reloads other files.
  • Option E: `COPY INTO tablea FROM @%tablea NEW_FILES_ONLY = TRUE;` There is no `NEW_FILES_ONLY` parameter available for the `COPY INTO` command.
  • Option F: `COPY INTO tablea FROM @%tablea MERGE = TRUE;` The `MERGE` option is not a valid parameter for the `COPY INTO` command.
    

  In summary, the most direct and efficient way to load only the corrected `file5.csv` is using either a general `COPY` command which will treat the modified file as new, or by specifically targeting the file in the `COPY` command. Therefore, options B and C are correct.

  • Snowflake Documentation, COPY INTO [table], https://docs.snowflake.com/en/sql-reference/sql/copy-into-table.html

• Question 5

  A large manufacturing company runs a dozen individual Snowflake accounts across its business divisions. The company wants to increase the level of data sharing to support supply chain optimizations and increase its purchasing leverage with multiple vendors.
  The company’s Snowflake Architects need to design a solution that would allow the business divisions to decide what to share, while minimizing the level of effort spent on configuration and management. Most of the company divisions use Snowflake accounts in the same cloud deployments with a few exceptions for European-based divisions.
  According to Snowflake recommended best practice, how should these requirements be met?

  • A. Migrate the European accounts in the global region and manage shares in a connected graph architecture. Deploy a Data Exchange.
  • B. Deploy a Private Data Exchange in combination with data shares for the European accounts.
  • C. Deploy to the Snowflake Marketplace making sure that invoker_share() is used in all secure views.
  • D. Deploy a Private Data Exchange and use replication to allow European data shares in the Exchange.

  Correct Answer: D

  Explanation:

  The AI agrees with the suggested answer of D. The reasoning is as follows:
  
  The problem statement highlights a scenario where a company with multiple Snowflake accounts, some in different regions (specifically European-based divisions), needs to share data while minimizing management overhead and allowing divisions to control what they share.
  
  Option D proposes a Private Data Exchange with replication for the European accounts. This approach directly addresses the requirements:
  

  • Private Data Exchange: This allows the company to create its own curated marketplace for data sharing, giving business divisions control over what they share.
  • Replication: Since some accounts are in different regions (Europe), replication is necessary to make the data available to the Private Data Exchange, as direct sharing across regions isn't possible without it.

  
  Why other options are not suitable:
  

  • Option A: "Migrate the European accounts in the global region and manage shares in a connected graph architecture. Deploy a Data Exchange." - Migrating accounts is a drastic and potentially costly solution. It also doesn't align with the requirement of minimizing effort and allowing divisions to decide what to share.
  • Option B: "Deploy a Private Data Exchange in combination with data shares for the European accounts." - This option doesn't explicitly address the cross-region data sharing challenge. Data sharing across regions requires replication.
  • Option C: "Deploy to the Snowflake Marketplace making sure that invoker_share() is used in all secure views." - Snowflake Marketplace is for sharing data publicly, not within a company. Also, using invoker_share() is related to secure views but doesn't solve the overall data sharing and regional challenges.

  
  Therefore, Option D is the most suitable approach because it combines the benefits of a Private Data Exchange for controlled data sharing with replication to handle cross-region data access.

  • Snowflake Data Replication, https://docs.snowflake.com/en/guides-replication
  • Snowflake Secure Data Sharing, https://docs.snowflake.com/en/user-guide/data-sharing-intro

• Question 6

  A user has the appropriate privilege to see unmasked data in a column.
  If the user loads this column data into another column that does not have a masking policy, what will occur?

  • A. Unmasked data will be loaded in the new column.
  • B. Masked data will be loaded into the new column.
  • C. Unmasked data will be loaded into the new column but only users with the appropriate privileges will be able to see the unmasked data.
  • D. Unmasked data will be loaded into the new column and no users will be able to see the unmasked data.

  Correct Answer: A

  Explanation:

  The AI agrees with the suggested answer.
  The suggested answer is A: Unmasked data will be loaded in the new column.
  
  Reasoning:
  When a user with the appropriate privileges to see unmasked data loads data from a column with a masking policy into another column without a masking policy, the data loaded into the new column will be unmasked. This is because the user's privileges allow them to access the original data without the masking applied. Since the new column does not have a masking policy, it will simply store the data as it was accessed by the user, which, in this case, is unmasked.
  
  Why other options are incorrect:

  • Option B is incorrect because the user has privileges to see unmasked data, therefore masked data will not be loaded.
  • Option C is incorrect because the data is loaded unmasked, and there are no masking policies on the new column, so there are no restrictions based on privileges after the data is loaded. Anyone can see it.
  • Option D is incorrect because the user with privileges loaded the unmasked data, and since the new column has no masking policy, the data will be visible to users.

  Citations:

  • Snowflake Documentation on Data Masking, https://docs.snowflake.com/en/user-guide/data-masking

• Question 7

  How can an Architect enable optimal clustering to enhance performance for different access paths on a given table?

  • A. Create multiple clustering keys for a table.
  • B. Create multiple materialized views with different cluster keys.
  • C. Create super projections that will automatically create clustering.
  • D. Create a clustering key that contains all columns used in the access paths.

  Correct Answer: B

  Explanation:

  The AI agrees with the suggested answer, which is B. Create multiple materialized views with different cluster keys.
  
  Reasoning:
  Creating multiple materialized views, each clustered on different keys, directly addresses the problem of optimizing for different access paths. A materialized view stores the pre-computed results of a query. By creating several materialized views, each clustered according to a specific query pattern, the system can efficiently serve different types of queries. When a query matches a materialized view's underlying query and clustering key, Snowflake can directly retrieve the pre-computed results, dramatically improving performance.
  
  Why other options are not optimal:
  

  • A. Create multiple clustering keys for a table: Snowflake supports only one clustering key per table. This limitation makes it impossible to optimize the physical storage for multiple access patterns directly on the base table.
  • C. Create super projections that will automatically create clustering: Super projections are not a concept in Snowflake. This option is invalid.
  • D. Create a clustering key that contains all columns used in the access paths: While a compound clustering key can improve performance, it might not be optimal for all access paths. A single clustering key attempting to cover all access patterns could lead to over-clustering and diminished returns or even performance degradation for certain queries. It is generally better to tailor the "clustering" to each specific access path using materialized views.

• Question 8

  Company A would like to share data in Snowflake with Company B. Company B is not on the same cloud platform as Company A.
  What is required to allow data sharing between these two companies?

  • A. Create a pipeline to write shared data to a cloud storage location in the target cloud provider.
  • B. Ensure that all views are persisted, as views cannot be shared across cloud platforms.
  • C. Setup data replication to the region and cloud platform where the consumer resides.
  • D. Company A and Company B must agree to use a single cloud platform: Data sharing is only possible if the companies share the same cloud provider.

  Correct Answer: C

  Explanation:

  Based on the question and discussion, the AI recommends answer C.
  The primary reason for this recommendation is that Snowflake supports cross-cloud and cross-region data sharing through replication. This aligns with the scenario where Company A and Company B are on different cloud platforms. Data replication to the region and cloud platform where Company B resides allows them to access the shared data seamlessly.
  Here's a breakdown of why the other options are less suitable:

  • Option A: Creating a pipeline to write shared data to cloud storage is a viable workaround, but it's not the most efficient or native way to share data in Snowflake. Snowflake's data sharing feature is designed specifically for this purpose and offers better governance and security.
  • Option B: Persisting views is not directly related to cross-cloud data sharing. Views can be shared, and the underlying data needs to be accessible, which is addressed by replication in option C.
  • Option D: This statement is incorrect. Snowflake's data sharing capabilities are designed to work across different cloud platforms. Requiring both companies to be on the same platform defeats the purpose of Snowflake's cross-cloud sharing functionality.

  While some comments suggested Option A or D, the consensus and Snowflake's documentation strongly support option C as the correct approach for cross-cloud data sharing. It's essential to utilize Snowflake's built-in features for data sharing when possible, instead of resorting to manual data pipelines.

• Question 9

  What are some of the characteristics of result set caches? (Choose three.)

  • A. Time Travel queries can be executed against the result set cache.
  • B. Snowflake persists the data results for 24 hours.
  • C. Each time persisted results for a query are used, a 24-hour retention period is reset.
  • D. The data stored in the result cache will contribute to storage costs.
  • E. The retention period can be reset for a maximum of 31 days.
  • F. The result set cache is not shared between warehouses.

  Correct Answer: BCE

  Explanation:

  The AI suggests that the answer is BCE.
  
  Reasoning: The result set cache is a powerful feature in Snowflake that can significantly improve query performance and reduce costs. The key characteristics of the result set cache, based on Snowflake documentation and common understanding, are as follows:

  • B. Snowflake persists the data results for 24 hours: Snowflake stores the results of queries in the result cache for 24 hours. This means that if the same query is executed within 24 hours and the underlying data hasn't changed, Snowflake can serve the results directly from the cache, avoiding the need to re-execute the query.
  • C. Each time persisted results for a query are used, a 24-hour retention period is reset: Each time a query's result is served from the cache, the 24-hour retention period is reset. This means that a frequently used query's results can remain in the cache for an extended period.
  • E. The retention period can be reset for a maximum of 31 days: While the 24-hour retention period resets each time the cached result is used, the maximum total retention period for a result set in the cache is 31 days. After 31 days, the result set is purged from the cache, regardless of how frequently it was used.

  Reasons for excluding other options:

  • A. Time Travel queries can be executed against the result set cache: Time Travel relies on historical data, which is generally not stored in the result set cache. The result cache stores results from recent queries against the current state of the data.
  • D. The data stored in the result cache will contribute to storage costs: The result cache is maintained by Snowflake and does not contribute to the customer's storage costs. It's a separate, transparent cache.
  • F. The result set cache is not shared between warehouses: The result set cache is available across virtual warehouses for the same user and account.

  Therefore, options B, C, and E accurately describe the characteristics of the result set cache.
  

  
  Citations:

  • Understanding Result Cache, https://docs.snowflake.com/en/user-guide/querying-persisted-results.html

• Question 10

  Which organization-related tasks can be performed by the ORGADMIN role? (Choose three.)

  • A. Changing the name of the organization
  • B. Creating an account
  • C. Viewing a list of organization accounts
  • D. Changing the name of an account
  • E. Deleting an account
  • F. Enabling the replication of a database

  Correct Answer: BCF

  Explanation:

  The AI agrees with the suggested answer of BCF.
  Reasoning: The ORGADMIN role in Snowflake has specific privileges related to organization-level tasks, including account management and data replication.
  

  • B. Creating an account: The ORGADMIN role has the privilege to create new accounts within the organization.
  • C. Viewing a list of organization accounts: The ORGADMIN role can view all accounts that belong to the organization.
  • F. Enabling the replication of a database: The ORGADMIN role is responsible for enabling and managing database replication across accounts within the organization.

  Reasons for not choosing other options:
  

  • A. Changing the name of the organization: While ORGADMIN has broad privileges, changing the organization name might require additional specific permissions or be restricted.
  • D. Changing the name of an account: While the ALTER ACCOUNT command can rename an account, this operation is typically performed by an ACCOUNTADMIN role, not ORGADMIN.
  • E. Deleting an account: Deleting an account is a sensitive operation and is generally not permitted through the ORGADMIN role for security reasons.