Snowflake SnowPro Advanced Architect Practice Test Questions, Exam Dumps

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SnowPro Advanced Architect Snowflake Practice Test Questions and Exam Dumps

Question No 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. The CHANGE_DATA_CAPTURE command

Correct Answer:A, D

Explanation:

Snowflake offers several built-in features that utilize the change tracking metadata for tracking changes made to tables. These features help capture, track, and apply changes efficiently without the need to directly query the table for updates.

Let's break down each option:

Option A: The MERGE command
The MERGE command in Snowflake is used to perform conditional INSERT, UPDATE, or DELETE operations based on whether matching rows are found in the target table. The MERGE command can take advantage of change tracking metadata to efficiently compare the source and target data, allowing it to apply changes (e.g., updates or inserts) only when necessary. The metadata tracked by Snowflake helps to minimize unnecessary operations, improving performance. This makes A a valid answer.
Option B: The UPSERT command
While UPSERT is a term commonly used to describe the MERGE command or a combination of INSERT and UPDATE, Snowflake does not have a dedicated UPSERT command distinct from the MERGE command. Since the UPSERT term is often synonymous with MERGE and is not a separate, distinct feature, B is not correct.
Option C: The CHANGES clause
The CHANGES clause is not a feature in Snowflake. In Snowflake, the equivalent functionality to track changes involves features like streams and the MERGE command. There is no specific "CHANGES" clause in the standard SQL syntax used for change tracking, so C is not correct.
Option D: A STREAM object
A STREAM object in Snowflake is designed to track changes (inserts, updates, and deletes) made to a table. It uses change tracking metadata to keep track of all changes that occur after the stream is created. You can query the STREAM object to view changes that have occurred, without having to manually check the source table for modifications. This is a core feature for working with change tracking in Snowflake, so D is a valid answer.
Option E: The CHANGE_DATA_CAPTURE command
Snowflake does not have a CHANGE_DATA_CAPTURE command. Instead, change data capture functionality is handled via Streams and Tasks, which work in conjunction with the change tracking metadata. Therefore, E is not a correct answer.

In conclusion, the correct answers are A (MERGE command) and D (STREAM object), as both make use of change tracking metadata in Snowflake for efficient handling of table changes.

Question No 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 Answers: C, D

Explanation:

The Snowflake Connector for Kafka allows you to stream data from Kafka topics directly into Snowflake. This connector is designed to handle the ingestion of high volumes of data, and it supports a variety of message formats for streaming.

Let's go through the provided options and understand which formats are supported by the Snowflake Connector for Kafka:

Option A: CSV
CSV is a common data format, but it is not one of the primary formats supported by the Snowflake Connector for Kafka. Snowflake can ingest CSV data through other means (e.g., using Snowpipe or manual data loading), but the Kafka connector does not natively support CSV as a format for messages. CSV files are more commonly used for batch data loads rather than continuous data streams from Kafka.

Option B: XML
XML is another widely used data format, but it is not one of the supported formats for the Snowflake Connector for Kafka. The connector is not designed to work directly with XML messages from Kafka topics. Snowflake itself can process XML data, but not in the context of real-time streaming through the Kafka connector.

Option C: Avro
Avro is one of the supported formats for messages when using the Snowflake Connector for Kafka. It is a compact, binary data format often used in Kafka because of its efficiency and schema evolution capabilities. Avro provides an ideal format for streaming data, especially when used with schema registries, and it is supported by the connector for seamless integration with Snowflake.

Option D: JSON
JSON is another supported data format for messages in the Snowflake Connector for Kafka. JSON is a flexible, human-readable format often used for streaming and real-time data exchanges. It is commonly used in Kafka environments because of its ease of use and support for semi-structured data. Snowflake can handle JSON data efficiently, making it a popular choice for Kafka-to-Snowflake streaming.

Option E: Parquet
While Parquet is a columnar storage format commonly used in big data ecosystems, it is not one of the direct formats supported by the Snowflake Connector for Kafka. Parquet files are typically used in batch data processing and storage systems, and Snowflake supports them for file-based loads but not for Kafka streaming through the connector.

The two formats supported by the Snowflake Connector for Kafka for streaming messages are Avro (C) and JSON (D). These formats are well-suited for real-time data streaming and offer the necessary features for efficient and scalable integration between Kafka and Snowflake.

Question No 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: D

Explanation:

The APPLY MASKING POLICY, APPLY ROW ACCESS POLICY, and APPLY SESSION POLICY privileges are associated with specific data security and access control features in database management systems, particularly in systems like Oracle or Snowflake.

These privileges are granted to control who can apply certain security policies to data at the level of individual database objects. Let's break down the options to understand which object type level these privileges apply to:

Masking Policies control how sensitive data is hidden or obfuscated in certain views of the data. This is a crucial part of ensuring that sensitive information is only exposed to authorized users.
Row Access Policies define what rows of data a user is allowed to access. This is useful in multi-tenant databases or scenarios where access control is fine-grained.
Session Policies manage session-level controls and security for database users, controlling how certain session parameters or behaviors are enforced.

These privileges can only be applied at the Table level, because the policies themselves are typically designed to apply directly to individual tables and their associated rows or columns. When dealing with sensitive data, masking, row-level access, and session policies are usually implemented at the table level to ensure fine-grained control of data access.

Option A (Global) is not correct because these privileges are typically not applied at the global level, but at the object level within the database.
Option B (Database) is incorrect as these privileges are more granular than the entire database and are implemented at the object level, such as on tables.
Option C (Schema) is not correct either, because schemas represent collections of database objects (e.g., tables, views), and the policies in question are generally applied at the individual table level, not to the entire schema.

Therefore, the correct answer is D: Table, as the APPLY MASKING POLICY, APPLY ROW ACCESS POLICY, and APPLY SESSION POLICY privileges are typically granted at the table level.

Question No 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: C and A

Explanation:

In this scenario, the Architect needs to reload a single file (file5.csv) from the stage to the target table (TABLEA) after the file was fixed and previously skipped due to an error. Let's explore the options provided:

A. COPY INTO tablea FROM @%tablea RETURN_FAILED_ONLY = TRUE;
This option would reload only the files that failed during a previous load attempt. Since file5.csv was the one that failed earlier, using this option ensures that only file5.csv will be reloaded into the table. This command is effective in scenarios where some files were processed correctly, but others failed. The RETURN_FAILED_ONLY option focuses on reloading files that were previously skipped due to errors, which fits the requirements of the scenario.

C. COPY INTO tablea FROM @%tablea FILES = ('file5.csv');
This option specifies the exact file(s) to be loaded, which in this case is file5.csv. The FILES parameter allows you to target specific files, so even though the file was previously skipped due to an error, it can be reloaded individually without affecting the other files in the stage. This is an ideal choice when you want to load only a specific file after it has been fixed.

Now, let’s examine the other options:

B. COPY INTO tablea FROM @%tablea;
This command would reload all files from the stage into the table. It does not allow targeting specific files, and it would reload all files, including those that were already successfully loaded in the past. This approach would not be efficient when only one file needs to be reloaded.

D. COPY INTO tablea FROM @%tablea FORCE = TRUE;
The FORCE option forces the load to occur regardless of any previous errors, but it does not specifically target only the failed files. It would still attempt to load all files in the stage, even if some of them were already successfully loaded. This is not the most efficient choice if only one file (file5.csv) needs to be reloaded.

E. COPY INTO tablea FROM @%tablea NEW_FILES_ONLY = TRUE;
The NEW_FILES_ONLY option loads only new files that have been added to the stage since the last load. Since file5.csv was already in the stage and has been reloaded after being fixed, it would be considered a "new" file, but this option does not directly address the situation where a file was skipped previously due to an error. It is not the best choice for reloading only a single failed file.

F. COPY INTO tablea FROM @%tablea MERGE = TRUE;
The MERGE option is used for performing a merge operation during the load (typically for upsert scenarios where existing records are updated or new ones are inserted). It does not directly help with targeting specific files for reloading after a failure. Additionally, it would reload all files, not just the one that failed.

Therefore, the most appropriate commands for reloading only file5.csv after it was fixed are A and C.

Question No 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:

To meet the company's requirements of increasing data sharing and supporting supply chain optimizations, Snowflake recommends a solution that provides a centralized approach to data sharing while also addressing the specific needs of divisions, including those located in Europe. Here's an analysis of each option:

Option D: Deploy a Private Data Exchange and use replication to allow European data shares in the Exchange.
This is the recommended solution for several reasons:

Private Data Exchange: A Private Data Exchange is a Snowflake feature that facilitates the sharing of data between Snowflake accounts in a secure and manageable way. It allows organizations to share data with controlled access, minimizing the management effort typically involved with configuration across multiple accounts.

Replication: Using replication ensures that data can be shared across Snowflake accounts in multiple regions, including the European-based accounts that might be located in a different cloud region. Replication synchronizes data between regions, enabling seamless sharing of data across different geographical locations.

This approach reduces the complexity of managing individual data shares across different business divisions, as data can be replicated into the Exchange and shared based on need. The European divisions can continue to share data securely and efficiently, while leveraging Snowflake’s infrastructure to maintain data locality and compliance with European regulations.

Option A: Migrate the European accounts in the global region and manage shares in a connected graph architecture. Deploy a Data Exchange.
While the idea of managing data in a Data Exchange is appropriate, migrating the European accounts to the global region could introduce unnecessary complexity, especially if there are data residency or compliance requirements that prevent this migration. Moreover, the connected graph architecture doesn’t align as closely with Snowflake’s recommended approaches for cross-region sharing and data management in this context. The migration of the European accounts might be costly and operationally challenging.

Option B: Deploy a Private Data Exchange in combination with data shares for the European accounts.
This option does provide a good solution using a Private Data Exchange, but it fails to address the specific challenge of handling European-based accounts. The approach of combining data shares with the Exchange might be feasible, but without replication, there could be additional management overhead for each European account. Without replication, it might be challenging to keep data synchronized across regions, especially if the European divisions are in a separate region. Replication ensures seamless sharing across regions, minimizing the administrative load.

Option C: Deploy to the Snowflake Marketplace making sure that invoker_share() is used in all secure views.
The Snowflake Marketplace allows for the sharing of datasets within the Snowflake ecosystem but is not designed for intra-company data sharing in the manner required by the manufacturing company. While invoker_share() is useful for ensuring secure views are accessible by authorized users, the Marketplace is more appropriate for external sharing rather than sharing within internal business divisions. This option is not aligned with the company's internal data-sharing needs.

In conclusion, Option D offers the most comprehensive solution by leveraging Snowflake's Private Data Exchange in combination with replication, which ensures smooth data sharing and synchronization across both local and European divisions. It optimizes for reduced management effort while providing the necessary scalability and data locality.

Question No 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.

Answer: A

Explanation:

In systems where data masking policies are applied, the purpose is to prevent unauthorized access to sensitive data by presenting a masked version of the data to users who do not have the necessary privileges to view the unmasked data. However, when it comes to loading data from a column with a masking policy to another column without such a policy, the behavior depends on the user’s privileges and the nature of the masking policy.

Let's break down each option:

A. Unmasked data will be loaded in the new column.
This is correct. If the user has the appropriate privilege to view unmasked data in the original column (i.e., they have sufficient access rights to bypass the masking policy), then the data that is loaded into another column without a masking policy will retain its unmasked form. The masking policy does not apply to the new column because it was not defined for that column. Therefore, the data will be loaded in its unmasked form into the new column.
B. Masked data will be loaded into the new column.
This is incorrect. Since the user has the appropriate privileges to view unmasked data, the masking policy does not affect their ability to load the unmasked version of the data into the new column. Masked data would only be loaded if the user did not have the required privileges to access the unmasked data in the first place.
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.
This is incorrect. Once the data is loaded into the new column (which has no masking policy), there are no restrictions applied based on user privileges. Therefore, all users can see the data in the new column as it is; it will not be subject to the same masking rules unless a masking policy is explicitly applied to the new column.
D. Unmasked data will be loaded into the new column and no users will be able to see the unmasked data.
This is incorrect. Once unmasked data is loaded into the new column, it is visible to all users unless a masking policy is applied to that column. Since no masking policy exists for the new column, all users will be able to see the data in its unmasked form.

Conclusion: The key point is that if the user has the appropriate privilege to view unmasked data and loads it into a new column that does not have a masking policy, the data will be loaded unmasked, and there will be no restrictions on visibility. Therefore, the correct answer is A.

Question No 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: D

Explanation:

Clustering in a database refers to the physical arrangement of data within storage to optimize query performance. The concept of clustering is particularly important in columnar databases or databases that support automatic data distribution and clustering strategies. For optimal performance, it’s crucial to align how the data is stored with the queries that will be frequently executed. When dealing with a given table, clustering is mainly used to enhance the efficiency of access paths, especially when certain columns are accessed frequently in query predicates.

A. Create multiple clustering keys for a table: While this might seem like a way to optimize performance by addressing different access paths, most databases allow only one clustering key per table (or at least recommend it for simplicity). Using multiple clustering keys can complicate performance optimization and isn't typically the solution to enhancing access paths.

B. Create multiple materialized views with different cluster keys: Materialized views are used for precomputing query results and storing them for faster access. However, creating materialized views with different clustering keys does not directly optimize the access paths for the main table. Materialized views are more about improving query performance through caching precomputed results rather than optimizing data clustering.

C. Create super projections that will automatically create clustering: Super projections are related to specific implementations in some databases like columnar or distributed systems, and they help by optimizing query performance for certain workloads. However, super projections do not necessarily optimize clustering for all access paths in the same way a clustering key does. This option may not fully address the problem of enhancing performance through optimal clustering for all access patterns.

D. Create a clustering key that contains all columns used in the access paths: This is the most effective approach. By defining a clustering key that includes all columns frequently used in the access paths (i.e., columns that appear in query filters, join conditions, or sorting clauses), the data will be physically organized in a way that aligns with those queries. This reduces the amount of I/O required and optimizes the query performance, making access paths faster and more efficient.

In conclusion, the best way to optimize clustering for performance on a given table is to create a clustering key that contains all columns used in the access paths. This ensures the table’s physical organization matches the query patterns, reducing disk I/O and improving overall efficiency. Therefore, the correct answer is D.

Question No 8:

What is required to allow data sharing between Company A and Company B if they are on different cloud platforms in Snowflake?

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. Set up 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.

Answer: C

Explanation:

In Snowflake, data sharing allows one account (the provider) to share data with another account (the consumer) across different cloud platforms, and cross-cloud data sharing is supported between cloud providers like AWS, Azure, and Google Cloud. This is a key feature of Snowflake, allowing seamless and secure sharing of data between different organizations, even when they are not on the same cloud platform.

However, to enable data sharing between two companies on different cloud platforms, there are some important requirements. In this case, setting up data replication between the regions and cloud platforms where the companies are located is necessary. Snowflake supports replicating data between cloud providers and regions, which allows the shared data to be accessible regardless of the cloud platform or region the companies are using.

Here's why the other options are not correct:

A. Create a pipeline to write shared data to a cloud storage location in the target cloud provider.
This is not necessary for cross-cloud data sharing in Snowflake. Snowflake provides built-in capabilities for data sharing that do not require writing data to external storage locations. Data can be shared directly between Snowflake accounts without needing intermediate storage locations.

B. Ensure that all views are persisted, as views cannot be shared across cloud platforms.
This is incorrect. In Snowflake, views can be shared across different cloud platforms. There is no need to persist views for data sharing to work; as long as the provider shares the underlying data objects (like tables or views), they can be accessed by the consumer. Snowflake handles the sharing process without requiring any special changes to views.

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.
This is false. One of the benefits of Snowflake’s cross-cloud data sharing is that it allows companies to share data even if they are on different cloud platforms. Snowflake supports sharing between AWS, Azure, and Google Cloud platforms, meaning the companies do not need to agree on using the same cloud provider.

Thus, the correct answer is C, as it directly addresses the requirement for data replication between different cloud platforms and regions to facilitate sharing.

Question No 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.

Answer: B, C, F

Explanation:

Result set caching in Snowflake provides a way to store the results of queries temporarily to improve performance. When a query is executed, its result set is cached, allowing subsequent identical queries to be retrieved faster without re-executing the query. Let’s analyze the characteristics:

Option A: Time Travel queries can be executed against the result set cache – Time Travel in Snowflake allows you to query historical data from a previous point in time. However, result set caching is designed for speeding up repeated queries by storing query results, not querying historical data. Therefore, Time Travel queries cannot be executed against the result set cache because the result set cache only contains the actual result of the query, not historical versions of the data.
Option B: Snowflake persists the data results for 24 hours – This is correct. By default, Snowflake caches the results of queries for a period of 24 hours. If the same query is executed within that 24-hour window, Snowflake will use the cached result set instead of re-running the query, which improves performance and reduces resource consumption.
Option C: Each time persisted results for a query are used, a 24-hour retention period is reset – This is also correct. The retention period of the result set cache is reset each time the cached result is used. So, every time the result is accessed, the 24-hour timer is restarted, ensuring that the cached result remains valid for another 24 hours from the time it is last accessed.
Option D: The data stored in the result cache will contribute to storage costs – This is incorrect. While result set caches do store query results, they do not contribute to storage costs in the same way that persistent data like tables and databases do. Snowflake does not charge for storage of result set caches, as these are temporary and typically stored in memory.
Option E: The retention period can be reset for a maximum of 31 days – This is incorrect. The retention period for result set caches is fixed at 24 hours and cannot be extended to 31 days. The cache is refreshed every 24 hours and can be reset when the cached result is used, but it is not configurable beyond the 24-hour period.
Option F: The result set cache is not shared between warehouses – This is correct. The result set cache is specific to the warehouse where the query was executed. Different virtual warehouses in Snowflake do not share result caches, meaning that even if two different warehouses execute the same query, they will not be able to use each other’s result set cache. This behavior ensures that each warehouse operates independently with its own cache.

Thus, the correct characteristics of result set caches are that they are persisted for 24 hours (Option B), the retention period is reset with each use of the cached result (Option C), and they are not shared between warehouses (Option F).

Question No 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

Answer: A, B, C

Explanation:

The ORGADMIN role, typically found in cloud platforms or service management systems, holds a significant level of administrative privileges within the organization. This role is designed to manage and maintain the organization's overall configuration and resources, often including user management and organizational settings. Let's go over the options to determine which tasks an ORGADMIN role can perform:

A. Changing the name of the organization:
The ORGADMIN role has the necessary permissions to manage the organization's settings and configurations. Changing the name of the organization is a fundamental administrative task, and an ORGADMIN would typically have this level of access, as it directly impacts the organization's identity in the system. This is a valid task for the ORGADMIN role.

B. Creating an account:
Account management is a critical function for an ORGADMIN role. Creating new accounts for users, systems, or services under the organization's domain is part of the responsibilities of an ORGADMIN. This allows them to add new members, configure new systems, or allocate resources accordingly. Therefore, creating an account is a task the ORGADMIN role can perform.

C. Viewing a list of organization accounts:
Viewing organizational accounts is a task that ORGADMIN can easily perform as part of their oversight role. This is a fundamental administrative function that helps the ORGADMIN track and monitor all the accounts associated with the organization. Accessing this list is within the scope of responsibilities for the ORGADMIN role.

D. Changing the name of an account:
Changing the name of an account is more granular than organizational-level management. While an ORGADMIN has broad organizational permissions, tasks like changing individual account names are typically restricted to specific account administrators or users with direct permissions over that account. Therefore, this is not a task that ORGADMIN would commonly handle.

E. Deleting an account:
Although an ORGADMIN generally has high-level access, deleting an account may not always fall under the ORGADMIN role's scope. Deletion of accounts, especially those associated with critical data or systems, is a high-risk operation that may require additional authorization or a different administrative role focused on account lifecycle management. Therefore, this task is not universally within the ORGADMIN role's responsibilities.

F. Enabling the replication of a database:
Database replication is usually a task handled by database administrators (DBAs) or those with direct access to the database management systems, not by the ORGADMIN role. The ORGADMIN role typically focuses on organizational configuration and not specific technical database operations. Hence, enabling replication is not part of the ORGADMIN role.

Thus, the ORGADMIN role is most likely responsible for A, B, and C, as these are fundamental organizational management tasks.