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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Accounts and Security | 25% | - Access Control and Authentication
|
| Topic 2: Data Engineering | 25% | - Data Loading and Pipelines
|
| Topic 3: Performance Optimization | 20% | - Query Performance
|
| Topic 4: Snowflake Architecture | 30% | - Platform Architecture Design
|
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NEW QUESTION # 176
A media company needs a data pipeline that will ingest customer review data into a Snowflake table, and apply some transformations. The company also needs to use Amazon Comprehend to do sentiment analysis and make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions.
The data pipeline needs to run continuously and efficiently as new records arrive in the object storage leveraging event notifications. Also, the operational complexity, maintenance of the infrastructure, including platform upgrades and security, and the development effort should be minimal.
Which design will meet these requirements?
Answer: D
Explanation:
Option B is the best design to meet the requirements because it uses Snowpipe to ingest the data continuously and efficiently as new records arrive in the object storage, leveraging event notifications. Snowpipe is a service that automates the loading of data from external sources into Snowflake tables1. It also uses streams and tasks to orchestrate transformations on the ingested data. Streams are objects that store the change history of a table, and tasks are objects that execute SQL statements on a schedule or when triggered by another task2. Option B also uses an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. An external function is a user-defined function that calls an external API, such as Amazon Comprehend, to perform computations that are not natively supported by Snowflake3. Finally, option B uses the Snowflake Marketplace to make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions. The Snowflake Marketplace is a platform that enables data providers to list and share their data sets with data consumers, regardless of the cloud platform or region they use4.
Option A is not the best design because it uses copy into to ingest the data, which is not as efficient and continuous as Snowpipe. Copy into is a SQL command that loads data from files into a table in a single transaction. It also exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.
Option C is not the best design because it uses Amazon EMR and PySpark to ingest and transform the data, which also increases the operational complexity and maintenance of the infrastructure. Amazon EMR is a cloud service that provides a managed Hadoop framework to process and analyze large-scale data sets. PySpark is a Python API for Spark, a distributed computing framework that can run on Hadoop. Option C also develops a python program to do model inference by leveraging the Amazon Comprehend text analysis API, which increases the development effort.
Option D is not the best design because it is identical to option A, except for the ingestion method. It still exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.
NEW QUESTION # 177
An Architect has a table called leader_follower that contains a single column named JSON. The table has one row with the following structure:
{
"activities": [
{ "activityNumber": 1, "winner": 5 },
{ "activityNumber": 2, "winner": 4 }
],
"follower": {
"name": { "default": "Matt" },
"number": 4
},
"leader": {
"name": { "default": "Adam" },
"number": 5
}
}
Which query will produce the following results?
ACTIVITY_NUMBER
WINNER_NAME
1
Adam
2
Matt
Answer: J
Explanation:
This question tests several core Snowflake semi-structured data concepts that are explicitly part of the SnowPro Architect exam scope: working with VARIANT data, array handling, and the use of LATERAL FLATTEN. The activities element in the JSON structure is an array, meaning it must be flattened before individual attributes such as activityNumber and winner can be accessed. Option A is invalid because it attempts to directly reference fields inside an array without flattening it.
Option B correctly uses LATERAL FLATTEN on json:activities, which produces one row per activity. The alias p.value represents each array element, allowing access to activityNumber and winner. The IFF expression then compares the activity's winner value with leader.number. When they match, the query returns leader.name.default; otherwise, it returns follower.name.default. Casting the result to VARCHAR ensures a proper scalar output.
Option C is incorrect because it attempts to return full JSON objects (leader or follower) rather than the nested name.default value. Option D uses OUTER => TRUE, which is unnecessary in this case because the activities array is guaranteed to exist; while it would still work, Snowflake exam questions typically expect the most precise and minimal correct solution.
NEW QUESTION # 178
What are purposes for creating a storage integration? (Choose three.)
Answer: A,B,E
Explanation:
The purpose of creating a storage integration in Snowflake includes:
B: Store a generated identity and access management (IAM) entity for an external cloud provider - This helps in managing authentication and authorization with external cloud storage without embedding credentials in Snowflake. It supports various cloud providers like AWS, Azure, or GCP, ensuring that the identity management is streamlined across platforms.
C: Support multiple external stages using one single Snowflake object - Storage integrations allow you to set up access configurations that can be reused across multiple external stages, simplifying the management of external data integrations.
D: Avoid supplying credentials when creating a stage or when loading or unloading data - By using a storage integration, Snowflake can interact with external storage without the need to continuously manage or expose sensitive credentials, enhancing security and ease of operations.
Reference: Snowflake documentation on storage integrations, found within the SnowPro Advanced: Architect course materials.
NEW QUESTION # 179
What is a valid object hierarchy when building a Snowflake environment?
Answer: C
Explanation:
This is the valid object hierarchy when building a Snowflake environment, according to the Snowflake documentation and the web search results. Snowflake is a cloud data platform that supports various types of objects, such as databases, schemas, tables, views, stages, warehouses, and more. These objects are organized in a hierarchical structure, as follows:
* Organization: An organization is the top-level entity that represents a group of Snowflake accounts that are related by business needs or ownership. An organization can have one or more accounts, and can enable features such as cross-account data sharing, billing and usage reporting, and single sign-on across accounts12.
* Account: An account is the primary entity that represents a Snowflake customer. An account can have one or more databases, schemas, stages, warehouses, and other objects. An account can also have one or more users, roles, and security integrations. An account is associated with a specific cloud platform, region, and Snowflake edition34.
* Database: A database is a logical grouping of schemas. A database can have one or more schemas, and can store structured, semi-structured, or unstructured data. A database can also have properties such as retention time, encryption, and ownership56.
* Schema: A schema is a logical grouping of tables, views, stages, and other objects. A schema can have one or more objects, and can define the namespace and access control for the objects. A schema can also have properties such as ownership and default warehouse .
* Stage: A stage is a named location that references the files in external or internal storage. A stage can be used to load data into Snowflake tables using the COPY INTO command, or to unload data from Snowflake tables using the COPY INTO LOCATION command. A stage can be created at the account, database, or schema level, and can have properties such as file format, encryption, and credentials .
The other options listed are not valid object hierarchies, because they either omit or misplace some objects in the structure. For example, option A omits the organization level and places the warehouse under the schema level, which is incorrect. Option C omits the organization, account, and stage levels, and places the table under the schema level, which is incorrect. Option D omits the database level and places the stage and table under the account level, which is incorrect.
References:
* Snowflake Documentation: Organizations
* Snowflake Blog: Introducing Organizations in Snowflake
* Snowflake Documentation: Accounts
* Snowflake Blog: Understanding Snowflake Account Structures
* Snowflake Documentation: Databases
* Snowflake Blog: How to Create a Database in Snowflake
* [Snowflake Documentation: Schemas]
* [Snowflake Blog: How to Create a Schema in Snowflake]
* [Snowflake Documentation: Stages]
* [Snowflake Blog: How to Use Stages in Snowflake]
NEW QUESTION # 180
A global retail company must ensure comprehensive data governance, security, and compliance with various international regulations while using Snowflake for data warehousing and analytics.
What should an Architect do to meet these requirements? (Select TWO).
Answer: A,D
Explanation:
Snowflake provides built-in governance and security mechanisms that align with global regulatory requirements. Column-level security-implemented through features such as dynamic data masking and row access policies-allows architects to restrict access to sensitive data at a granular level based on roles or conditions (Answer B). This is essential for compliance with regulations such as GDPR, HIPAA, and similar frameworks that require limiting access to personally identifiable or sensitive data.
Role-Based Access Control (RBAC) is the foundation of Snowflake's security model and is critical for governing who can access which data and perform which actions (Answer D). By assigning privileges to roles instead of users, organizations can centrally manage permissions, enforce separation of duties, and audit access more effectively.
Snowflake does not support column-level network policies, and encryption keys are managed by Snowflake (or via Tri-Secret Secure), not manually by customers. Secure Data Sharing is useful for collaboration but is not a core requirement for governance and compliance in this scenario. For the SnowPro Architect exam, mastering RBAC and column-level security is essential for designing compliant and secure Snowflake architectures.
NEW QUESTION # 181
......
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