Free Practice Questions for Snowflake ARA-C01 Exam

This view can be used to query login attempts by Snowflake users within the last 365 days (1 year). It provides information such as the event timestamp, the user name, the client IP, the authentication method, the success or failure status, and the error code or message if the login attempt was unsuccessful. By querying this view, the IT Security team can identify any suspicious or malicious login attempts to Snowflake and take appropriate actions to prevent credential stuffing attacks1. The other options are not the best ways to find recent and ongoing login attempts to Snowflake. Option A is incorrect because the LOGIN_HISTORY Information Schema table function only returns login events within the last 7 days, which may not be sufficient to detect credential stuffing attacks that span a longer period of time2. Option C is incorrect because the History tab in the Snowflake UI only shows the queries executed by the current user or role, not the login events of other users or roles3. Option D is incorrect because the Users section in the Account tab in the Snowflake UI only shows the last login time for each user, not the details of the login attempts or the failures.

The correct answer is A because it grants the specific privilege to create a materialized view on the schema EDW.STG_SCHEMA to the user USER_01 directly.

Option B is incorrect because it grants the privilege to create a materialized view on the entire database EDW, which is too broad and unnecessary. Also, there is a typo in the user name (USERJD1 instead of USER_01).

Option C is incorrect because it grants the privilege to create a materialized view on a different schema (ECW.STG_SCHEKA instead of EDW.STG_SCHEMA). Also, there is no need to create a new role for this purpose.

Option D is incorrect because it grants the privilege to create a materialized view on an invalid object (EDW.STG_SCHEMA is not a valid schema name, it should be EDW.STG_SCHEMA). Also, there is no need to create a new role for this purpose. References:

Snowflake Documentation: CREATE MATERIALIZED VIEW

Snowflake Documentation: Working with Materialized Views

[Snowflake Documentation: GRANT Privileges on a Schema]

 The most efficient solution is to ask the partner to create a share and add the company’s account (Option A). This way, the company can access the live data from the partner without any data movement or manual intervention. Snowflake’s secure data sharing feature allows data providers to share selected objects in a database with other Snowflake accounts. The shared data is read-only and does not incur any storage or compute costs for the data consumers. The data consumers can query the shared data directly or create local copies of the shared objects in their own databases. Option B is not efficient because it involves using the data lake export feature, which is intended for exporting data from Snowflake to an external data lake, not for importing data from another Snowflake account. The data lake export feature also requires the data provider to create an external stage on cloud storage and use the COPY INTO command to export the data into parquet files. The data consumer then needs to create an external table or a file format to load the data from the cloud storage into Snowflake. This process can be complex and costly, especially if the data changes frequently. Option C is not efficient because it does not solve the problem of manual data ingestion and adaptation. Keeping the current structure of daily JSON extracts on an FTP server and requesting the partner to stop changing files, instead only appending new files, does not improve the efficiency or reliability of the data ingestion process. The company still needs to upload the data to Snowflake manually and deal with any schema changes or data quality issues. Option D is not efficient because it requires the partner to set up a Snowflake reader account and use that account to get the data for ingestion. A reader account is a special type of account that can only consume data from the provider account that created it. It is intended for data consumers who are not Snowflake customers and do not have a licensing agreement with Snowflake. A reader account is not suitable for data ingestion from another Snowflake account, as it does not allow uploading, modifying, or unloading data. The company would need to use external tools or interfaces to access the data from the reader account and load it into their own account, which can be slow and expensive. References: The answer can be verified from Snowflake’s official documentation on secure data sharing, data lake export, and reader accounts available on their website. Here are some relevant links:

Introduction to Secure Data Sharing | Snowflake Documentation

Data Lake Export Public Preview Is Now Available on Snowflake | Snowflake Blog

Managing Reader Accounts | Snowflake Documentation

The requirement is for the data to be accessible as quickly as possible after it arrives in the external stage with minimal coding effort.

Option A:Snowpipe with auto-ingest is a service that continuously loads data as it arrives in the stage. With auto-ingest, Snowpipe automatically detects new files as they arrive in a cloud stage and loads the data into the specified Snowflake table with minimal delay and no intervention required. This is an ideal low-maintenance solution for the given scenario where files are arriving at a very high frequency.

Option E:Using a combination of a task and a stream allows for real-time change data capture in Snowflake. A stream records changes (inserts, updates, and deletes) made to a table, and a task can be scheduled to trigger on a very short interval, ensuring that changes are processed into the dashboard tables as they occur.

The best way to integrate an enterprise identity provider with federated authentication and enable automatic user creation and role assignment in Snowflake is to use SCIM (System for Cross-domain Identity Management). SCIM allows Snowflake to synchronize with the identity provider and create users and groups based on the information provided by the identity provider. The groups are mapped to roles in Snowflake, and the users are assigned the roles based on their group membership. This way, the identity provider remains the source of truth for user and group management, and Snowflake automatically reflects the changes without manual intervention. The other options are either incorrect or incomplete, as they involve using OAuth, which is a protocol for authorization, not authentication or user provisioning, and require additional configuration of authorization and resource servers.

 A star schema model is a type of dimensional data model that consists of a single fact table and multiple dimension tables. A 3NF model is a type of relational data model that follows the third normal form, which eliminates data redundancy and ensures referential integrity. A Snowflake Architect might use a star schema model rather than a 3NF model when designing a data architecture to run in Snowflake for the following reasons:

A star schema model is more suitable for analytical queries that require aggregating and slicing data across different dimensions, such as those performed by a BI tool. A 3NF model is more suitable for transactional queries that require inserting, updating, and deleting individual records.

A star schema model is simpler and faster to query than a 3NF model, as it involves fewer joins and less complex SQL statements. A 3NF model is more complex and slower to query, as it involves more joins and more complex SQL statements.

A star schema model can provide a simple flattened single view of the data to a particular group of end users, such as business analysts or data scientists, who need to explore and visualize the data. A 3NF model can provide a more detailed and normalized view of the data to a different group of end users, such as application developers or data engineers, who need to maintain and update the data.

The other options are not valid reasons for choosing a star schema model over a 3NF model in Snowflake:

Snowflake can handle the joins implied in a 3NF data model, as it supports ANSI SQL and has a powerful query engine that can optimize and execute complex queries efficiently.

The Architect can use both star schema and 3NF models to remove data duplication from the data stored in Snowflake, as both models can enforce data integrity and avoid data anomalies. However, the trade-off is that a star schema model may have more data redundancy than a 3NF model, as it denormalizes the data for faster query performance, while a 3NF model may have less data redundancy than a star schema model, as it normalizes the data for easier data maintenance.

The Architect can use both star schema and 3NF models to design a landing zone to receive raw data into Snowflake, as both models can accommodate different types of data sources and formats. However, the choice of the model may depend on the purpose and scope of the landing zone, such as whether it is a temporary or permanent storage, whether it is a staging area or a data lake, and whether it is a single source or a multi-source integration.

Snowflake Architect Training

Data Modeling: Understanding the Star and Snowflake Schemas

Data Vault vs Star Schema vs Third Normal Form: Which Data Model to Use?

Star Schema vs Snowflake Schema: 5 Key Differences

Dimensional Data Modeling - Snowflake schema

Star schema vs Snowflake Schema

If an AWS Administrator accidentally deletes the SQS subscription to the SNS topic in Step 2, the pipe that references the topic to receive event messages from Amazon S3 will no longer be able to receive the messages. This is because the SQS subscription is the link between the SNS topic and the Snowpipe notification channel. Without the subscription, the SNS topic will not be able to send notifications to the Snowpipe queue, and the pipe will not be triggered to load the new files. To restore the system immediately, the user needs to manually create a new SNS topic with a different name and then recreate the pipe by specifying the new SNS topic name in the pipe definition. This will create a new notification channel and a new SQS subscription for the pipe. Alternatively, the user can also recreate the SQS subscription to the existing SNS topic and then alter the pipe to use the same SNS topic name in the pipe definition. This will also restore the notification channel and the pipe functionality. References:

Automating Snowpipe for Amazon S3

Enabling Snowpipe Error Notifications for Amazon SNS

HowTo: Configuration steps for Snowpipe Auto-Ingest with AWS S3 Stages

"To circumvent the 72-hour delay, you can create a SNS topic with a different name. Recreate any pipes that reference the topic using the CREATE OR REPLACE PIPE command, and specify the new topic name."https://docs.snowflake.com/en/user-guide/data-load-snowpipe-ts#snowpipe-stops-loading-files-after-amazon-sns-topic-subscription-is-deleted

The minimum value supported for the buffer.flush.time property is 1 (in seconds). For higher average data flow rates, we suggest that you decrease the default value for improved latency. If cost is a greater concern than latency, you could increase the buffer flush time. Be careful to flush the Kafka memory buffer before it becomes full to avoid out of memory exceptions.https://docs.snowflake.com/en/user-guide/data-load-snowpipe-streaming-kafka