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The ARA-C01 exam is highly competitive and acing it is not a piece of cake for majority of the people. It requires a great skill set and deep knowledge ARA-C01 Exam Questions. An aspirant achieving SnowPro Advanced Architect Certification (ARA-C01) certificate truly reflects his hard work and consistent struggle. These ARA-C01 exam practice test a person's true capacities and passing it requires extensive knowledge of each ARA-C01 topic.
| Certification Vendor: | Snowflake |
|---|---|
| Exam Name: | SnowPro Advanced: Architect Certification Exam |
| Exam Number: | ARA-C01 |
| Exam Duration: | 115 minutes |
| Exam Format: | Multiple select, Multiple choice |
| Related Certifications: | SnowPro Core Certification |
| Certificate Validity Period: | 2 years |
| Exam Price: | $375 USD |
| Passing Score: | 750 (scaled score out of 1000) |
| Available Languages: | English |
| Real Exam Qty: | Approximately 65 |
| Recommended Training: | Snowflake Training & Certification Learning Path |
| Exam Registration: | Snowflake Certification Portal |
| Sample Questions: | Snowflake ARA-C01 Sample Questions |
| Exam Way: | Online proctored exam |
| Pre Condition: | SnowPro Core Certification is strongly recommended and often expected before attempting the Advanced Architect exam |
| Official Syllabus URL: | https://www.snowflake.com/certification/ |
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The SnowPro Advanced Architect Certification is a valuable credential for data architects and engineers who want to demonstrate their expertise in designing and implementing complex Snowflake solutions. SnowPro Advanced Architect Certification certification provides a competitive edge to professionals by showcasing their skills and knowledge in the field of data management, warehousing, and analytics. SnowPro Advanced Architect Certification certification is recognized globally by companies that use Snowflake for their data management needs.
NEW QUESTION # 117
Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported.
What could be causing this?
Answer: B
Explanation:
Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported. This could be caused by the following factors:
The order of the keys in the JSON was changed. Snowflake stores semi-structured data internally in a column- like structure for the most common elements, and the remainder in a leftovers-like column. The order of the keys in the JSON affects how Snowflake determines the common elements and how it optimizes the query performance. If the order of the keys in the JSON was changed, Snowflake might have to re-parse the data and re-organize the internal storage, which could result in slower query performance.
There were variations in string lengths for the JSON values in the recent data imports. Non-native values, such as dates and timestamps, are stored as strings when loaded into a VARIANT column. Operations on these values could be slower and also consume more space than when stored in a relational column with the corresponding data type. If there were variations in string lengths for the JSON values in the recent data imports, Snowflake might have to allocate more space and perform more conversions, which could also result in slower query performance.
The other options are not valid causes for poor query performance:
There were JSON nulls in the recent data imports. Snowflake supports two types of null values in semi- structured data: SQL NULL and JSON null. SQL NULL means the value is missing or unknown, while JSON null means the value is explicitly set to null. Snowflake can distinguish between these two types of null values and handle them accordingly. Having JSON nulls in the recent data imports should not affect the query performance significantly.
The recent data imports contained fewer fields than usual. Snowflake can handle semi-structured data with varying schemas and fields. Having fewer fields than usual in the recent data imports should not affect the query performance significantly, as Snowflake can still optimize the data ingestion and query execution based on the existing fields.
Considerations for Semi-structured Data Stored in VARIANT
Snowflake Architect Training
Snowflake query performance on unique element in variant column
Snowflake variant performance
NEW QUESTION # 118
Which command will you run to list all privileges and roles granted to the role
Answer: D
NEW QUESTION # 119
Why does a conditional multi-table insert option support the Data Vault data model?
Answer: B
Explanation:
The Data Vault modeling approach separates data into hubs (business keys), links, and satellites, often requiring simultaneous insertion of related records derived from the same source dataset. Snowflake's conditional multi-table INSERT enables a single source query to populate multiple target tables in parallel based on conditional logic. This capability aligns well with Data Vault patterns, where hubs and satellites are typically loaded together from the same staging data.
By inserting into hubs and satellites in parallel using surrogate keys (Answer A), architects ensure consistency, atomicity, and efficient processing without re-scanning source data multiple times. This approach reduces compute usage and simplifies ETL logic, which is particularly valuable in large-scale, near-real-time ingestion pipelines.
Options involving dimensions and facts relate more closely to star schema modeling, not Data Vault.
Sequential insertion is also less efficient and not the defining advantage of conditional multi-table inserts. For SnowPro Architect candidates, this question emphasizes understanding how Snowflake SQL features support modern data modeling techniques such as Data Vault.
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NEW QUESTION # 120
An Architect wants to integrate Snowflake with a Git repository that requires authentication. What is the correct sequence of steps to be followed?
Answer:
Explanation:
Explanation:
This question tests Snowflake's native Git integration setup pattern, which relies on Snowflake security objects to authenticate outbound access to an external Git provider. The correct sequence begins with creating an API integration because it defines and authorizes the external endpoint(s) Snowflake is allowed to communicate with. In Snowflake, integrations are the governance layer for external connectivity- administrators explicitly allow network destinations and control whether the integration is enabled, which is foundational before any credential object can be used.
Next, the Architect creates a secret to securely store the authentication material required by the Git provider (for example, a token or other supported credential). Secrets are designed for securely managing sensitive values and are referenced by other Snowflake objects without exposing the credential in plain text in SQL or configuration.
Finally, the Architect creates the Snowflake Git repository stage, which is the object that actually references the Git repo location and uses the configured integration and secret to authenticate and interact with the repository. Creating the stage last ensures all required prerequisites (allowed connectivity + stored credentials) exist and can be bound to the stage cleanly, aligning with Snowflake's documented dependency model for external integrations.
NEW QUESTION # 121
A data platform team creates two multi-cluster virtual warehouses with the AUTO_SUSPEND value set to NULL on one. and '0' on the other. What would be the execution behavior of these virtual warehouses?
Answer: C
Explanation:
The AUTO_SUSPEND parameter controls the amount of time, in seconds, of inactivity after which a warehouse is automatically suspended. If the parameter is set to NULL, the warehouse never suspends. If the parameter is set to '0', the warehouse suspends immediately after executing a query. Therefore, the execution behavior of the two virtual warehouses will be different depending on the AUTO_SUSPEND value. The warehouse with NULL value will keep running until it is manually suspended or the resource monitor limits are reached. The warehouse with '0' value will suspend as soon as it finishes a query and release the compute resources. Reference:
ALTER WAREHOUSE
Parameters
NEW QUESTION # 122
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