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Get All SnowPro Advanced: Architect Certification Exam Questions with Validated Answers
| Vendor: | Snowflake |
|---|---|
| Exam Code: | ARA-C01 |
| Exam Name: | SnowPro Advanced: Architect Certification Exam |
| Exam Questions: | 162 |
| Last Updated: | July 7, 2026 |
| Related Certifications: | SnowPro Certification |
| Exam Tags: |
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Which technique will efficiently ingest and consume semi-structured data for Snowflake data lake workloads?
Option C is the correct answer because schema-on-read is a technique that allows Snowflake to ingest and consume semi-structured data without requiring a predefined schema. Snowflake supports various semi-structured data formats such as JSON, Avro, ORC, Parquet, and XML, and provides native data types (ARRAY, OBJECT, and VARIANT) for storing them. Snowflake also provides native support for querying semi-structured data using SQL and dot notation. Schema-on-read enables Snowflake to query semi-structured data at the same speed as performing relational queries while preserving the flexibility of schema-on-read. Snowflake's near-instant elasticity rightsizes compute resources, and consumption-based pricing ensures you only pay for what you use.
Option A is incorrect because IDEF1X is a data modeling technique that defines the structure and constraints of relational data using diagrams and notations. IDEF1X is not suitable for ingesting and consuming semi-structured data, which does not have a fixed schema or structure.
Option B is incorrect because schema-on-write is a technique that requires defining a schema before loading and processing data. Schema-on-write is not efficient for ingesting and consuming semi-structured data, which may have varying or complex structures that are difficult to fit into a predefined schema. Schema-on-write also introduces additional overhead and complexity for data transformation and validation.
Option D is incorrect because information schema is a set of metadata views that provide information about the objects and privileges in a Snowflake database. Information schema is not a technique for ingesting and consuming semi-structured data, but rather a way of accessing metadata about the data.
Two queries are run on the customer_address table:
create or replace TABLE CUSTOMER_ADDRESS ( CA_ADDRESS_SK NUMBER(38,0), CA_ADDRESS_ID VARCHAR(16), CA_STREET_NUMBER VARCHAR(IO) CA_STREET_NAME VARCHAR(60), CA_STREET_TYPE VARCHAR(15), CA_SUITE_NUMBER VARCHAR(10), CA_CITY VARCHAR(60), CA_COUNTY
VARCHAR(30), CA_STATE VARCHAR(2), CA_ZIP VARCHAR(10), CA_COUNTRY VARCHAR(20), CA_GMT_OFFSET NUMBER(5,2), CA_LOCATION_TYPE
VARCHAR(20) );
ALTER TABLE DEMO_DB.DEMO_SCH.CUSTOMER_ADDRESS ADD SEARCH OPTIMIZATION ON SUBSTRING(CA_ADDRESS_ID);
Which queries will benefit from the use of the search optimization service? (Select TWO).
The use of the search optimization service in Snowflake is particularly effective when queries involve operations that match exact substrings or start from the beginning of a string. The ALTER TABLE command adding search optimization specifically for substrings on the CA_ADDRESS_ID field allows the service to create an optimized search path for queries using substring matches.
Option A benefits because it directly matches a substring from the start of the CA_ADDRESS_ID, aligning with the optimization's capability to quickly locate records based on the beginning segments of strings.
Option B also benefits, despite performing a full equality check, because it essentially compares the full length of CA_ADDRESS_ID to a substring, which can leverage the substring index for efficient retrieval. Options C, D, and E involve patterns that do not start from the beginning of the string or use negations, which are not optimized by the search optimization service configured for starting substring matches. Reference: Snowflake's documentation on the use of search optimization for substring matching in SQL queries.
A retail company has 2000+ stores spread across the country. Store Managers report that they are having trouble running key reports related to inventory management, sales targets, payroll, and staffing during business hours. The Managers report that performance is poor and time-outs occur frequently.
Currently all reports share the same Snowflake virtual warehouse.
How should this situation be addressed? (Select TWO).
The best way to address the performance issues and time-outs faced by the Store Manager team is to configure a dedicated virtual warehouse for them and make it multi-clustered. This will allow them to run their reports independently from other workloads and scale up or down the compute resources as needed. A dedicated virtual warehouse will also enable them to apply specific security and access policies for their data. A multi-clustered virtual warehouse will provide high availability and concurrency for their queries and avoid queuing or throttling.
Using a Business Intelligence tool for in-memory computation may improve performance, but it will not solve the underlying issue of insufficient compute resources in the shared virtual warehouse. It will also introduce additional costs and complexity for the data architecture.
Configuring the virtual warehouse to size 4-XL may increase the performance, but it will also increase the cost and may not be optimal for the workload. It will also not address the concurrency and availability issues that may arise from sharing the virtual warehouse with other workloads.
Advising the Store Manager team to defer report execution to off-business hours may reduce the load on the shared virtual warehouse, but it will also reduce the timeliness and usefulness of the reports for the business. It will also not guarantee that the performance issues and time-outs will not occur at other times.
Snowflake SnowPro Advanced Architect Certification - Preparation Guide
What does a Snowflake Architect need to consider when implementing a Snowflake Connector for Kafka?
When loading data into a table that captures the load time in a column with a default value of either CURRENT_TIME () or CURRENT_TIMESTAMP() what will occur?
According to the Snowflake documentation, when loading data into a table that captures the load time in a column with a default value of either CURRENT_TIME () or CURRENT_TIMESTAMP(), the default value is evaluated once per COPY statement, not once per row. Therefore, all rows loaded using a specific COPY statement will have the same timestamp value. This behavior ensures that the timestamp value reflects the time when the data was loaded into the table, not when the data was read from the source or created in the source.Reference:
Snowflake Documentation: Loading Data into Tables with Default Values
Snowflake Documentation: COPY INTO table
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