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Snowflake ARA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Performance Optimization20%- Query Performance
  • 1. Caching strategies
  • 2. Materialized views
  • 3. Search optimization service
  • 4. Query profiling and tuning
- Cost and Resource Optimization
  • 1. Concurrency scaling
  • 2. Resource monitors
  • 3. Warehouse cost optimization
Topic 2: Snowflake Architecture30%- Storage and Compute Design
  • 1. Warehouse sizing and scaling
  • 2. Micro-partitions and clustering
  • 3. Database and schema design
- Platform Architecture Design
  • 1. Multi-account strategies
  • 2. Cross-region and cross-cloud architecture
  • 3. Data sharing architecture
  • 4. Organization and account structure
Topic 3: Data Engineering25%- Data Loading and Pipelines
  • 1. Snowpipe and tasks
  • 2. External stages and file formats
  • 3. Bulk and continuous data loading
  • 4. Streams and CDC
- Data Transformation
  • 1. Semi-structured data processing
  • 2. ETL and ELT design
  • 3. Data modeling strategies
Topic 4: Accounts and Security25%- Governance and Compliance
  • 1. Object ownership and privilege management
  • 2. Data governance strategies
  • 3. Encryption and security architecture
- Access Control and Authentication
  • 1. Data masking and row access policies
  • 2. SSO and MFA
  • 3. Role-based access control
  • 4. Network policies

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Snowflake SnowPro Advanced Architect Certification Sample Questions (Q12-Q17):

NEW QUESTION # 12
An Architect needs to define a table structure for an unfamiliar semi-structured data set. The Architect wants to identify a list of distinct key names present in the semi-structured objects.
What function should be used?

Answer: D

Explanation:
When working with unfamiliar semi-structured data such as JSON, a common first step is to explore its structure and identify all possible keys. Snowflake's FLATTEN function is specifically designed to explode VARIANT, OBJECT, or ARRAY data into relational form. Using the RECURSIVE option allows FLATTEN to traverse nested objects and arrays, returning all nested keys regardless of depth (Answer A).
This approach enables architects to query and aggregate distinct key names, making it ideal for schema discovery and exploratory analysis. INFER_SCHEMA, by contrast, is used primarily with staged files to infer column definitions for external tables or COPY operations, not for exploring existing VARIANT data already stored in tables. PARSE_JSON simply converts a string into a VARIANT type and does not help identify keys. RESULT_SCAN is used to query the results of a previously executed query and is unrelated to schema discovery.
For SnowPro Architect candidates, this highlights an important semi-structured data design pattern: using FLATTEN (often with RECURSIVE) to explore, profile, and understand evolving data structures before committing to a relational schema or transformation pipeline.
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NEW QUESTION # 13
While choosing a cluster key, what is recommended by snowflake?

Answer: A,B


NEW QUESTION # 14
An Architect needs to design a data unloading strategy for Snowflake, that will be used with the COPY INTO
<location> command.
Which configuration is valid?

Answer: B


NEW QUESTION # 15
Which columns can be included in an external table schema? (Select THREE).

Answer: C,E,F


NEW QUESTION # 16
You have created a table as below
CREATE TABLE SNOWFLAKE_BLOG(BLOG_ID NUMBER, BLOG_CONTENT VARCHAR,BLOG_AUTHOR VARCHAR,BLOG_CREATE_DATE TIMESTAMP );
Now you want to cluster it by BLOG_AUTHOR and BLOG_CREATE_DATE.
Which command below will you choose?

Answer: C


NEW QUESTION # 17
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