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Snowflake ARA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced Architect Certification Sample Questions (Q114-Q119):

NEW QUESTION # 114
What Snowflake system functions are used to view and or monitor the clustering metadata for a table? (Select TWO).

Answer: A,E

Explanation:
The Snowflake system functions used to view and monitor the clustering metadata for a table are:
* SYSTEM$CLUSTERING_INFORMATION
* SYSTEM$CLUSTERING_DEPTH
Comprehensive But Short Explanation:
* The SYSTEM$CLUSTERING_INFORMATION function in Snowflake returns a variety of clustering information for a specified table. This information includes the average clustering depth, total number of micro-partitions, total constant partition count, average overlaps, average depth, and a partition depth histogram. This function allows you to specify either one or multiple columns for which the clustering information is returned, and it returns this data in JSON format.
* The SYSTEM$CLUSTERING_DEPTH function computes the average depth of a table based on specified columns or the clustering key defined for the table. A lower average depth indicates that the table is better clustered with respect to the specified columns. This function also allows specifying columns to calculate the depth, and the values need to be enclosed in single quotes.
References:
* SYSTEM$CLUSTERING_INFORMATION: Snowflake Documentation
* SYSTEM$CLUSTERING_DEPTH: Snowflake Documentation


NEW QUESTION # 115
There are two databases in an account, named fin_db and hr_db which contain payroll and employee data, respectively. Accountants and Analysts in the company require different permissions on the objects in these databases to perform their jobs. Accountants need read-write access to fin_db but only require read-only access to hr_db because the database is maintained by human resources personnel.
An Architect needs to create a read-only role for certain employees working in the human resources department.
Which permission sets must be granted to this role?

Answer: A


NEW QUESTION # 116
A user is executing the following command sequentially within a timeframe of 10 minutes from start to finish:

What would be the output of this query?

Answer: A

Explanation:
The query is executing a clone operation on an existing tablet_saleswith an offset to account for the retention time. The syntax used is correct for cloning a table in Snowflake, and the use of theat(offset => -60*30)clause is valid. This specifies that the clone should be based on the state of the table 30 minutes prior (60 seconds *
30). Assuming the tablet_salesexists and has been modified within the last 30 minutes, and considering thedata_retention_time_in_daysis set to 1 day (which enables time travel queries for the past 24 hours), the tablet_sales_clonewould be successfully created based on the state oft_sales30 minutes before the clone command was issued.


NEW QUESTION # 117
An Architect is implementing a CI/CD process. When attempting to clone a table from a production to a development environment, the cloning operation fails.
What could be causing this to happen?

Answer: C

Explanation:
Cloning a table with a masking policy can cause the cloning operation to fail because the masking policy is not automatically cloned with the table. This is due to the fact that the masking policy is considered a separate object with its own set of privileges1.
References
Snowflake Documentation on Cloning Considerations1.


NEW QUESTION # 118
Which Snowflake data modeling approach is designed for BI queries?

Answer: C

Explanation:
A star schema is a Snowflake data modeling approach that is designed for BI queries. A star schema is a type of dimensional modeling that organizes data into fact tables and dimension tables. A fact table contains the measures or metrics of the business process, such as sales amount, order quantity, or profit margin. A dimension table contains the attributes or descriptors of the business process, such as product name, customer name, or order date. A star schema is called so because it resembles a star, with one fact table in the center and multiple dimension tables radiating from it. A star schema can improve the performance and simplicity of BI queries by reducing the number of joins, providing fast access to aggregated data, and enabling intuitive query syntax. A star schema can also support various types of analysis, such as trend analysis, slice and dice, drill down, and roll up12.
References:
* Snowflake Documentation: Dimensional Modeling
* Snowflake Documentation: Star Schema


NEW QUESTION # 119
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