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SnowFlake ARA-C01 SnowPro Advanced Architect Certification

1. Accounts and Security

- Account Management

- Multi-Account Strategies: Design and implement strategies for managing multiple Snowflake accounts (e.g., production, development, testing) using organizations.
- Organization Administration: Configure and manage Snowflake organizations, including account creation, role assignments, and resource allocation.
- Cross-Account Data Sharing: Implement secure data sharing between accounts using Snowflake’s secure data sharing capabilities.
- Account Parameters: Configure account-level settings, such as session policies, timeout settings, and default time zones.

- Authentication and Authorization

- Single Sign-On (SSO): Configure SSO integration with identity providers (e.g., Okta, Azure AD) for centralized authentication.
- Multi-Factor Authentication (MFA): Enable and enforce MFA for enhanced user security.
- Role-Based Access Control (RBAC): Design and implement RBAC hierarchies, including custom roles, system roles, and privilege assignments.
- Access Control Models: Apply fine-grained access controls using privileges (e.g., SELECT, MODIFY) and object-level permissions.
- OAuth and External Authentication: Configure OAuth for secure API access and external application integration.

- Security and Compliance

- Data Encryption: Ensure data is encrypted at rest and in transit using Snowflake’s built-in encryption mechanisms (e.g., AES-256, TLS).
- Network Policies: Implement network policies to restrict access based on IP allowlists or blocklists.
- Private Connectivity: Configure private connectivity options, such as AWS PrivateLink, Azure Private Link, or Google Cloud Private Service Connect.
- Compliance Standards: Align Snowflake configurations with regulatory requirements (e.g., GDPR, HIPAA, SOC 2).
- Key Pair Authentication: Set up key pair authentication for secure programmatic access.

- Monitoring and Auditing

- Access History: Use Snowflake’s access history to monitor user activity and detect unauthorized access.
- Audit Logs: Analyze query history and login history for security auditing purposes.
- Resource Monitors: Configure resource monitors to track and manage account-level usage and prevent overages.

2. Snowflake Architecture

- Snowflake Core Architecture

- Cloud Services Layer: Understand the role of the cloud services layer in handling authentication, metadata management, and query optimization.
- Compute Layer: Design and manage virtual warehouses for compute isolation and scalability.
- Storage Layer: Leverage Snowflake’s cloud-agnostic storage for data persistence and separation of compute and storage.
- Multi-Cloud and Cross-Cloud Deployments: Architect solutions that span multiple cloud providers (AWS, Azure, Google Cloud).

- Data Storage and Management

- Micro-Partitions: Understand how Snowflake organizes data into micro-partitions for efficient querying and storage.
- Clustering Keys: Design and implement clustering keys to optimize data retrieval for large tables.
- Table Types: Differentiate between permanent, transient, and temporary tables and their use cases.
- Time Travel: Configure and manage time travel for data recovery and historical analysis.
- Fail-Safe: Understand fail-safe retention periods and their impact on storage costs.

- Scalability and Elasticity

- Virtual Warehouse Sizing: Select appropriate warehouse sizes (e.g., X-Small to 4X-Large) based on workload requirements.
- Auto-Scaling: Configure auto-scaling for warehouses to handle variable workloads.
- Multi-Cluster Warehouses: Implement multi-cluster warehouses for concurrent query execution and load balancing.
- Resource Optimization: Balance compute and storage resources to minimize costs while maintaining performance.

- Data Sharing and Collaboration

- Snowflake Data Marketplace: Architect solutions that leverage the Snowflake Data Marketplace for third-party data integration.
- Secure Data Sharing: Implement secure direct shares, reader accounts, and database replication for external collaboration.
- Snowgrid: Understand Snowflake’s Snowgrid for cross-region and cross-cloud data sharing.

3. Data Engineering

- Data Ingestion

- Bulk Loading: Use the COPY INTO command for efficient bulk data loading from cloud storage (e.g., S3, Azure Blob, Google Cloud Storage).
- Snowpipe: Implement Snowpipe for continuous, automated data ingestion from cloud storage.
- External Tables: Create and manage external tables for querying data in external cloud storage without ingestion.
- File Formats: Configure file formats (e.g., CSV, JSON, Parquet) and handle complex data structures during ingestion.

- Data Transformation

- SQL-Based Transformations: Write complex SQL queries for data transformation, including joins, aggregations, and window functions.
- Stored Procedures: Develop stored procedures using Snowflake’s JavaScript-based scripting for advanced logic.
- User-Defined Functions (UDFs): Create scalar and table UDFs in SQL, JavaScript, or other supported languages.
- Streams and Tasks: Implement streams for change data capture (CDC) and tasks for scheduling automated workflows.

- Data Integration

- ETL/ELT Pipelines: Design ELT pipelines using Snowflake as the transformation engine and external orchestration tools (e.g., Airflow, dbt).
- Partner Ecosystem: Integrate Snowflake with partner tools for data integration (e.g., Informatica, Talend, Fivetran).
- External Functions: Configure external functions to integrate Snowflake with external services (e.g., AWS Lambda).
- Snowflake Connector: Use Snowflake connectors for Python, Spark, or Kafka to build custom integrations.

- Data Governance

- Data Cataloging: Implement tagging and classification for data governance and discovery.
- Dynamic Data Masking: Apply dynamic data masking to protect sensitive data during querying.
- Row-Level Security: Configure row access policies to restrict data access based on user attributes.
- Object Tagging: Use object tagging for cost allocation and governance tracking.

Performance Optimization

- Query Optimization

- Query Execution Plans: Analyze query profiles and execution plans to identify performance bottlenecks.
- Materialized Views: Implement materialized views to precompute and store query results for faster access.
- Search Optimization Service: Enable search optimization for point lookups and selective queries on large tables.
- Caching Mechanisms: Leverage result caching, metadata caching, and local disk caching to reduce query execution time.

- Data Organization

- Clustering Keys: Optimize table clustering to reduce data scanning for frequently queried columns.
- Partition Pruning: Design schemas and queries to maximize partition pruning for efficient data access.
- Data Compression: Understand Snowflake’s automatic compression and its impact on storage and query performance.

- Warehouse Optimization

- Warehouse Tuning: Adjust warehouse sizes and configurations to match workload demands.
- Query Acceleration Service: Enable query acceleration for complex queries with unpredictable performance.
- Concurrency Management: Configure warehouses to handle high-concurrency workloads using multi-cluster setups.
- Auto-Suspend and Auto-Resume: Optimize cost by enabling auto-suspend and auto-resume for idle warehouses.

- Cost Management

- Storage Cost Optimization: Minimize storage costs by managing time travel and fail-safe retention periods.
- Compute Cost Optimization: Monitor and optimize warehouse usage to reduce compute costs.
- Resource Monitors: Use resource monitors to enforce budget limits and prevent unexpected overages.
- Cost Attribution: Implement tagging and account-level reporting for cost allocation across teams or projects.

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SnowFlake
ARA-C01
SnowPro Advanced Architect Certification
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Question: 191
What conditions should be true for a table to consider search optimization
A. The table size is at least 100 GB
B. The table is not clustered OR The table is frequently queried on columns other than the primary cluster key
C. The table can be of any size
Answer: A,B
Explanation:
Search optimization works best to Improve the performance of a query when the following conditions are true:
For the table being queried:
Question: 192
One of your query is taking a long time to finish, when you open the query profiler you see that lot of data is spilling
to the remote disk(Bytes spilled to remote storage).
What may be the cause of this?
A. The amount of memory available for the servers used to execute the operation might not be sufficient to hold
intermediate results
B. The size of the AWS bucket used to hold the data is not sufficient for the query
C. Number of disks attached to the virtual warehouse is not enough for the processing
Answer: A
Explanation:
This is again a question based on work experience. One variation of this may be, you will be given a
query profile snapshot which will be having a huge number against Bytes spilled to remote storage. You
will be asked to find the possible cuase
Queries Too Large to Fit in Memory
For some operations (e.g. duplicate elimination for a huge data set), the amount of memory available for the servers
used to execute the operation might not be sufficient to hold intermediate results. As a result, the query processing
engine will start spilling the data to local disk. If the local disk space is not sufficient, the spilled data is then saved to
remote disks.
This spilling can have a profound effect on query performance (especially if remote disk is used for spilling). To
alleviate this, we recommend:
Question: 193
While loading data into a table from stage, which are the valid copyOptions
A. CONTINUE
B. SKIP_FILE
C. SKIP_FILE_
D. SKIP_FILE_%
E. ABORT_STATEMENT
F. ERROR_STATEMENT
Answer: A,B,C,D,E
Explanation:
Question: 194
For this object, Snowflake executes code outside Snowflake; the executed code is known as remote service.
What is this object called?
A. External procedure
B. External function
C. External Script
D. External job
Answer: B
Explanation:
An external function calls code that executes outside Snowflake; the executed code is known as a remote service.
Users can write and call their own remote services, or call remote services written by third parties. These remote
services can be written using any HTTP server stack, including cloud serverless compute services such as AWS
Lambda.
From the perspective of a user running a SQL statement, an external function behaves like any other scalar function. A
SQL statement performs the following actions: Calls the function, optionally passing parameters.
Receives a value back from the function.
In SQL statements, external functions generally behave like UDFs (user-defined functions). For example, external
functions follow these rules:
Inside Snowflake, an external function is represented as a database object. That object is created in a specific database
and schema, and can be referenced using dot notation (e.g.
MY_DATABASE.MY_SCHEMA.MY_EXTERNAL_FUNCTION()).
An external function can appear in any clause of a SQL statement in which other types of functions can appear (e.g.
the WHERE clause).
The returned value can be a compound value, such as a VARIANT that contains JSON.
External functions can be overloaded; two different functions can have the same name but different signatures
(different numbers or data types of input parameters).
An external function can be part of a more complex expression: select
upper(zipcode_to_city_external_function(zipcode)) from address_table;
https://docs.snowflake.com/en/sql-reference/external-functions-introduction.html#what-is-an-external-fun ction
Question: 195
Validation mode can take the below options
A. RETURN__ROWS
B. RETURN_ERRORS
C. RETURN_ALL_ERRORS
D. RETURN_SEVERE_EERORS_ONLY
Answer: A,B,C
Explanation:
VALIDATION_MODE = RETURN_n_ROWS | RETURN_ERRORS | RETURN_ALL_ERRORS
String (constant) that instructs the COPY command to validate the data files instead of loading them into the specified
table; i.e. the COPY command tests the files for errors but does not load them. The command validates the data to be
loaded and returns results based on the validation option specified:
Question: 196
Which semi structured data function interprets an input string as a JSON document, producing a VARIANT value.
A. PARSE_JSON
B. PARSE_XML
C. STRIP_JSON
Answer: A
Explanation:
Try a hands-on exercise to understand this
create or replace table vartab (n number(2), v variant); insert into vartab
select column1 as n, parse_json(column2) as v
from values (1, �null�),
(2, null),
(3, �true�),
(4, �-17�),
(7, �"Om ara pa ca na dhih" �),
(8, �[-1, 12, 289, 2188, false,]�),
(9, �{ "x" : "abc", "y" : false, "z": 10} �)
as vals;
select n, v, typeof(v) from vartab;
Question: 197
Remote service in external function can be an AWS Lambda function
A. TRUE
B. FALSE
Answer: A
Explanation:
remote service
A remote service is stored and executed outside Snowflake, and returns a value. For example, remote
services can be implemented as:
An AWS Lambda function.
An HTTPS server (e.g. Node.js) running on an EC2 instance.
To be called by the Snowflake external function feature, the remote service must:
Expose an HTTPS endpoint.
Accept JSON inputs and return JSON outputs.
Question: 198
Bytes spilled to remote storage in query profile indicates volume of data spilled to remote disk
A. TRUE
B. FALSE
Answer: A
Explanation:
This question may come in various format in the exam, so let us not mug it up. Let us understand what it means.
When you run large aggregations, sorts in snowflake the processing usually happens in memory of the virtual
warehouse. But if the virtual warehouse is not properly sized and if it does not have enough memory, the intermediate
results starts spilling to remote disk(in AWS, it will be S3). When this happens, it impacts the query performance
because now you are retrieving your results from remote disk instead of memory. In most of these cases, if it is a
regular occurrence you may need to move to a bigger warehouse.
Also read this section referred in the link
https://docs.snowflake.com/en/user-guide/ui-query-profile.html#queries-too-large-to-fit-in-memory
Question: 199
{"stuId":2000,"stuCourse":"Snowflake"}
How will you write a query that will check if stuId in JSON in #1 is also there in JSON in#2
A. with stu_demography as (select parse_json(column1) as src, src:stuId as ID from values(�{"stuId":2000,
"stuName":"Amy"}�)),
B. stu_course as (select parse_json(column1) as src, src:stuId as ID from
values(�{"stuId":2000,"stuCourse":"Snowflake"}�)) select case when stdemo.ID in(select ID from stu_course) then
�True� else �False� end as result from stu_demography stdemo;
C. with stu_demography as (select parse_json(column1) as src, src[�stuId�] as ID from values(�{"stuId":2000,
"stuName":"Amy"}�)), stu_course as (select parse_json(column1) as src, src[�stuId�] as ID from
values(�{"stuId":2000,"stuCourse":"Snowflake"}�)) select case when stdemo.ID in(select ID from stu_course) then
�True� else �False� end as result from stu_demography stdemo;
D. SELECT CONTAINS(�{"stuId":2000, "stuName":"Amy"}�,'{"stuId":2000,"stuCourse":"Snowflake"}�);
E. with stu_demography as (select parse_json(column1) as src, src[�STUID�] as ID from values(�{"stuId":2000,
"stuName":"Amy"}�)), stu_course as (select parse_json(column1) as src, src[�stuId�] as ID from
values(�{"stuId":2000,"stuCourse":"Snowflake"}�)) select case when stdemo.ID in(select ID
from stu_course) then �True� else �False� end as result from stu_demography stdemo;
Answer: B,C
Explanation:
I would like you to try this out in your snowflake instance and find that out
Please note that this may not be the way the question will appear in the certification exam, but why we are still
learning this?
Question: 200
In the default access control hierarchy, both securityadmin and sysadmin are owned by accountadmin
A. TRUE
B. FALSE
Answer: A
Explanation:
Role hierarchy is an important concept that you should read thoroughly. More than one question may
appear in the test on this topic. Please remember in snowflake you cannot assign a privilege to a user
directly. You need to create role and grant privileges to the role and then assign users to the role. As role
can be assigned to another role also.
https://docs.snowflake.com/en/user-guide/security-access-control-overview.html#role-hierarchy-and-privi
lege-inheritance
Question: 201
You are running a large join on snowflake. You ran it on a medium warehouse and it took almost an hour to run. You
then tried to run the join on a large warehouse but still the performance did not improve.
What may be the most possible cause of this.
A. There may be a symptom on skew in your data where one of the value of the column is significantly more than rest
of the values in the column
B. Your warehouses do not have enough memory
C. Since you have configured an warehouse with a low auto-suspend value, your warehouse is going
down frequently
Answer: A
Explanation:
In the snowflake advanced architect exam, 40% of the questions will be based on work experience and this is one such
question. You need to have a very good hold on the concepts of Snowflake. So, what may be happening here?
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