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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowflake Architecture | 30% | - Data protection and recovery strategies - Object hierarchy and architectural impact - Data modeling and use cases - Development lifecycle and workload support - Data sharing architecture design |
| Topic 2: Performance Optimization | 20% | - Clustering and partitioning strategies - Query performance tuning - Cost optimization and resource management - Virtual warehouse configuration and sizing |
| Topic 3: Accounts and Security | 25% | - Security principles and implementation scenarios - Account and database strategy design - Security, compliance and governance architecture |
| Topic 4: Data Engineering | 25% | - Data loading and unloading solutions - Transformation and processing frameworks - Data pipeline and integration design |
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NEW QUESTION # 168
An event table has 150B rows and 1.5M micro-partitions, with the following statistics:
Column NDV*
A_ID 11K
C_DATE 110
NAME 300K
EVENT_ACT_0 1.1G
EVENT_ACT_4 2.2G
*NDV = Number of Distinct Values
What three clustering keys should be used, in order?
Answer: C
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Snowflake SnowPro Architect exam scope and all publicly documented material:
Clustering keys are most beneficial when they improve micro-partition pruning for common filter patterns and when the chosen columns provide a useful ordering that co-locates data. A common heuristic is to place lower- cardinality columns earlier (to quickly narrow partitions) and then add a higher-cardinality column that further reduces scanned partitions for selective access paths. Here, C_DATE has very low NDV (110), making it an excellent leading key to organize data by date and enable strong pruning for time-bound queries typical of event tables. Next, A_ID (11K) is moderate cardinality and can further segment data within a date range, helping point lookups or narrow scans by identifier. For the third key, the options force choosing between very high-cardinality event activity columns; selecting EVENT_ACT_0 (1.1G) is preferable to EVENT_ACT_4 (2.2G) because it is comparatively less distinct while still supporting additional pruning when queries filter by that attribute. This ordering aligns with Snowflake guidance: keep keys few, ordered to match common predicates, and avoid excessively high-cardinality keys unless they directly match frequent selective filters.
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NEW QUESTION # 169
Following objects can be cloned in snowflake
Answer: A,B,C
Explanation:
* Snowflake supports cloning of various objects, such as databases, schemas, tables, stages, file formats, sequences, streams, tasks, and roles. Cloning creates a copy of an existing object in the system without copying the data or metadata. Cloning is also known as zero-copy cloning1.
* Among the objects listed in the question, the following ones can be cloned in Snowflake:
* Permanent table: A permanent table is a type of table that has a Fail-safe period and a Time Travel retention period of up to 90 days. A permanent table can be cloned using the CREATE TABLE ...
CLONE command2. Therefore, option A is correct.
* Transient table: A transient table is a type of table that does not have a Fail-safe period and can have a Time Travel retention period of either 0 or 1 day. A transient table can also be cloned using the CREATE TABLE ... CLONE command2. Therefore, option B is correct.
* External table: An external table is a type of table that references data files stored in an external location, such as Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage. An external table can be cloned using the CREATE EXTERNAL TABLE ... CLONE command3.
Therefore, option D is correct.
* The following objects listed in the question cannot be cloned in Snowflake:
* Temporary table: A temporary table is a type of table that is automatically dropped when the session ends or the current user logs out. Temporary tables do not support cloning4. Therefore, option C is incorrect.
* Internal stage: An internal stage is a type of stage that is managed by Snowflake and stores files in Snowflake's internal cloud storage. Internal stages do not support cloning5. Therefore, option E is
* incorrect.
References: : Cloning Considerations : CREATE TABLE ... CLONE : CREATE EXTERNAL TABLE ...
CLONE : Temporary Tables : Internal Stages
NEW QUESTION # 170
A Snowflake Architect is designing a multi-tenant application strategy for an organization in the Snowflake Data Cloud and is considering using an Account Per Tenant strategy.
Which requirements will be addressed with this approach? (Choose two.)
Answer: C,E
Explanation:
* An Account Per Tenant strategy means creating a separate Snowflake account for each tenant (customer or business unit) of the multi-tenant application.
* This approach has some advantages and disadvantages compared to other strategies, such as Database Per Tenant or Schema Per Tenant.
* One advantage is that each tenant can have a unique data shape, meaning they can define their own tables, views, and other objects without affecting other tenants. This allows for more flexibility and customization for each tenant. Therefore, option D is correct.
* Another advantage is that storage costs can be optimized, because each tenant can use their own storage credits and manage their own data retention policies. This also reduces the risk of data spillover or cross-tenant access. Therefore, option E is correct.
* However, this approach also has some drawbacks, such as:
* It requires more administrative overhead and complexity to manage multiple accounts and their resources.
* It may not optimize compute costs, because each tenant has to provision their own warehouses and pay for their own compute credits. This may result in underutilization or overprovisioning of compute resources. Therefore, option C is incorrect.
* It may not simplify security and RBAC policies, because each account has to define its own roles, users, and privileges. This may increase the risk of human errors or inconsistencies in security configurations. Therefore, option B is incorrect.
* It may not reduce the number of objects per tenant, because each tenant still has to create their own databases, schemas, and other objects within their account. This may affect the performance and scalability of the application. Therefore, option A is incorrect.
References: : Multi-Tenant Application Strategies
NEW QUESTION # 171
Which of the two are limitations of the insertReport API of SnowPipe?
Answer: A,C
NEW QUESTION # 172
An Architect on a new project has been asked to design an architecture that meets Snowflake security, compliance, and governance requirements as follows:
1) Use Tri-Secret Secure in Snowflake
2) Share some information stored in a view with another Snowflake customer
3) Hide portions of sensitive information from some columns
4) Use zero-copy cloning to refresh the non-production environment from the production environment To meet these requirements, which design elements must be implemented? (Choose three.)
Answer: D,E,F
Explanation:
These three design elements are required to meet the security, compliance, and governance requirements for the project.
To use Tri-Secret Secure in Snowflake, the Business Critical edition of Snowflake is required. This edition provides enhanced data protection features, such as customer-managed encryption keys, that are not available in lower editions. Tri-Secret Secure is a feature that combines a Snowflake-maintained key and a customer- managed key to create a composite master key to encrypt the data in Snowflake1.
To share some information stored in a view with another Snowflake customer, a secure view is recommended.
A secure view is a view that hides the underlying data and the view definition from unauthorized users. Only the owner of the view and the users who are granted the owner's role can see the view definition and the data in the base tables of the view2. A secure view can be shared with another Snowflake account using a data share3.
To hide portions of sensitive information from some columns, Dynamic Data Masking can be used. Dynamic Data Masking is a feature that allows applying masking policies to columns to selectively mask plain-text data at query time. Depending on the masking policy conditions and the user's role, the data can be fully or partially masked, or shown as plain-text4.
NEW QUESTION # 173
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