Free Snowflake ARA-C01 Practice Exam with Questions & Answers | Set: 5

 According to the Snowflake documentation, the best practices for choosing clustering keys are:

Choose columns that are frequently used in join predicates. This can improve the join performance by reducing the number of micro-partitions that need to be scanned and joined.

Choose columns that are most actively used in selective filters. This can improve the scan efficiency by skipping micro-partitions that do not match the filter predicates.

Avoid using low cardinality columns, such as gender or country, as clustering keys. This can result in poor clustering and high maintenance costs.

Avoid using TIMESTAMP columns with nanoseconds, as they tend to have very high cardinality and low correlation with other columns. This can also result in poor clustering and high maintenance costs.

Avoid using columns with duplicate values or NULLs, as they can cause skew in the clustering and reduce the benefits of pruning.

Cluster on multiple columns if the queries use multiple filters or join predicates. This can increase the chances of pruning more micro-partitions and improve the compression ratio.

Clustering is not always useful, especially for small or medium-sized tables, or tables that are not frequently queried or updated. Clustering can incur additional costs for initially clustering the data and maintaining the clustering over time.

Clustering Keys & Clustered Tables | Snowflake Documentation

[Considerations for Choosing Clustering for a Table | Snowflake Documentation]

This question evaluates deep understanding of Snowflake RBAC, managed access schemas, and privilege inheritance, all of which are core SnowPro Architect topics. The schema DEV_TEST_DB.SCHTEST is created WITH MANAGED ACCESS, meaning that only the schema owner (or a higher role in the hierarchy) can grant object privileges such as SELECT on tables within the schema.

The table CURRENCY is created by the role DEV_PROJ_OWN, making it the table owner. As the owner, DEV_PROJ_OWN implicitly has full privileges on the table, including SELECT. Therefore, option B succeeds because the role selecting the data owns the table.

In option A, DEV_PROJ_OWN explicitly grants SELECT on the table to ANALYST_PROJ. Since this grant is performed by the schema/table owner and the role ANALYST_PROJ already has USAGE on both the database and schema, the subsequent SELECT succeeds. This makes A valid.

Option C fails because SYSADMIN does not inherit privileges from DEV_SYSADMIN or DEV_PROJ_OWN; Snowflake role inheritance is directional and not lateral. Option D fails for the same reason—MAPPING_ROLE has no privileges on the database or schema. Option E fails because ACCOUNTADMIN does not automatically bypass RBAC; without explicit USAGE and SELECT, access is denied.

According to the SnowPro Advanced: Architect documents and learning resources, column masking policies are applied at query time based on the privileges of the user who runs the query. Therefore, if a user has the privilege to see unmasked data in a column, they will see the original data when they query that column. If they load this column data into another column that does not have a masking policy, the unmasked data will be loaded in the new column, and any user who can query the new column will see the unmasked data as well. The masking policy does not affect the underlying data in the column, only the query results.

Snowflake Documentation: Column Masking

Snowflake Learning: Column Masking

According to the Snowflake documentation, unstructured data files can be shared by using a secure view and Secure Data Sharing. A secure view allows the result of a query to be accessed like a table, and a secure view is specifically designated for data privacy. A scoped URL is an encoded URL that permits temporary access to a staged file without granting privileges to the stage. The URL expires when the persisted query result period ends, which is currently 24 hours. A scoped URL is recommended for file administrators to give scoped access to data files to specific roles in the same account. Snowflake records information in the query history about who uses a scoped URL to access a file, and when. Therefore, a scoped URL is the best option to share unstructured data within Snowflake, as it provides security, accountability, and control over the data access. References:

Sharing unstructured Data with a secure view

Introduction to Loading Unstructured Data

This scenario tests understanding of Snowflake Time Travel and operational efficiency, both key topics in the SnowPro Architect exam. When a table is replaced using CREATE OR REPLACE TABLE, Snowflake treats the original table version as dropped but still recoverable within the Time Travel retention period. The goal is to recover the original data with the least effort and risk.

Option A is the most efficient solution. Using Time Travel with the BEFORE (STATEMENT => …) clause allows the Architect to query the exact state of the table immediately before the replacement occurred. This approach precisely targets the moment before the destructive operation, avoids guessing timestamps, and minimizes operational steps. It is also highly accurate, which is critical in production recovery scenarios.

Option B relies on a timestamp, which introduces risk if the timestamp is incorrect or ambiguous. Option C is invalid because UNDROP TABLE only works when a table has been explicitly dropped, not replaced. Option D introduces unnecessary steps and operational overhead, increasing the risk of mistakes.

The correct answer is B and E because they use the correct syntax and values for the identifier function and the session variables.

The identifier function allows you to use a variable or expression as an identifier (such as a table name or column name) in a SQL statement. It takes a single argument and returns it as an identifier. For example, identifier($tbl_ref) returns EMP_TBL as an identifier.

The session variables are set using the SET command and can be referenced using the $ sign. For example, $var1 returns Name1 as a value.

Option A is incorrect because it uses Stbl_ref and Scol_ref, which are not valid session variables or identifiers. They should be $tbl_ref and $col_ref instead.

Option C is incorrect because it uses identifier, which is not a valid syntax for the identifier function. It should be identifier($tbl_ref) instead.

Option D is incorrect because it uses Cvarl, var2, and var3, which are not valid session variables or values. They should be $var1, $var2, and $var3 instead. References:

Snowflake Documentation: Identifier Function

Snowflake Documentation: Session Variables

Snowflake Learning: SnowPro Advanced: Architect Exam Study Guide

This is the correct command to create a clone of the table Data as it was 5 minutes ago using cloning and Time Travel. Cloning is a feature that allows creating a copy of a database, schema, table, or view without duplicating the data or metadata. Time Travel is a feature that enables accessing historical data (i.e. data that has been changed or deleted) at any point within a defined period. To create a clone of a table at a point in time in the past, the syntax is:

CREATE TABLE CLONE AT (OFFSET => );

The OFFSET parameter specifies the time difference in seconds from the present time. A negative value indicates a point in the past. For example, -60*5 means 5 minutes ago. Alternatively, the TIMESTAMP parameter can be used to specify an exact timestamp in the past. The clone will contain the data as it existed in the source table at the specified point in time12.

Snowflake Documentation: Cloning Objects

Snowflake Documentation: Cloning Objects at a Point in Time in the Past

When using the Snowflake Connector for Kafka, architects must understand the behavior differences between Snowpipe (file-based) and Snowpipe Streaming. Snowpipe Streaming is optimized for low-latency ingestion and works by continuously sending records directly into Snowflake-managed channels rather than staging files. One important characteristic is that Snowpipe Streaming automatically flushes buffered records at short, fixed intervals (approximately every second), ensuring near real-time data availability (Answer D).

Another key consideration is offset handling. The Snowflake Connector for Kafka is designed to tolerate Kafka offset jumps or resets, such as those caused by topic reprocessing or consumer group changes. Snowflake can safely ingest records without corrupting state, relying on Kafka semantics and connector metadata to maintain consistency (Answer E).

Snowpipe Streaming is not always the default ingestion method; configuration determines whether file-based Snowpipe or Streaming is used. Schema detection is not supported in Snowpipe Streaming. Traditional Snowpipe does not offer lower latency than Snowpipe Streaming. For the SnowPro Architect exam, understanding ingestion latency, buffering behavior, and fault tolerance is essential when designing streaming architectures.

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QUESTION NO: 57 [Snowflake Data Engineering]

An Architect wants to create an externally managed Iceberg table in Snowflake.

What parameters are required? (Select THREE).

A. External volume

B. Storage integration

C. External stage

D. Data file path

E. Catalog integration

F. Metadata file path

Answer: A, E, F

Externally managed Iceberg tables in Snowflake rely on external systems for metadata and storage management. An external volume is required to define and manage access to the underlying cloud storage where the Iceberg data files reside (Answer A). A catalog integration is required so Snowflake can interact with the external Iceberg catalog (such as AWS Glue or other supported catalogs) that manages table metadata (Answer E).

Additionally, Snowflake must know the location of the Iceberg metadata files (the Iceberg metadata JSON), which is provided via the metadata file path parameter (Answer F). This allows Snowflake to read schema and snapshot information maintained externally.

An external stage is not required for Iceberg tables, as Snowflake accesses the data directly through the external volume. A storage integration is used for stages, not for Iceberg tables. The data file path is derived from metadata and does not need to be specified explicitly. This question tests SnowPro Architect understanding of modern open table formats and Snowflake’s Iceberg integration model.

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QUESTION NO: 58 [Security and Access Management]

A company stores customer data in Snowflake and must protect Personally Identifiable Information (PII) to meet strict regulatory requirements.

What should an Architect do?

A. Use row-level security to mask PII data.

B. Use tag-based masking policies for columns containing PII.

C. Create secure views for PII data and grant access as needed.

D. Separate PII into different tables and grant access as needed.

Answer: B

Tag-based masking policies provide a scalable and centralized way to protect PII across many tables and schemas (Answer B). By tagging columns that contain PII and associating masking policies with those tags, Snowflake automatically enforces masking rules wherever the tagged columns appear. This approach reduces administrative overhead and ensures consistent enforcement as schemas evolve.

Row access policies control row visibility, not column masking. Secure views and table separation can protect data but introduce significant maintenance complexity and do not scale well across large environments. Snowflake best practices—and the SnowPro Architect exam—emphasize tag-based governance for sensitive data.

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QUESTION NO: 59 [Security and Access Management]

An Architect created a data share and wants to verify that only specific records in secure views are visible to consumers.

What is the recommended validation method?

A. Create reader accounts and log in as consumers.

B. Create a row access policy and assign it to the share.

C. Set the SIMULATED_DATA_SHARING_CONSUMER session parameter.

D. Alter the share to impersonate a consumer account.

Answer: C

Snowflake provides the SIMULATED_DATA_SHARING_CONSUMER session parameter to allow providers to test how shared data appears to specific consumer accounts without logging in as those consumers (Answer C). This feature enables secure, efficient validation of row-level and column-level filtering logic implemented through secure views.

Creating reader accounts is unnecessary and operationally heavy. Row access policies are part of access control design, not validation. Altering a share does not provide impersonation capabilities. This question tests SnowPro Architect familiarity with governance validation tools in Secure Data Sharing scenarios.

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QUESTION NO: 60 [Architecting Snowflake Solutions]

Which requirements indicate that a multi-account Snowflake strategy should be used? (Select TWO).

A. A requirement to use different Snowflake editions.

B. A requirement for easy object promotion using zero-copy cloning.

C. A requirement to use Snowflake in a single cloud or region.

D. A requirement to minimize complexity of changing database names across environments.

E. A requirement to use RBAC to govern DevOps processes across environments.

Answer: A, B

A multi-account Snowflake strategy is appropriate when environments have fundamentally different requirements. Using different Snowflake editions (for example, Business Critical for production and Enterprise for non-production) requires separate accounts because edition is an account-level property (Answer A).

Zero-copy cloning is frequently used for fast environment refresh and object promotion, but cloning only works within a single account. To promote data between environments cleanly, many organizations use separate accounts combined with replication or sharing strategies, making multi-account design relevant when environment isolation and promotion workflows are required (Answer B).

Single-region usage, minimizing database name changes, and RBAC governance can all be handled within a single account. This question reinforces SnowPro Architect principles around environment isolation, governance, and account-level design decisions.

In Snowflake, a stream records data manipulation language (DML) changes to its base table since the stream was created or last consumed. STREAM_1 will show all changes including the TRUNCATE operation, while STREAM_2, being APPEND_ONLY, will not show deletions like TRUNCATE. Therefore, STREAM_1 will count the three inserts, the TRUNCATE (counted as a single operation), and the subsequent two inserts before the delete, totaling 4. STREAM_2 will only count the three initial inserts and the two after the TRUNCATE, totaling 3, as it does not count the TRUNCATE or the delete operation.

The ON_ERROR clause is an optional parameter for the COPY INTO command that specifies the behavior of the command when it encounters errors in the files. The ON_ERROR clause can have one of the following values1:

CONTINUE: This value instructs the command to continue loading the file and return an error message for a maximum of one error encountered per data file. The difference between the ROWS_PARSED and ROWS_LOADED column values represents the number of rows that include detected errors. To view all errors in the data files, use the VALIDATION_MODE parameter or query the VALIDATE function1.

SKIP_FILE: This value instructs the command to skip the file when it encounters a data error on any of the records in the file. The command moves on to the next file in the stage and continues loading. The skipped file is not loaded and no error message is returned for the file1.

ABORT_STATEMENT: This value instructs the command to stop loading data when the first error is encountered. The command returns an error message for the file and aborts the load operation. This is the default value for the ON_ERROR clause1.

Therefore, options A, B, and C are correct.

 COPY INTO