Snowflake ARA-C01 Online Practice Questions and Exam Preparation

Snowflake ARA-C01 Online Questions & Answers

• Question 51:

  A company is storing large numbers of small JSON files (ranging from 1-4 bytes) that are received from IoT devices and sent to a cloud provider. In any given hour, 100,000 files are added to the cloud provider.

  What is the MOST cost-effective way to bring this data into a Snowflake table?

  A. An external table
  B. A pipe
  C. A stream
  D. A copy command at regular intervals
  B. A pipe

  ---

  Explanation/Reference:

  A pipe is a Snowflake object that continuously loads data from files in a stage (internal or external) into a table. A pipe can be configured to use auto-ingest, which means that Snowflake automatically detects new or modified files in the stage and loads them into the table without any manual intervention1. A pipe is the most cost-effective way to bring large numbers of small JSON files into a Snowflake table, because it minimizes the number of COPY commands executed and the number of micro-partitions created. A pipe can use file aggregation, which means that it can combine multiple small files into a single larger file before loading them into the table. This reduces the load time and the storage cost of the data2. An external table is a Snowflake object that references data files stored in an external location, such as Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage. An external table does not store the data in Snowflake, but only provides a view of the data for querying. An external table is not a cost-effective way to bring data into a Snowflake table, because it does not support file aggregation, and it requires additional network bandwidth and compute resources to

  query the external data3.

  A stream is a Snowflake object that records the history of changes (inserts, updates, and deletes) made to a table. A stream can be used to consume the changes from a table and apply them to another table or a task. A stream is not a way

  to bring data into a Snowflake table, but a way to process the data after it is loaded into a table4.

  A copy command is a Snowflake command that loads data from files in a stage into a table. A copy command can be executed manually or scheduled using a task. A copy command is not a cost-effective way to bring large numbers of small

  JSON files into a Snowflake table, because it does not support file aggregation, and it may create many micro-partitions that increase the storage cost of the data5.

  References: : Pipes : Loading Data Using Snowpipe : External Tables : Streams : COPY INTO

  Question 52:

  What built-in Snowflake features make use of the change tracking metadata for a table? (Choose two.)

  A. The MERGE command
  B. The UPSERT command
  C. The CHANGES clause
  D. A STREAM object
  E. TheCHANGE_DATA_CAPTURE command

  C. The CHANGES clause
  D. A STREAM object

  ---

  Explanation/Reference:

  The built-in Snowflake features that make use of the change tracking metadata for a table are the CHANGES clause and a STREAM object. The CHANGES clause enables querying the change tracking metadata for a table or view within a specified interval of time without having to create a stream with an explicit transactional offset1. A STREAM object records data manipulation language (DML) changes made to tables, including inserts, updates, and deletes, as well as metadata about each change, so that actions can be taken using the changed data. This process is referred to as change data capture (CDC)2. The other options are incorrect because they do not make use of the change tracking metadata for a table. The MERGE command performs insert, update, or delete operations on a target table based on the results of a join with a source table3. The UPSERT command is not a valid Snowflake command. The CHANGE_DATA_CAPTURE command is not a valid Snowflake command. References: CHANGES | Snowflake Documentation, Change Tracking Using Table Streams | Snowflake Documentation, MERGE | Snowflake Documentation

  Question 53:

  Which statements describe characteristics of the use of materialized views in Snowflake? (Choose two.)

  A. They can include ORDER BY clauses.
  B. They cannot include nested subqueries.
  C. They can include context functions, such as CURRENT_TIME().
  D. They can support MIN and MAX aggregates.
  E. They can support inner joins, but not outer joins.

  B. They cannot include nested subqueries.
  D. They can support MIN and MAX aggregates.

  ---

  Explanation/Reference:

  According to the Snowflake documentation, materialized views have some limitations on the query specification that defines them. One of these limitations is that they cannot include nested subqueries, such as subqueries in the FROM clause or scalar subqueries in the SELECT list. Another limitation is that they cannot include ORDER BY clauses, context functions (such as CURRENT_TIME()), or outer joins. However, materialized views can support MIN and MAX aggregates, as well as other aggregate functions, such as SUM, COUNT, and AVG. References: Limitations on Creating Materialized Views | Snowflake Documentation Working with Materialized Views | Snowflake Documentation

  Question 54:

  A healthcare company is deploying a Snowflake account that may include Personal Health Information (PHI). The company must ensure compliance with all relevant privacy standards.

  Which best practice recommendations will meet data protection and compliance requirements? (Choose three.)

  A. Use, at minimum, the Business Critical edition of Snowflake.
  B. Create Dynamic Data Masking policies and apply them to columns that contain PHI.
  C. Use the Internal Tokenization feature to obfuscate sensitive data.
  D. Use the External Tokenization feature to obfuscate sensitive data.
  E. Rewrite SQL queries to eliminate projections of PHI data based on current_role().
  F. Avoid sharing data with partner organizations.

  A. Use, at minimum, the Business Critical edition of Snowflake.
  B. Create Dynamic Data Masking policies and apply them to columns that contain PHI.
  D. Use the External Tokenization feature to obfuscate sensitive data.

  ---

  Explanation/Reference:

  A healthcare company that handles PHI data must ensure compliance with relevant privacy standards, such as HIPAA, HITRUST, and GDPR. Snowflake provides several features and best practices to help customers meet their data

  protection and compliance requirements1.

  One best practice recommendation is to use, at minimum, the Business Critical edition of Snowflake. This edition provides the highest level of data protection and security, including end-to-end encryption with customer-managed keys,

  enhanced object-level security, and HIPAA and HITRUST compliance2. Therefore, option A is correct.

  Another best practice recommendation is to create Dynamic Data Masking policies and apply them to columns that contain PHI. Dynamic Data Masking is a feature that allows masking or redacting sensitive data based on the current user's

  role. This way, only authorized users can view the unmasked data, while others will see masked values, such as NULL, asterisks, or random characters3.

  Therefore, option B is correct.

  A third best practice recommendation is to use the External Tokenization feature to obfuscate sensitive data. External Tokenization is a feature that allows replacing sensitive data with tokens that are generated and stored by an external

  service, such as Protegrity. This way, the original data is never stored or processed by Snowflake, and only authorized users can access the tokenized data through the external service4. Therefore, option D is correct. Option C is incorrect,

  because the Internal Tokenization feature is not available in Snowflake. Snowflake does not provide any native tokenization functionality, but only supports integration with external tokenization services4. Option E is incorrect, because

  rewriting SQL queries to eliminate projections of PHI data based on current_role() is not a best practice. This approach is error- prone, inefficient, and hard to maintain. A better alternative is to use Dynamic Data Masking policies, which can

  automatically mask data based on the user's role without modifying the queries3.

  Option F is incorrect, because avoiding sharing data with partner organizations is not a best practice. Snowflake enables secure and governed data sharing with internal and external consumers, such as business units, customers, or

  partners. Data sharing does not involve copying or moving data, but only granting access privileges to the shared objects. Data sharing can also leverage Dynamic Data Masking and External Tokenization features to protect sensitive data5.

  References: : Snowflake's Security and Compliance Reports : Snowflake Editions : Dynamic Data Masking : External Tokenization : Secure Data Sharing

  Question 55:

  What Snowflake features should be leveraged when modeling using Data Vault? (Choose two.)

  A. Snowflake's support of multi-table inserts into the data model's Data Vault tables
  B. Data needs to be pre-partitioned to obtain a superior data access performance
  C. Scaling up the virtual warehouses will support parallel processing of new source loads
  D. Snowflake's ability to hash keys so that hash key joins can run faster than integer joins

  A. Snowflake's support of multi-table inserts into the data model's Data Vault tables
  C. Scaling up the virtual warehouses will support parallel processing of new source loads

  ---

  Explanation/Reference:

  These two features are relevant for modeling using Data Vault on Snowflake. Data Vault is a data modeling approach that organizes data into hubs, links, and satellites. Data Vault is designed to enable high scalability, flexibility, and performance for data integration and analytics. Snowflake is a cloud data platform that supports various data modeling techniques, including Data Vault. Snowflake provides some features that can enhance the Data Vault modeling, such as: Snowflake's support of multi-table inserts into the data model's Data Vault tables. Multi-table inserts (MTI) are a feature that allows inserting data from a single query into multiple tables in a single DML statement. MTI can improve the performance and efficiency of loading datainto Data Vault tables, especially for real-time or near-real-time data integration. MTI can also reduce the complexity and maintenance of the loading code, as well as the data duplication and latency12. Scaling up the virtual warehouses will support parallel processing of new source loads. Virtual warehouses are a feature that allows provisioning compute resources on demand for data processing. Virtual warehouses can be scaled up or down by changing the size of the warehouse, which determines the number of servers in the warehouse. Scaling up the virtual warehouses can improve the performance and concurrency of processing new source loads into Data Vault tables, especially for large or complex data sets. Scaling up the virtual warehouses can also leverage the parallelism and distribution of Snowflake's architecture, which can optimize the data loading and querying34. References: Snowflake Documentation: Multi-table Inserts Snowflake Blog: Tips for Optimizing the Data Vault Architecture on Snowflake Snowflake Documentation: Virtual Warehouses Snowflake Blog: Building a Real-Time Data Vault in Snowflake

  Question 56:

  The Data Engineering team at a large manufacturing company needs to engineer data coming from many sources to support a wide variety of use cases and data consumer requirements which include:

  1) Finance and Vendor Management team members who require reporting and visualization

  2) Data Science team members who require access to raw data for ML model development

  3) Sales team members who require engineered and protected data for data monetization

  What Snowflake data modeling approaches will meet these requirements? (Choose two.)

  A. Consolidate data in the company's data lake and use EXTERNAL TABLES.
  B. Create a raw database for landing and persisting raw data entering the data pipelines.
  C. Create a set of profile-specific databases that aligns data with usage patterns.
  D. Create a single star schema in a single database to support all consumers' requirements.
  E. Create a Data Vault as the sole data pipeline endpoint and have all consumers directly access the Vault.

  B. Create a raw database for landing and persisting raw data entering the data pipelines.
  C. Create a set of profile-specific databases that aligns data with usage patterns.

  ---

  Explanation/Reference:

  These two approaches are recommended by Snowflake for data modeling in a data lake scenario. Creating a raw database allows the data engineering team to ingest data from various sources without any transformation or cleansing, preserving the original data quality and format. This enables the data science team to access the raw data for ML model development. Creating a set of profile-specific databases allows the data engineering team to apply different transformations and optimizations for different use cases and data consumer requirements. For example, the finance and vendor management team can access a dimensional database that supports reporting and visualization, while the sales team can access a secure database that supports data monetization. References: Snowflake Data Lake Architecture | Snowflake Documentation Snowflake Data Lake Best Practices | Snowflake Documentation

  Question 57:

  An Architect runs the following SQL query:

  

  How can this query be interpreted?

  A. FILEROWS is a stage. FILE_ROW_NUMBER is line number in file.
  B. FILEROWS is the table. FILE_ROW_NUMBER is the line number in the table.
  C. FILEROWS is a file. FILE_ROW_NUMBER is the file format location.
  D. FILERONS is the file format location. FILE_ROW_NUMBER is a stage.

  A. FILEROWS is a stage. FILE_ROW_NUMBER is line number in file.

  ---

  Explanation/Reference:

  A stage is a named location in Snowflake that can store files for data loading and unloading. A stage can be internal or external, depending on where the files are stored.

  The query in the question uses the LIST function to list the files in a stage named FILEROWS. The function returns a table with various columns, including FILE_ROW_NUMBER, which is the line number of the file in the stage. Therefore, the

  query can be interpreted as listing the files in a stage named FILEROWS and showing the line number of each file in the stage.

  References:

  : Stages

  : LIST Function

  Question 58:

  What is a valid object hierarchy when building a Snowflake environment?

  A. Account --> Database --> Schema --> Warehouse
  B. Organization --> Account --> Database --> Schema --> Stage
  C. Account --> Schema > Table --> Stage
  D. Organization --> Account --> Stage --> Table --> View

  B. Organization --> Account --> Database --> Schema --> Stage

  ---

  Explanation/Reference:

  This is the valid object hierarchy when building a Snowflake environment, according to the Snowflake documentation and the web search results. Snowflake is a cloud data platform that supports various types of objects, such as

  databases, schemas, tables, views, stages, warehouses, and more. These objects are organized in a hierarchical structure, as follows:

  Organization: An organization is the top-level entity that represents a group of Snowflake accounts that are related by business needs or ownership. An organization can have one or more accounts, and can enable features such as cross-

  account data sharing, billing and usage reporting, and single sign-on across accounts12.

  Account: An account is the primary entity that represents a Snowflake customer. An account can have one or more databases, schemas, stages, warehouses, and other objects. An account can also have one or more users, roles, and

  security integrations. An account is associated with a specific cloud platform, region, and Snowflake edition34.

  Database: A database is a logical grouping of schemas. A database can have one or more schemas, and can store structured, semi-structured, or unstructured data. A database can also have properties such as retention time, encryption,

  and ownership56.

  Schema: A schema is a logical grouping of tables, views, stages, and other objects. A schema can have one or more objects, and can define the namespace and access control for the objects. A schema can also have properties such as

  ownership and default warehouse .

  Stage: A stage is a named location that references the files in external or internal storage. A stage can be used to load data into Snowflake tables using the COPY INTO command, or to unload data from Snowflake tables using the COPY

  INTO LOCATION command. A stage can be created at the account, database, or schema level, and can have properties such as file format, encryption, and credentials .

  The other options listed are not valid object hierarchies, because they either omit or misplace some objects in the structure. For example, option A omits the organization level and places the warehouse under the schema level, which is

  incorrect. Option C omits the organization, account, and stage levels, and places the table under the schema level, which is incorrect. Option D omits the database level and places the stage and table under the account level, which is

  incorrect.

  References:

  Snowflake Documentation: Organizations

  Snowflake Blog: Introducing Organizations in Snowflake Snowflake Documentation: Accounts

  Snowflake Blog: Understanding Snowflake Account Structures Snowflake Documentation: Databases

  Snowflake Blog: How to Create a Database in Snowflake [Snowflake Documentation: Schemas]

  [Snowflake Blog: How to Create a Schema in Snowflake] [Snowflake Documentation: Stages]

  [Snowflake Blog: How to Use Stages in Snowflake]

  Question 59:

  A DevOps team has a requirement for recovery of staging tables used in a complex set of data pipelines. The staging tables are all located in the same staging schema. One of the requirements is to have online recovery of data on a rolling 7day basis.

  After setting up the DATA_RETENTION_TIME_IN_DAYS at the database level, certain tables remain unrecoverable past 1 day.

  What would cause this to occur? (Choose two.)

  A. The staging schema has not been setup for MANAGED ACCESS.
  B. The DATA_RETENTION_TIME_IN_DAYS for the staging schema has been set to 1 day.
  C. The tables exceed the 1 TB limit for data recovery.
  D. The staging tables are of the TRANSIENT type.
  E. The DevOps role should be granted ALLOW_RECOVERY privilege on the staging schema.

  B. The DATA_RETENTION_TIME_IN_DAYS for the staging schema has been set to 1 day.
  D. The staging tables are of the TRANSIENT type.

  ---

  Explanation/Reference:

  The DATA_RETENTION_TIME_IN_DAYS parameter controls the Time Travel retention period for an object (database, schema, or table) in Snowflake. This parameter specifies the number of days for which historical data is preserved and can be accessed using Time Travel operations (SELECT, CREATE ... CLONE, UNDROP)1. The requirement for recovery of staging tables on a rolling 7-day basis means that the DATA_RETENTION_TIME_IN_DAYS parameter should be set to 7 at the database level. However, this parameter can be overridden at the lower levels (schema or table) if they have a different value1. Therefore, one possible cause for certain tables to remain unrecoverable past 1 day is that the DATA_RETENTION_TIME_IN_DAYS for the staging schema has been set to 1 day. This would override the database level setting and limit the Time Travel retention period for all the tables in the schema to 1 day. To fix this, the parameter should be unset or set to 7 at the schema level1. Therefore, option B is correct. Another possible cause for certain tables to remain unrecoverable past 1 day is that the staging tables are of the TRANSIENT type. Transient tables are tables that do not have a Fail-safe period and can have a Time Travel retention period of either 0 or 1 day. Transient tables are suitable for temporary or intermediate data that can be easily reproduced or replicated2. To fix this, the tables should be created as permanent tables, which can have a Time Travel retention period of up to 90 days1. Therefore, option D is correct. Option A is incorrect because the MANAGED ACCESS feature is not related to the data recovery requirement. MANAGED ACCESS is a feature that allows granting access privileges to objects without explicitly granting the privileges to roles. It does not affect the Time Travel retention period or the data availability3. Option C is incorrect because there is no 1 TB limit for data recovery in Snowflake. The data storage size does not affect the Time Travel retention period or the data availability4. Option E is incorrect because there is no ALLOW_RECOVERY privilege in Snowflake. The privilege required to perform Time Travel operations is SELECT, which allows querying historical data in tables5. References: : Understanding and Using Time Travel : Transient Tables : Managed Access : Understanding Storage Cost : Table Privileges

  Question 60:

  Files arrive in an external stage every 10 seconds from a proprietary system. The files range in size from 500 K to 3 MB. The data must be accessible by dashboards as soon as it arrives.

  How can a Snowflake Architect meet this requirement with the LEAST amount of coding? (Choose two.)

  A. Use Snowpipe with auto-ingest.
  B. Use a COPY command with a task.
  C. Use a materialized view on an external table.
  D. Use the COPY INTO command.
  E. Use a combination of a task and a stream.

  A. Use Snowpipe with auto-ingest.
  C. Use a materialized view on an external table.

  ---

  Explanation/Reference:

  These two options are the best ways to meet the requirement of loading data from an external stage and making it accessible by dashboards with the least amount of coding. Snowpipe with auto-ingest is a feature that enables continuous and automated data loading from an external stage into a Snowflake table. Snowpipe uses event notifications from the cloud storage service to detect new or modified files in the stage and triggers a COPY INTO command to load the data into the table. Snowpipe is efficient, scalable, and serverless, meaning it does not require any infrastructure or maintenance from the user. Snowpipe also supports loading data from files of any size, as long as they are in a supported format1. A materialized view on an external table is a feature that enables creating a pre- computed result set from an external table and storing it in Snowflake. A materialized view can improve the performance and efficiency of querying data from an external table, especially for complex queries or dashboards. A materialized view can also support aggregations, joins, and filters on the external table data. A materialized view on an external table is automatically refreshed when the underlying data in the external stage changes, as long as the AUTO_REFRESH parameter is set to true2. References: Snowpipe Overview | Snowflake Documentation Materialized Views on External Tables | Snowflake Documentation

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