Snowflake ARA-C01 Exam Practice Test Instant Access

Question 1

The data share exists between a data provider account and a data consumer account. Five tables from the provider account are being shared with the consumer account. The consumer role has been granted the imported privileges privilege.

What will happen to the consumer account if a new table (table_6) is added to the provider schema?

AThe consumer role will automatically see the new table and no additional grants are needed.

BThe consumer role will see the table only after this grant is given on the consumer side:
grant imported privileges on database PSHARE_EDW_4TEST_DB to DEV_ROLE;

CThe consumer role will see the table only after this grant is given on the provider side:
use role accountadmin;
Grant select on table EDW.ACCOUNTING.Table_6 to share PSHARE_EDW_4TEST;

DThe consumer role will see the table only after this grant is given on the provider side:
use role accountadmin;
grant usage on database EDW to share PSHARE_EDW_4TEST ;
grant usage on schema EDW.ACCOUNTING to share PSHARE_EDW_4TEST ;
Grant select on table EDW.ACCOUNTING.Table_6 to database PSHARE_EDW_4TEST_DB ;

Answer : D

When a new table (table_6) is added to a schema in the provider's account that is part of a data share, the consumer will not automatically see the new table. The consumer will only be able to access the new table once the appropriate privileges are granted by the provider. The correct process, as outlined in option D, involves using the provider's ACCOUNTADMIN role to grant USAGE privileges on the database and schema, followed by SELECT privileges on the new table, specifically to the share that includes the consumer's database. This ensures that the consumer account can access the new table under the established data sharing setup. Reference:

Snowflake Documentation on Managing Access Control

Snowflake Documentation on Data Sharing

Question 2

An Architect needs to design a data unloading strategy for Snowflake, that will be used with the COPY INTO command.

Which configuration is valid?

ALocation of files: Snowflake internal location
. File formats: CSV, XML
. File encoding: UTF-8
. Encryption: 128-bit

BLocation of files: Amazon S3
. File formats: CSV, JSON
. File encoding: Latin-1 (ISO-8859)
. Encryption: 128-bit

CLocation of files: Google Cloud Storage
. File formats: Parquet
. File encoding: UTF-8
* Compression: gzip

DLocation of files: Azure ADLS
. File formats: JSON, XML, Avro, Parquet, ORC
. Compression: bzip2
. Encryption: User-supplied key

Answer : C

For the configuration of data unloading in Snowflake, the valid option among the provided choices is 'C.' This is because Snowflake supports unloading data into Google Cloud Storage using the COPY INTO <location> command with specific configurations. The configurations listed in option C, such as Parquet file format with UTF-8 encoding and gzip compression, are all supported by Snowflake. Notably, Parquet is a columnar storage file format, which is optimal for high-performance data processing tasks in Snowflake. The UTF-8 file encoding and gzip compression are both standard and widely used settings that are compatible with Snowflake's capabilities for data unloading to cloud storage platforms. Reference:

Snowflake Documentation on COPY INTO command

Snowflake Documentation on Supported File Formats

Snowflake Documentation on Compression and Encoding Options

Question 3

A company is designing a process for importing a large amount of loT JSON data from cloud storage into Snowflake. New sets of loT data get generated and uploaded approximately every 5 minutes.

Once the loT data is in Snowflake, the company needs up-to-date information from an external vendor to join to the data. This data is then presented to users through a dashboard that shows different levels of aggregation. The external vendor is a Snowflake customer.

What solution will MINIMIZE complexity and MAXIMIZE performance?

A1. Create an external table over the JSON data in cloud storage.
2. Create a task that runs every 5 minutes to run a transformation procedure on new data, based on a saved timestamp.
3. Ask the vendor to expose an API so an external function can be used to generate a call to join the data back to the loT data in the transformation procedure.
4. Give the transformed table access to the dashboard tool.
5. Perform the aggregations on the dashboard tool.

B1. Create an external table over the JSON data in cloud storage.
2. Create a task that runs every 5 minutes to run a transformation procedure on new data based on a saved timestamp.
3. Ask the vendor to create a data share with the required data that can be imported into the company's Snowflake account.
4. Join the vendor's data back to the loT data using a transformation procedure.
5. Create views over the larger dataset to perform the aggregations required by the dashboard.
6. Give the views access to the dashboard tool.

C1. Create a Snowpipe to bring the JSON data into Snowflake.
2. Use streams and tasks to trigger a transformation procedure when new JSON data arrives.
3. Ask the vendor to expose an API so an external function call can be made to join the vendor's data back to the loT data in a transformation procedure.
4. Create materialized views over the larger dataset to perform the aggregations required by the dashboard.
5. Give the materialized views access to the dashboard tool.

D1. Create a Snowpipe to bring the JSON data into Snowflake.
2. Use streams and tasks to trigger a transformation procedure when new JSON data arrives.
3. Ask the vendor to create a data share with the required data that is then imported into the Snowflake account.
4. Join the vendor's data back to the loT data in a transformation procedure
5. Create materialized views over the larger dataset to perform the aggregations required by the dashboard.
6. Give the materialized views access to the dashboard tool.

Answer : D

Using Snowpipe for continuous, automated data ingestion minimizes the need for manual intervention and ensures that data is available in Snowflake promptly after it is generated. Leveraging Snowflake's data sharing capabilities allows for efficient and secure access to the vendor's data without the need for complex API integrations. Materialized views provide pre-aggregated data for fast access, which is ideal for dashboards that require high performance1234.

Reference =

* Snowflake Documentation on Snowpipe4

* Snowflake Documentation on Secure Data Sharing2

* Best Practices for Data Ingestion with Snowflake1

Question 4

A retailer's enterprise data organization is exploring the use of Data Vault 2.0 to model its data lake solution. A Snowflake Architect has been asked to provide recommendations for using Data Vault 2.0 on Snowflake.

What should the Architect tell the data organization? (Select TWO).

AChange data capture can be performed using the Data Vault 2.0 HASH_DIFF concept.

BChange data capture can be performed using the Data Vault 2.0 HASH_DELTA concept.

CUsing the multi-table insert feature in Snowflake, multiple Point-in-Time (PIT) tables can be loaded in parallel from a single join query from the data vault.

DUsing the multi-table insert feature, multiple Point-in-Time (PIT) tables can be loaded sequentially from a single join query from the data vault.

EThere are performance challenges when using Snowflake to load multiple Point-in-Time (PIT) tables in parallel from a single join query from the data vault.

Answer : A, C

Data Vault 2.0 on Snowflake supports the HASH_DIFF concept for change data capture, which is a method to detect changes in the data by comparing the hash values of the records. Additionally, Snowflake's multi-table insert feature allows for the loading of multiple PIT tables in parallel from a single join query, which can significantly streamline the data loading process and improve performance1.

Reference =

* Snowflake's documentation on multi-table inserts1

* Blog post on optimizing Data Vault architecture on Snowflake2

Question 5

What is the MOST efficient way to design an environment where data retention is not considered critical, and customization needs are to be kept to a minimum?

AUse a transient database.

BUse a transient schema.

CUse a transient table.

DUse a temporary table.

Answer : A

Transient databases in Snowflake are designed for situations where data retention is not critical, and they do not have the fail-safe period that regular databases have. This means that data in a transient database is not recoverable after the Time Travel retention period. Using a transient database is efficient because it minimizes storage costs while still providing most functionalities of a standard database without the overhead of data protection features that are not needed when data retention is not a concern.

Question 6

A company is trying to Ingest 10 TB of CSV data into a Snowflake table using Snowpipe as part of Its migration from a legacy database platform. The records need to be ingested in the MOST performant and cost-effective way.

How can these requirements be met?

AUse ON_ERROR = continue in the copy into command.

BUse purge = TRUE in the copy into command.

CUse FURGE = FALSE in the copy into command.

DUse on error = SKIP_FILE in the copy into command.

Answer : D

For ingesting a large volume of CSV data into Snowflake using Snowpipe, especially for a substantial amount like 10 TB, the on error = SKIP_FILE option in the COPY INTO command can be highly effective. This approach allows Snowpipe to skip over files that cause errors during the ingestion process, thereby not halting or significantly slowing down the overall data load. It helps in maintaining performance and cost-effectiveness by avoiding the reprocessing of problematic files and continuing with the ingestion of other data.

Question 7

Consider the following scenario where a masking policy is applied on the CREDICARDND column of the CREDITCARDINFO table. The masking policy definition Is as follows:

Sample data for the CREDITCARDINFO table is as follows:

NAME EXPIRYDATE CREDITCARDNO

JOHN DOE 2022-07-23 4321 5678 9012 1234

if the Snowflake system rotes have not been granted any additional roles, what will be the result?

AThe sysadmin can see the CREDICARDND column data in clear text.

BThe owner of the table will see the CREDICARDND column data in clear text.

CAnyone with the Pl_ANALYTICS role will see the last 4 characters of the CREDICARDND column data in dear text.

DAnyone with the Pl_ANALYTICS role will see the CREDICARDND column as*** 'MASKED* **'.

Answer : D

The masking policy defined in the image indicates that if a user has the PI_ANALYTICS role, they will be able to see the last 4 characters of the CREDITCARDNO column data in clear text. Otherwise, they will see 'MASKED'. Since Snowflake system roles have not been granted any additional roles, they won't have the PI_ANALYTICS role and therefore cannot view the last 4 characters of credit card numbers.

To apply a masking policy on a column in Snowflake, you need to use the ALTER TABLE ... ALTER COLUMN command or the ALTER VIEW command and specify the policy name. For example, to apply the creditcardno_mask policy on the CREDITCARDNO column of the CREDITCARDINFO table, you can use the following command:

ALTER TABLE CREDITCARDINFO ALTER COLUMN CREDITCARDNO SET MASKING POLICY creditcardno_mask;

For more information on how to create and use masking policies in Snowflake, you can refer to the following resources:

CREATE MASKING POLICY: This document explains the syntax and usage of the CREATE MASKING POLICY command, which allows you to create a new masking policy or replace an existing one.

Using Dynamic Data Masking: This guide provides instructions on how to configure and use dynamic data masking in Snowflake, which is a feature that allows you to mask sensitive data based on the execution context of the user.

ALTER MASKING POLICY: This document explains the syntax and usage of the ALTER MASKING POLICY command, which allows you to modify the properties of an existing masking policy.

Snowflake ARA-C01 SnowPro Advanced: Architect Certification Exam Practice Test