Snowflake ARA-R01 Online Access

A.

Configure the client application to call the Snowpipe REST endpoint when new files have arrived in Amazon S3 storage.

B.

Configure the client application to call the Snowpipe REST endpoint when new files have arrived in Amazon S3 Glacier storage.

C.

Create an AWS Lambda function to call the Snowpipe REST endpoint when new files have arrived in Amazon S3 storage.

D.

Configure AWS Simple Notification Service (SNS) to notify Snowpipe when new files have arrived in Amazon S3 storage.

E.

Configure the client application to issue a COPY INTO

command to Snowflake when new files have arrived in Amazon S3 Glacier storage.

Answer:

A, C

Explanation:

Snowpipe is a feature that enables continuous, near-real-time data ingestion from external sources into Snowflake tables. Snowpipe can ingest files from Amazon S3, Google Cloud Storage, or Azure Blob Storage into Snowflake tables on any cloud platform. Snowpipe can be triggered in two ways: by using the Snowpipe REST API or by using cloud notifications2

To ingest files from the company’s AWS storage accounts into the company’s Snowflake GCP account, the Architect can use either of these methods:

Configure the client application to call the Snowpipe REST endpoint when new files have arrived in Amazon S3 storage. This method requires the client application to monitor the S3 buckets for new files and send a request to the Snowpipe REST API with the list of files to ingest. The client application must also handle authentication, error handling, and retry logic3
Create an AWS Lambda function to call the Snowpipe REST endpoint when new files have arrived in Amazon S3 storage. This method leverages the AWS Lambda service to execute a function that calls the Snowpipe REST API whenever an S3 event notification is received. The AWS Lambda function must be configured with the appropriate permissions, triggers, and code to invoke the Snowpipe REST API4

The other options are not valid methods for triggering Snowpipe:

Configure the client application to call the Snowpipe REST endpoint when new files have arrived in Amazon S3 Glacier storage. This option is not feasible because Snowpipe does not support ingesting files from Amazon S3 Glacier storage, which is a long-term archival storage service. Snowpipe only supports ingesting files from Amazon S3 standard storage classes5
Configure AWS Simple Notification Service (SNS) to notify Snowpipe when new files have arrived in Amazon S3 storage. This option is not applicable because Snowpipe does not support cloud notifications from AWS SNS. Snowpipe only supports cloud notifications from AWS SQS, Google Cloud Pub/Sub, or Azure Event Grid6
Configure the client application to issue a COPY INTO

command to Snowflake when new files have arrived in Amazon S3 Glacier storage. This option is not relevant because it does not use Snowpipe, but rather the standard COPY command, which is a batch loading method. Moreover, the COPY command also does not support ingesting files from Amazon S3 Glacier storage7 References:

1: SnowPro Advanced: Architect | Study Guide 8

2: Snowflake Documentation | Snowpipe Overview 9

3: Snowflake Documentation | Using the Snowpipe REST API 10

4: Snowflake Documentation | Loading Data Using Snowpipe and AWS Lambda 11

5: Snowflake Documentation | Supported File Formats and Compression for Staged Data Files 12

6: Snowflake Documentation | Using Cloud Notifications to Trigger Snowpipe 13

7: Snowflake Documentation | Loading Data Using COPY into a Table

: SnowPro Advanced: Architect | Study Guide

: Snowpipe Overview

: Using the Snowpipe REST API

: Loading Data Using Snowpipe and AWS Lambda

: Supported File Formats and Compression for Staged Data Files

: Using Cloud Notifications to Trigger Snowpipe

: Loading Data Using COPY into a Table

Question 33

The following table exists in the production database:

A regulatory requirement states that the company must mask the username for events that are older than six months based on the current date when the data is queried.

How can the requirement be met without duplicating the event data and making sure it is applied when creating views using the table or cloning the table?

Options:

A.

Use a masking policy on the username column using a entitlement table with valid dates.

B.

Use a row level policy on the user_events table using a entitlement table with valid dates.

C.

Use a masking policy on the username column with event_timestamp as a conditional column.

D.

Use a secure view on the user_events table using a case statement on the username column.

Answer:

C

Explanation:

A masking policy is a feature of Snowflake that allows masking sensitive data in query results based on the role of the user and the condition of the data. A masking policy can be applied to a column in a table or a view, and it can use another column in the same table or view as a conditional column. A conditional column is a column that determines whether the masking policy is applied or not based on its value1.

In this case, the requirement can be met by using a masking policy on the username column with event_timestamp as a conditional column. The masking policy can use a function that masks the username if the event_timestamp is older than six months based on the current date, and returns the original username otherwise. The masking policy can be applied to the user_events table, and it will also be applied when creating views using the table or cloning the table2.

The other options are not correct because:

A. Using a masking policy on the username column using an entitlement table with valid dates would require creating another table that stores the valid dates for each username, and joining it with the user_events table in the masking policy function. This would add complexity and overhead to the masking policy, and it would not use the event_timestamp column as the condition for masking.
B. Using a row level policy on the user_events table using an entitlement table with valid dates would require creating another table that stores the valid dates for each username, and joining it with the user_events table in the row access policy function. This would filter out the rows that have event_timestamp older than six months based on the valid dates, instead of masking the username column. This would not meet the requirement of masking the username, and it would also reduce the visibility of the event data.
D. Using a secure view on the user_events table using a case statement on the username column would require creating a view that uses a case expression to mask the username column based on the event_timestamp column. This would meet the requirement of masking the username, but it would not be applied when cloning the table. A secure view is a view that prevents the underlying data from being exposed by queries on the view. However, a secure view does not prevent the underlying data from being exposed by cloning the table3.

References:

1: Masking Policies | Snowflake Documentation
2: Using Conditional Columns in Masking Policies | Snowflake Documentation
3: Secure Views | Snowflake Documentation

Question 34

Two queries are run on the customer_address table:

create or replace TABLE CUSTOMER_ADDRESS ( CA_ADDRESS_SK NUMBER(38,0), CA_ADDRESS_ID VARCHAR(16), CA_STREET_NUMBER VARCHAR(IO) CA_STREET_NAME VARCHAR(60), CA_STREET_TYPE VARCHAR(15), CA_SUITE_NUMBER VARCHAR(10), CA_CITY VARCHAR(60), CA_COUNTY

VARCHAR(30), CA_STATE VARCHAR(2), CA_ZIP VARCHAR(10), CA_COUNTRY VARCHAR(20), CA_GMT_OFFSET NUMBER(5,2), CA_LOCATION_TYPE

VARCHAR(20) );

ALTER TABLE DEMO_DB.DEMO_SCH.CUSTOMER_ADDRESS ADD SEARCH OPTIMIZATION ON SUBSTRING(CA_ADDRESS_ID);

Which queries will benefit from the use of the search optimization service? (Select TWO).

Options:

A.

select * from DEMO_DB.DEMO_SCH.CUSTOMER_ADDRESS Where substring(CA_ADDRESS_ID,1,8)= substring('AAAAAAAAPHPPLBAAASKDJHASLKDJHASKJD',1,8);

B.

select * from DEMO_DB.DEMO_SCH.CUSTOMER_ADDRESS Where CA_ADDRESS_ID= substring('AAAAAAAAPHPPLBAAASKDJHASLKDJHASKJD',1,16);

C.

select*fromDEMO_DB.DEMO_SCH.CUSTOMER_ADDRESSWhereCA_ADDRESS_IDLIKE ’%BAAASKD%';

D.

select*fromDEMO_DB.DEMO_SCH.CUSTOMER_ADDRESSWhereCA_ADDRESS_IDLIKE '%PHPP%';

E.

select*fromDEMO_DB.DEMO_SCH.CUSTOMER_ADDRESSWhereCA_ADDRESS_IDNOT LIKE '%AAAAAAAAPHPPL%';

Question 35

A company is designing its serving layer for data that is in cloud storage. Multiple terabytes of the data will be used for reporting. Some data does not have a clear use case but could be useful for experimental analysis. This experimentation data changes frequently and is sometimes wiped out and replaced completely in a few days.

The company wants to centralize access control, provide a single point of connection for the end-users, and maintain data governance.

What solution meets these requirements while MINIMIZING costs, administrative effort, and development overhead?

Options:

A.

Import the data used for reporting into a Snowflake schema with native tables. Then create external tables pointing to the cloud storage folders used for the experimentation data. Then create two different roles with grants to the different datasets to match the different user personas, and grant these roles to the corresponding users.

B.

Import all the data in cloud storage to be used for reporting into a Snowflake schema with native tables. Then create a role that has access to this schema and manage access to the data through that role.

C.

Import all the data in cloud storage to be used for reporting into a Snowflake schema with native tables. Then create two different roles with grants to the different datasets to match the different user personas, and grant these roles to the corresponding users.

D.

Import the data used for reporting into a Snowflake schema with native tables. Then create views that have SELECT commands pointing to the cloud storage files for the experimentation data. Then create two different roles to match the different user personas, and grant these roles to the corresponding users.