Snowflake ARA-R01 SnowPro Advanced: Architect Recertification Exam Practice Test

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Total 162 questions
Question 1

A company has built a data pipeline using Snowpipe to ingest files from an Amazon S3 bucket. Snowpipe is configured to load data into staging database tables. Then a task runs to load the data from the staging database tables into the reporting database tables.

The company is satisfied with the availability of the data in the reporting database tables, but the reporting tables are not pruning effectively. Currently, a size 4X-Large virtual warehouse is being used to query all of the tables in the reporting database.

What step can be taken to improve the pruning of the reporting tables?



Answer : C

Effective pruning in Snowflake relies on the organization of data within micro-partitions. By using an ORDER BY clause with clustering keys when loading data into the reporting tables, Snowflake can better organize the data within micro-partitions. This organization allows Snowflake to skip over irrelevant micro-partitions during a query, thus improving query performance and reducing the amount of data scanned12.

Reference =

* Snowflake Documentation on micro-partitions and data clustering2

* Community article on recognizing unsatisfactory pruning and improving it1


Question 2

An Architect needs to design a solution for building environments for development, test, and pre-production, all located in a single Snowflake account. The environments should be based on production data.

Which solution would be MOST cost-effective and performant?



Answer : A

Zero-copy cloning is a feature in Snowflake that allows for the creation of a clone of a database, schema, or table without duplicating any data, which is cost-effective as it saves on storage costs. Transient tables are temporary and do not incur storage costs for the time they are not accessed, making them a cost-effective option for development, test, and pre-production environments that do not require the durability of permanent tables123.

Reference

* Snowflake Documentation on Zero-Copy Cloning3.

* Articles discussing the cost-effectiveness and performance benefits of zero-copy cloning12.


Question 3

An Architect is implementing a CI/CD process. When attempting to clone a table from a production to a development environment, the cloning operation fails.

What could be causing this to happen?



Answer : B

Cloning a table with a masking policy can cause the cloning operation to fail because the masking policy is not automatically cloned with the table. This is due to the fact that the masking policy is considered a separate object with its own set of privileges1.

Reference

Snowflake Documentation on Cloning Considerations1.


Question 4

A new table and streams are created with the following commands:

CREATE OR REPLACE TABLE LETTERS (ID INT, LETTER STRING) ;

CREATE OR REPLACE STREAM STREAM_1 ON TABLE LETTERS;

CREATE OR REPLACE STREAM STREAM_2 ON TABLE LETTERS APPEND_ONLY = TRUE;

The following operations are processed on the newly created table:

INSERT INTO LETTERS VALUES (1, 'A');

INSERT INTO LETTERS VALUES (2, 'B');

INSERT INTO LETTERS VALUES (3, 'C');

TRUNCATE TABLE LETTERS;

INSERT INTO LETTERS VALUES (4, 'D');

INSERT INTO LETTERS VALUES (5, 'E');

INSERT INTO LETTERS VALUES (6, 'F');

DELETE FROM LETTERS WHERE ID = 6;

What would be the output of the following SQL commands, in order?

SELECT COUNT (*) FROM STREAM_1;

SELECT COUNT (*) FROM STREAM_2;



Answer : C

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.


Question 5

An Architect for a multi-national transportation company has a system that is used to check the weather conditions along vehicle routes. The data is provided to drivers.

The weather information is delivered regularly by a third-party company and this information is generated as JSON structure. Then the data is loaded into Snowflake in a column with a VARIANT data type. This

table is directly queried to deliver the statistics to the drivers with minimum time lapse.

A single entry includes (but is not limited to):

- Weather condition; cloudy, sunny, rainy, etc.

- Degree

- Longitude and latitude

- Timeframe

- Location address

- Wind

The table holds more than 10 years' worth of data in order to deliver the statistics from different years and locations. The amount of data on the table increases every day.

The drivers report that they are not receiving the weather statistics for their locations in time.

What can the Architect do to deliver the statistics to the drivers faster?



Answer : B

To improve the performance of queries on semi-structured data, such as JSON stored in a VARIANT column, Snowflake's search optimization service can be utilized. By adding search optimization specifically for the longitude and latitude fields within the VARIANT column, the system can perform point lookups and substring queries more efficiently. This will allow for faster retrieval of weather statistics, which is critical for the drivers to receive timely updates.


Question 6

What are characteristics of Dynamic Data Masking? (Select TWO).



Answer : A, B

Dynamic Data Masking is a feature that allows masking sensitive data in query results based on the role of the user who executes the query. A masking policy is a user-defined function that specifies the masking logic and can be applied to one or more columns in one or more tables. A masking policy that is currently set on a table can be dropped using the ALTER TABLE command. A single masking policy can be applied to columns in different tables using the ALTER TABLE command with the SET MASKING POLICY clause. The other options are either incorrect or not supported by Snowflake. A masking policy cannot be applied to the value column of an external table, as external tables do not support column-level security. The role that creates the masking policy will not always see unmasked data in query results, as the masking policy can be applied to the owner role as well. A masking policy cannot be applied to a column with the GEOGRAPHY data type, as Snowflake only supports masking policies for scalar data types.Reference:Snowflake Documentation: Dynamic Data Masking,Snowflake Documentation: ALTER TABLE


Question 7

Which technique will efficiently ingest and consume semi-structured data for Snowflake data lake workloads?



Answer : C

Option C is the correct answer because schema-on-read is a technique that allows Snowflake to ingest and consume semi-structured data without requiring a predefined schema. Snowflake supports various semi-structured data formats such as JSON, Avro, ORC, Parquet, and XML, and provides native data types (ARRAY, OBJECT, and VARIANT) for storing them. Snowflake also provides native support for querying semi-structured data using SQL and dot notation. Schema-on-read enables Snowflake to query semi-structured data at the same speed as performing relational queries while preserving the flexibility of schema-on-read. Snowflake's near-instant elasticity rightsizes compute resources, and consumption-based pricing ensures you only pay for what you use.

Option A is incorrect because IDEF1X is a data modeling technique that defines the structure and constraints of relational data using diagrams and notations. IDEF1X is not suitable for ingesting and consuming semi-structured data, which does not have a fixed schema or structure.

Option B is incorrect because schema-on-write is a technique that requires defining a schema before loading and processing data. Schema-on-write is not efficient for ingesting and consuming semi-structured data, which may have varying or complex structures that are difficult to fit into a predefined schema. Schema-on-write also introduces additional overhead and complexity for data transformation and validation.

Option D is incorrect because information schema is a set of metadata views that provide information about the objects and privileges in a Snowflake database. Information schema is not a technique for ingesting and consuming semi-structured data, but rather a way of accessing metadata about the data.


Semi-structured Data

Snowflake for Data Lake

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Total 162 questions