ARA-C01 Exam Dumps - Snowflake SnowPro Advanced: Architect Questions and Answers

According to the Snowflake documentation, object parameters are parameters that can be set on individual objects such as databases, schemas, tables, and stages. Object parameters can be set by users with the appropriate privileges on the objects. For example, to set the object parameter AUTO_REFRESH on a table, the user must have the MODIFY privilege on the table. The ACCOUNTADMIN role has the highest level of privileges on all objects in the account, so it can set any object parameter on any object. However, other roles, such as SECURITYADMIN or SYSADMIN, do not have the same level of privileges on all objects, so they cannot set object parameters on objects they do not own or have the required privileges on. Therefore, the correct answer is C. ACCOUNTADMIN, USER with PRIVILEGE.

Parameters | Snowflake Documentation

Object Parameters | Snowflake Documentation

Object Privileges | Snowflake Documentation

The requirement is for the data to be accessible as quickly as possible after it arrives in the external stage with minimal coding effort.

Option A:Snowpipe with auto-ingest is a service that continuously loads data as it arrives in the stage. With auto-ingest, Snowpipe automatically detects new files as they arrive in a cloud stage and loads the data into the specified Snowflake table with minimal delay and no intervention required. This is an ideal low-maintenance solution for the given scenario where files are arriving at a very high frequency.

Option E:Using a combination of a task and a stream allows for real-time change data capture in Snowflake. A stream records changes (inserts, updates, and deletes) made to a table, and a task can be scheduled to trigger on a very short interval, ensuring that changes are processed into the dashboard tables as they occur.

In a managed access schema, the privilege management is centralized with the schema owner, who has the authority to grant object privileges within the schema. Additionally, the SECURITYADMIN role has the capability to manage object grants globally, which includes within managed access schemas. Other roles, such as SYSADMIN or database owners, do not inherently have this privilege unless explicitly granted.

The Snowpipe REST API provides two endpoints for retrieving the data load history: insertReport and loadHistoryScan. The insertReport endpoint returns the status of the files that were submitted to the insertFiles endpoint, while the loadHistoryScan endpoint returns the history of the files that were actually loaded into the table by Snowpipe. To keep a log of data load history, it is recommended to use the loadHistoryScan endpoint, which provides more accurate and complete information about the data ingestion process. The loadHistoryScan endpoint accepts a start time and an end time as parameters, and returns the files that were loaded within that time range. The maximum time range that can be specified is 15 minutes, and the maximum number of files that can be returned is 10,000. Therefore, to keep a log of data load history, the best option is to call the loadHistoryScan endpoint every 10 minutes for a 15-minute time range, and store the results in a log file or a table. This way, the log will capture all the files that were loaded by Snowpipe, and avoid any gaps or overlaps in the time range. The other options are incorrect because:

Calling insertReport every 20 minutes, fetching the last 10,000 entries, will not provide a complete log of data load history, as some files may be missed or duplicated due to the asynchronous nature of Snowpipe. Moreover, insertReport only returns the status of the files that were submitted, not the files that were loaded.

Calling loadHistoryScan every minute for the maximum time range will result in too many API calls and unnecessary overhead, as the same files will be returned multiple times. Moreover, the maximum time range is 15 minutes, not 1 minute.

Calling insertReport every 8 minutes for a 10-minute time range will suffer from the same problems as option A, and also create gaps or overlaps in the time range.

Snowpipe REST API

Option 1: Loading Data Using the Snowpipe REST API

PIPE_USAGE_HISTORY

Option B is the best design to meet the requirements because it uses Snowpipe to ingest the data continuously and efficiently as new records arrive in the object storage, leveraging event notifications. Snowpipe is a service that automates the loading of data from external sources into Snowflake tables1. It also uses streams and tasks to orchestrate transformations on the ingested data. Streams are objects that store the change history of a table, and tasks are objects that execute SQL statements on a schedule or when triggered by another task2. Option B also uses an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. An external function is a user-defined function that calls an external API, such as Amazon Comprehend, to perform computations that are not natively supported by Snowflake3. Finally, option B uses the Snowflake Marketplace to make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions. The Snowflake Marketplace is a platform that enables data providers to list and share their data sets with data consumers, regardless of the cloud platform or region they use4.

Option A is not the best design because it uses copy into to ingest the data, which is not as efficient and continuous as Snowpipe. Copy into is a SQL command that loads data from files into a table in a single transaction. It also exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.

Option C is not the best design because it uses Amazon EMR and PySpark to ingest and transform the data, which also increases the operational complexity and maintenance of the infrastructure. Amazon EMR is a cloud service that provides a managed Hadoop framework to process and analyze large-scale data sets. PySpark is a Python API for Spark, a distributed computing framework that can run on Hadoop. Option C also develops a python program to do model inference by leveraging the Amazon Comprehend text analysis API, which increases the development effort.

Option D is not the best design because it is identical to option A, except for the ingestion method. It still exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.

To create a read-only role for certain employees working in the human resources department, the role needs to have the following permissions on the hr_db database:

USAGE on the database: This allows the role to access the database and see its schemas and objects.

USAGE on all schemas in the database: This allows the role to access the schemas and see their objects.

SELECT on all tables in the database: This allows the role to query the data in the tables.

Option A is the correct answer because it grants the minimum permissions required for a read-only role on the hr_db database.

Option B is incorrect because SELECT on schemas is not a valid permission. Schemas only support USAGE and CREATE permissions.

Option C is incorrect because MODIFY on the database is not a valid permission. Databases only support USAGE, CREATE, MONITOR, and OWNERSHIP permissions. Moreover, USAGE on tables is not sufficient for querying the data. Tables support SELECT, INSERT, UPDATE, DELETE, TRUNCATE, REFERENCES, and OWNERSHIP permissions.

Option D is incorrect because REFERENCES on tables is not relevant for querying the data. REFERENCES permission allows the role to create foreign key constraints on the tables.

https://docs.snowflake.com/en/user-guide/security-access-control-privileges.html#database-privileges

https://docs.snowflake.com/en/user-guide/security-access-control-privileges.html#schema-privileges

https://docs.snowflake.com/en/user-guide/security-access-control-privileges.html#table-privileges

 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]

 Compilation time is the time it takes for the optimizer to create an optimal query plan for the efficient execution of the query. It also involves some pruning of partition files, making the query execution efficient2

If the compilation time is greater than the execution time, it means that the optimizer spent more time analyzing the query than actually running it. This could indicate that the query has overly complex logic, such as multiple joins, subqueries, aggregations, or expressions. The complexity of the query could also affect the size and quality of the query plan, which could impact the performance of the query3

To reduce the compilation time, the Architect can try to simplify the query logic, use views or common table expressions (CTEs) to break down the query into smaller parts, or use hints to guide the optimizer. The Architect can also use the EXPLAIN command to examine the query plan and identify potential bottlenecks or inefficiencies4 References:

1: SnowPro Advanced: Architect | Study Guide 5

2: Snowflake Documentation | Query Profile Overview 6

3: Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time 7

4: Snowflake Documentation | Optimizing Query Performance 8

: SnowPro Advanced: Architect | Study Guide

: Query Profile Overview

: Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time

: Optimizing Query Performance