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Snowflake SnowPro Advanced Architect Certification Sample Questions (Q88-Q93):

NEW QUESTION # 88
Which of the following ingestion methods can be used to load near real-time data by using the messaging services provided by a cloud provider?

A. Snowpipe
B. Spark
C. Snowflake Connector for Kafka
D. Snowflake streams

Answer: A,C

Explanation:
Snowflake Connector for Kafka and Snowpipe are two ingestion methods that can be used to load near real-time data by using the messaging services provided by a cloud provider. Snowflake Connector for Kafka enables you to stream structured and semi-structured data from Apache Kafka topics into Snowflake tables.
Snowpipe enables you to load data from files that are continuously added to a cloud storage location, such as Amazon S3 or Azure Blob Storage. Both methods leverage Snowflake's micro-partitioning and columnar storage to optimize data ingestion and query performance. Snowflake streams and Spark are not ingestion methods, but rather components of the Snowflake architecture. Snowflake streams provide change data capture (CDC) functionality by tracking data changes in a table. Spark is a distributed computing framework that can be used to process large-scale data and write it to Snowflake using the Snowflake Spark Connector. References:
* Snowflake Connector for Kafka
* Snowpipe
* Snowflake Streams
* Snowflake Spark Connector

NEW QUESTION # 89
A company's daily Snowflake workload consists of a huge number of concurrent queries triggered between 9pm and 11pm. At the individual level, these queries are smaller statements that get completed within a short time period.
What configuration can the company's Architect implement to enhance the performance of this workload? (Choose two.)

A. Set the MAX_CONCURRENCY_LEVEL to a higher value than its default value of 8 at the virtual warehouse level.
B. Increase the size of the virtual warehouse to size X-Large.
C. Set the connection timeout to a higher value than its default.
D. Enable a multi-clustered virtual warehouse in maximized mode during the workload duration.
E. Reduce the amount of data that is being processed through this workload.

Answer: A,D

Explanation:
These two configuration options can enhance the performance of the workload that consists of a huge number of concurrent queries that are smaller and faster.
Enabling a multi-clustered virtual warehouse in maximized mode allows the warehouse to scale out automatically by adding more clusters as soon as the current cluster is fully loaded, regardless of the number of queries in the queue. This can improve the concurrency and throughput of the workload by minimizing or preventing queuing. The maximized mode is suitable for workloads that require high performance and low latency, and are less sensitive to credit consumption1.
Setting the MAX_CONCURRENCY_LEVEL to a higher value than its default value of 8 at the virtual warehouse level allows the warehouse to run more queries concurrently on each cluster. This can improve the utilization and efficiency of the warehouse resources, especially for smaller and faster queries that do not require a lot of processing power. The MAX_CONCURRENCY_LEVEL parameter can be set when creating or modifying a warehouse, and it can be changed at any time2.
Reference:
Snowflake Documentation: Scaling Policy for Multi-cluster Warehouses
Snowflake Documentation: MAX_CONCURRENCY_LEVEL

NEW QUESTION # 90
A media company needs a data pipeline that will ingest customer review data into a Snowflake table, and apply some transformations. The company also needs to use Amazon Comprehend to do sentiment analysis and make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions.
The data pipeline needs to run continuously ang efficiently as new records arrive in the object storage leveraging event notifications. Also, the operational complexity, maintenance of the infrastructure, including platform upgrades and security, and the development effort should be minimal.
Which design will meet these requirements?

A. Ingest the data into Snowflake using Amazon EMR and PySpark using the Snowflake Spark connector.
Apply transformations using another Spark job. Develop a python program to do model inference by leveraging the Amazon Comprehend text analysis API. Then write the results to a Snowflake table and create a listing in the Snowflake Marketplace to make the data available to other companies.
B. Ingest the data using Snowpipe and use streams and tasks to orchestrate transformations. Create an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.
C. Ingest the data using COPY INTO and use streams and tasks to orchestrate transformations. Export the data into Amazon S3 to do model inference with Amazon Comprehend and ingest the data back into a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.
D. Ingest the data using Snowpipe and use streams and tasks to orchestrate transformations. Export the data into Amazon S3 to do model inference with Amazon Comprehend and ingest the data back into a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.

Answer: B

Explanation:
This design meets all the requirements for the data pipeline. Snowpipe is a feature that enables continuous data loading into Snowflake from object storage using event notifications. It is efficient, scalable, and serverless, meaning it does not require any infrastructure or maintenance from the user. Streams and tasks are features that enable automated data pipelines within Snowflake, using change data capture and scheduled execution.
They are also efficient, scalable, and serverless, and they simplify the data transformation process. External functions are functions that can invoke external services or APIs from within Snowflake. They can be used to integrate with Amazon Comprehend and perform sentiment analysis on the data. The results can be written back to a Snowflake table using standard SQL commands. Snowflake Marketplace is a platform that allows data providers to share data with data consumers across different accounts, regions, and cloud platforms. It is a secure and easy way to make data publicly available to other companies.
References:
* Snowpipe Overview | Snowflake Documentation
* Introduction to Data Pipelines | Snowflake Documentation
* External Functions Overview | Snowflake Documentation
* Snowflake Data Marketplace Overview | Snowflake Documentation

NEW QUESTION # 91
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?

A. The consumer role will automatically see the new table and no additional grants are needed.
B. The 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;
C. The 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;
D. The 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 ;

Answer: D

Explanation:
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 ; Explanation:
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

NEW QUESTION # 92
What step will im the performance of queries executed against an external table?

A. Use an internal stage instead of an external stage to store the source files.
B. Convert the source files' character encoding to UTF-8.
C. Partition the external table.
D. Shorten the names of the source files.

Answer: C

Explanation:
Partitioning an external table is a technique that improves the performance of queries executed against the table by reducing the amount of data scanned. Partitioning an external table involves creating one or more partition columns that define how the table is logically divided into subsets of data based on the values in those columns. The partition columns can be derived from the file metadata (such as file name, path, size, or modification time) or from the file content (such as a column value or a JSON attribute). Partitioning an external table allows the query optimizer to prune the files that do not match the query predicates, thus avoiding unnecessary data scanning and processing2 The other options are not effective steps for improving the performance of queries executed against an external table:
* Shorten the names of the source files. This option does not have any impact on the query performance, as the file names are not used for query processing. The file names are only used for creating the external table and displaying the query results3
* Convert the source files' character encoding to UTF-8. This option does not affect the query performance, as Snowflake supports various character encodings for external table files, such as UTF-8, UTF-16, UTF-32, ISO-8859-1, and Windows-1252. Snowflake automatically detects the character encoding of the files and converts them to UTF-8 internally for query processing4
* Use an internal stage instead of an external stage to store the source files. This option is not applicable, as external tables can only reference files stored in external stages, such as Amazon S3, Google Cloud
* Storage, or Azure Blob Storage. Internal stages are used for loading data into internal tables, not external tables5 References:
* 1: SnowPro Advanced: Architect | Study Guide
* 2: Snowflake Documentation | Partitioning External Tables
* 3: Snowflake Documentation | Creating External Tables
* 4: Snowflake Documentation | Supported File Formats and Compression for Staged Data Files
* 5: Snowflake Documentation | Overview of Stages
* : SnowPro Advanced: Architect | Study Guide
* : Partitioning External Tables
* : Creating External Tables
* : Supported File Formats and Compression for Staged Data Files
* : Overview of Stages

NEW QUESTION # 93
......

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