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| Section | Weight | Objectives |
|---|---|---|
| Performance Optimization | 20% | - Clustering and partitioning strategies - Query performance tuning - Virtual warehouse configuration and sizing - Cost optimization and resource management |
| Accounts and Security | 25% | - Account and database strategy design - Security, compliance and governance architecture - Security principles and implementation scenarios |
| Snowflake Architecture | 30% | - Development lifecycle and workload support - Data sharing architecture design - Object hierarchy and architectural impact - Data protection and recovery strategies - Data modeling and use cases |
| Data Engineering | 25% | - Data loading and unloading solutions - Data pipeline and integration design - Transformation and processing frameworks |
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NEW QUESTION # 185
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: C
Explanation:
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.
References: The solution is supported by Snowflake documentation that details how search optimization can enhance query performance for semi-structured data1.
NEW QUESTION # 186
What step will im the performance of queries executed against an external table?
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 # 187
An Architect needs to meet a company requirement to ingest files from the company's AWS storage accounts into the company's Snowflake Google Cloud Platform (GCP) account. How can the ingestion of these files into the company's Snowflake account be initiated? (Select TWO).
Answer: A,E
NEW QUESTION # 188
A company has a Snowflake environment running in AWS us-west-2 (Oregon). The company needs to share data privately with a customer who is running their Snowflake environment in Azure East US 2 (Virginia).
What is the recommended sequence of operations that must be followed to meet this requirement?
Answer: A
Explanation:
Option C is the correct answer because it allows the company to share data privately with the customer across different cloud platforms and regions. The company can create a new Snowflake account in Azure East US 2 (Virginia) and set up replication between AWS us-west-2 (Oregon) and Azure East US 2 (Virginia) for the database objects to be shared. This way, the company can ensure that the data is always up to date and consistent in both accounts. The company can then create a share and add the database privileges to the share, and alter the share and add the customer's Snowflake account to the share. The customer can then access the shared data from their own Snowflake account in Azure East US 2 (Virginia).
Option A is incorrect because the Snowflake Marketplace is not a private way of sharing data. The Snowflake Marketplace is a public data exchange platform that allows anyone to browse and subscribe to data sets from various providers. The company would not be able to control who can access their data if they use the Snowflake Marketplace.
Option B is incorrect because it requires the customer to create a new Snowflake account in Azure East US 2 (Virginia), which may not be feasible or desirable for the customer. The customer may already have an existing Snowflake account in a different cloud platform or region, and may not want to incur additional costs or complexity by creating a new account.
Option D is incorrect because it involves creating a reader account in Azure East US 2 (Virginia), which is a limited and temporary way of sharing data. A reader account is a special type of Snowflake account that can only access data from a single share, and has a fixed duration of 30 days. The company would have to manage the reader account's URL and credentials, and renew the account every 30 days. The customer would not be able to use their own Snowflake account to access the shared data, and would have to rely on the company's reader account.
References:
* Snowflake Replication
* Secure Data Sharing Overview
* Snowflake Marketplace Overview
* Reader Account Overview
NEW QUESTION # 189
Snowflake supports the following query performance optimizing methods
Answer: B,C
NEW QUESTION # 190
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