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| Section | Objectives |
|---|---|
| Topic 1: Security, Governance, and Compliance | - Data governance
|
| Topic 2: Data Sharing and Collaboration | - Secure data sharing
|
| Topic 3: Data Architecture and Design | - Design scalable Snowflake architectures
|
| Topic 4: Performance and Optimization | - Query performance tuning
|
| Topic 5: Data Pipelines and Data Engineering | - Data orchestration
|
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NEW QUESTION # 62
A large manufacturing company runs a dozen individual Snowflake accounts across its business divisions. The company wants to increase the level of data sharing to support supply chain optimizations and increase its purchasing leverage with multiple vendors.
The company's Snowflake Architects need to design a solution that would allow the business divisions to decide what to share, while minimizing the level of effort spent on configuration and management. Most of the company divisions use Snowflake accounts in the same cloud deployments with a few exceptions for European-based divisions.
According to Snowflake recommended best practice, how should these requirements be met?
Answer: A
Explanation:
Explanation
According to Snowflake recommended best practice, the requirements of the large manufacturing company should be met by deploying a Private Data Exchange in combination with data shares for the European accounts. A Private Data Exchange is a feature of the Snowflake Data Cloud platform that enables secure and governed sharing of data between organizations. It allows Snowflake customers to create their own data hub and invite other parts of their organization or external partners to access and contribute data sets. A Private Data Exchange provides centralized management, granular access control, and data usage metrics for the data shared in the exchange1. A data share is a secure and direct way of sharing data between Snowflake accounts without having to copy or move the data. A data share allows the data provider to grant privileges on selected objects in their account to one or more data consumers in other accounts2. By using a Private Data Exchange in combination with data shares, the company can achieve the following benefits:
* The business divisions can decide what data to share and publish it to the Private Data Exchange, where it can be discovered and accessed by other members of the exchange. This reduces the effort and complexity of managing multiple data sharing relationships and configurations.
* The company can leverage the existing Snowflake accounts in the same cloud deployments to create the Private Data Exchange and invite the members to join. This minimizes the migration and setup costs and leverages the existing Snowflake features and security.
* The company can use data shares to share data with the European accounts that are in different regions or cloud platforms. This allows the company to comply with the regional and regulatory requirements for data sovereignty and privacy, while still enabling data collaboration across the organization.
* The company can use the Snowflake Data Cloud platform to perform data analysis and transformation on the shared data, as well as integrate with other data sources and applications. This enables the company to optimize its supply chain and increase its purchasing leverage with multiple vendors.
The other options are incorrect because they do not meet the requirements or follow the best practices. Option A is incorrect because migrating the European accounts to the global region may violate the data sovereignty and privacy regulations, and deploying a Data Exchange may not provide the level of control and management that the company needs. Option C is incorrect because deploying to the Snowflake Marketplace may expose the company's data to unwanted consumers, and using invoker_share() in secure views may not provide the desired level of security and governance. Option D is incorrect because using replication to allow European data shares in the Exchange may incur additional costs and complexity, and may not be necessary if data shares can be used instead. References: Private Data Exchange | Snowflake Documentation, Introduction to Secure Data Sharing | Snowflake Documentation
NEW QUESTION # 63
You have turned on auto clustering in your table.
How will you monitor how much credit is getting burnt for the auto clustering in the past week for a specific table?
Answer: A
NEW QUESTION # 64
Which technique will efficiently ingest and consume semi-structured data for Snowflake data lake workloads?
Answer: A
Explanation:
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.
References:
Semi-structured Data
Snowflake for Data Lake
NEW QUESTION # 65
You can define a clustering key directly on top of VARIANT columns
Answer: B
NEW QUESTION # 66
Which SQL alter command will MAXIMIZE memory and compute resources for a Snowpark stored procedure when executed on the snowpark_opt_wh warehouse?
Answer: B
Explanation:
To maximize memory and compute resources for a Snowpark stored procedure, you need to set the MAX_CONCURRENCY_LEVEL parameter for the warehouse that executes the stored procedure. This parameter determines the maximum number of concurrent queries that can run on a single warehouse. By setting it to 16, you ensure that the warehouse can use all the available CPU cores and memory on a single node, which is the optimal configuration for Snowpark-optimized warehouses. This will improve the performance and efficiency of the stored procedure, as it will not have to share resources with other queries or nodes. The other options are incorrect because they either do not change the MAX_CONCURRENCY_LEVEL parameter, or they set it to a lower value than 16, which will reduce the memory and compute resources for the stored procedure. References:
* [Snowpark-optimized Warehouses] 1
* [Training Machine Learning Models with Snowpark Python] 2
* [Snowflake Shorts: Snowpark Optimized Warehouses] 3
NEW QUESTION # 67
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