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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Transformation & Processing | 30% | - Work with semi-structured and unstructured data - Use streams, tasks, dynamic tables for pipelines and CDC - Design and implement transformations: SQL, stored procedures, user-defined functions - Implement data quality, validation, and schema evolution |
| Topic 2: Data Ingestion | 25% | - Use COPY INTO, external tables, Snowpipe, Snowpipe Streaming - Ingest from various sources: cloud storage, on-premises, APIs, data lakes - Handle file formats, compression, encoding, and schema detection |
| Topic 3: Data Governance, Security & Sharing | 10% | - Share data securely across accounts and platforms - Apply access control, masking, encryption, and compliance - Track lineage, audit, and maintain data integrity |
| Topic 4: Data Pipeline Orchestration & Automation | 20% | - Integrate with external orchestration tools - Build, schedule, monitor, and manage workflows - Error handling, retry logic, and dependency management |
| Topic 5: Performance Optimization & Scalability | 15% | - Optimize warehouses, clustering, partitioning, and query performance - Monitor and tune workloads and resource usage - Design scalable compute and storage solutions |
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NEW QUESTION # 375
Jonas, a Lead Performance Engineer,identifed that some of the operation of his query which func-tionally remove the duplicates from huge data set is spilling the data to remote disk. How can he alleviate spilling to a remote disk for better query performance?
Answer: B,C
Explanation:
Explanation
For some operations (e.g. duplicate elimination for a huge data set), the amount of memory available for the compute resources used to execute the operation might not be sufficient to hold intermediate results. As a result, the query processing engine will start spilling the data to local disk. If the local disk space is not sufficient, the spilled data is then saved to remote disks.
This spilling can have a profound effect on query performance (especially if remote disk is used for spilling).
To alleviate this, It is recommend that:
Using a larger warehouse (effectively increasing the available memory/local disk space for the op-eration), and/or Processing data in smaller batches.
NEW QUESTION # 376
Two data engineering teams use separate AWS accounts. Both teams request access to the same datashare in an Amazon Redshift cluster that is in a third AWS account. The datashare is named salesshare.
A data engineer must use the Amazon Redshift SQL interface to grant both data engineering teams' access to the datashare.
Which command or commands will meet this requirement?
Answer: D
Explanation:
In Amazon Redshift data sharing, cross-account permissions for a datashare are granted to consumer cluster/serverless namespaces. Granting USAGE on the datashare to each consumer namespace enables both teams' Redshift environments in their respective accounts to create and query databases from the shared data.
NEW QUESTION # 377
A company stores customer data that contains personally identifiable information (PII) in an Amazon Redshift cluster. The company's marketing, claims, and analytics teams need to be able to access the customer data.
The marketing team should have access to obfuscated claim information but should have full access to customer contact information. The claims team should have access to customer information for each claim that the team processes. The analytics team should have access only to obfuscated PII data.
Which solution will enforce these data access requirements with the LEAST administrative overhead?
Answer: A
NEW QUESTION # 378
Stuart, a Lead Data Engineer in MACRO Data Company created streams on set of External tables. He has been asked to extend the data retention period of the stream for 90 days, which parameter he can utilize to enable this extension?
Answer: A
Explanation:
Explanation
External tables do not have data retention period applicable.
Good to Understand other Options available.
DATA_RETENTION_TIME_IN_DAYS
Type: Object (for databases, schemas, and tables) - Can be set for Account - Database - Schema - Table Description: Number of days for which Snowflake retains historical data for performing Time Trav-el actions (SELECT, CLONE, UNDROP) on the object. A value of 0 effectively disables Time Travel for the specified database, schema, or table.
Values:
0 or 1 (for Standard Edition)
0 to 90 (for Enterprise Edition or higher)
Default:
1
MAX_DATA_EXTENSION_TIME_IN_DAYS
Type: Object (for databases, schemas, and tables) - Can be set for Account - Database - Schema - Table Description: Maximum number of days for which Snowflake can extend the data retention period for tables to prevent streams on the tables from becoming stale. By default, if the DA-TA_RETENTION_TIME_IN_DAYS setting for a source table is less than 14 days, and a stream has not been consumed, Snowflake temporarily extends this period to the stream's offset, up to a maximum of 14 days, regardless of the Snowflake Edition for your account. The MAX_DATA_EXTENSION_TIME_IN_DAYS parameter enables you to limit this automatic ex-tension period to control storage costs for data retention or for compliance reasons.
This parameter can be set at the account, database, schema, and table levels. Note that setting the parameter at the account or schema level only affects tables for which the parameter has not already been explicitly set at a lower level (e.g. at the table level by the table owner). A value of 0 effective-ly disables the automatic extension for the specified database, schema, or table.
Values:
0 to 90 (i.e. 90 days) - a value of 0 disables the automatic extension of the data retention period. To increase the maximum value for tables in your account, Client needs to contact Snowflake Sup-port.
Default:14
NEW QUESTION # 379
A company stores data from an application in an Amazon DynamoDB table that operates in provisioned capacity mode. The workloads of the application have predictable throughput load on a regular schedule. Every Monday, there is an immediate increase in activity early in the morning.
The application has very low usage during weekends.
The company must ensure that the application performs consistently during peak usage times.
Which solution will meet these requirements in the MOST cost-effective way?
Answer: C
NEW QUESTION # 380
......
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