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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Transformation & Processing | 30% | - Work with semi-structured and unstructured data - Design and implement transformations: SQL, stored procedures, user-defined functions - Implement data quality, validation, and schema evolution - Use streams, tasks, dynamic tables for pipelines and CDC |
| Topic 2: Data Ingestion | 25% | - Handle file formats, compression, encoding, and schema detection - Use COPY INTO, external tables, Snowpipe, Snowpipe Streaming - Ingest from various sources: cloud storage, on-premises, APIs, data lakes |
| Topic 3: Performance Optimization & Scalability | 15% | - Optimize warehouses, clustering, partitioning, and query performance - Monitor and tune workloads and resource usage - Design scalable compute and storage solutions |
| Topic 4: Data Pipeline Orchestration & Automation | 20% | - Build, schedule, monitor, and manage workflows - Integrate with external orchestration tools - Error handling, retry logic, and dependency management |
| Topic 5: Data Governance, Security & Sharing | 10% | - Apply access control, masking, encryption, and compliance - Track lineage, audit, and maintain data integrity - Share data securely across accounts and platforms |
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NEW QUESTION # 365
A data engineer is building a solution to detect sensitive information that is stored in a data lake across multiple Amazon S3 buckets. The solution must detect personally identifiable information (PII) that is in a proprietary data format.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: B
Explanation:
The AWS Glue Detect PII transform is a built-in feature that can automatically identify personally identifiable information (PII) using predefined patterns or custom regular expressions. It works directly within AWS Glue jobs on proprietary data formats and integrates with the Glue Data Catalog, delivering a managed, low-overhead solution compared to building and maintaining custom Lambda or SQL-based detection.
NEW QUESTION # 366
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: B
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 # 367
Jackie, a Data engineer advised to his data team members about one of the Role highlighting fol-lows points:
1. Avoid Using the <?> Role for Automated Scripts
2. Avoid Using the <?> Role to Create Objects
Which System defined or Custom Role She is mentioning?
Answer: C
NEW QUESTION # 368
Which Scenario Data engineer decide Materialized views are not useful. Select All that apply.
Answer: A
Explanation:
Explanation
A materialized view is a pre-computed data set derived from a query specification (the SELECT in the view definition) and stored for later use. Because the data is pre-computed, querying a material-ized view is faster than executing a query against the base table of the view. This performance dif-ferencecan be significant when a query is run frequently or is sufficiently complex. As a result, ma-terialized views can speed up expensive aggregation, projection, and selection operations, especially those that run frequently and that run on large data sets.
Materialized views require Enterprise Edition.
Materialized views are designed to improve query performance for workloads composed of com-mon, repeated query patterns. However, materializing intermediate results incurs additional costs. As such, before creating any materialized views, you should consider whether the costs are offset by the savings from re-using these results frequently enough.
Materialized views are particularly useful when:
Query results contain a small number of rows and/or columns relative to the base table (the table on which the view is defined).
Query results contain results that require significant processing, including:
1. Analysis of semi-structured data.
2. Aggregates that take a long time to calculate.
The query is on an external table (i.e. data sets stored in files in an external stage), which might have slower performance compared to querying native database tables.
The view's base table does not change frequently.
NEW QUESTION # 369
A company has a production AWS account that runs company workloads. The company's security team created a security AWS account to store and analyze security logs from the production AWS account. The security logs in the production AWS account are stored in Amazon CloudWatch Logs.
The company needs to use Amazon Kinesis Data Streams to deliver the security logs to the security AWS account.
Which solution will meet these requirements?
Answer: C
NEW QUESTION # 370
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