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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Governance, Security & Sharing | 10% | - Track lineage, audit, and maintain data integrity - Share data securely across accounts and platforms - Apply access control, masking, encryption, and compliance |
| Topic 2: Data Transformation & Processing | 30% | - Use streams, tasks, dynamic tables for pipelines and CDC - Design and implement transformations: SQL, stored procedures, user-defined functions - Work with semi-structured and unstructured data - Implement data quality, validation, and schema evolution |
| Topic 3: Performance Optimization & Scalability | 15% | - Design scalable compute and storage solutions - Monitor and tune workloads and resource usage - Optimize warehouses, clustering, partitioning, and query performance |
| Topic 4: Data Ingestion | 25% | - Ingest from various sources: cloud storage, on-premises, APIs, data lakes - Use COPY INTO, external tables, Snowpipe, Snowpipe Streaming - Handle file formats, compression, encoding, and schema detection |
| Topic 5: Data Pipeline Orchestration & Automation | 20% | - Error handling, retry logic, and dependency management - Build, schedule, monitor, and manage workflows - Integrate with external orchestration tools |
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NEW QUESTION # 375
A company wants to use Apache Spark jobs that run on an Amazon EMR cluster to process streaming data. The Spark jobs will transform and store the data in an Amazon S3 bucket. The company will use Amazon Athena to perform analysis.
The company needs to optimize the data format for analytical queries.
Which solutions will meet these requirements with the SHORTEST query times? (Choose two.)
Answer: A,E
Explanation:
ORC is a columnar format optimized for Athena and provides high compression and predicate pushdown for faster queries; Glue Data Catalog manages schema evolution.
Parquet is a columnar format optimized for Athena with efficient compression and predicate pushdown; Glue Data Catalog tracks schema changes.
NEW QUESTION # 376
A company stores sensitive transaction data in an Amazon S3 bucket. A data engineer must implement controls to prevent accidental deletions. Which solution will meet this requirement?
Answer: D
Explanation:
Enabling S3 versioning preserves all object versions so data can be recovered if deletion occurs, and MFA delete adds an additional layer of protection by requiring multi-factor authentication to permanently delete objects or change versioning settings, effectively preventing accidental deletions.
NEW QUESTION # 377
A company is building a dashboard for thousands of Analysts. The dashboard presents the results of a few summary queries on tables that are regularly updated. The query conditions vary by tope according to what data each Analyst needs Responsiveness of the dashboard queries is a top priority, and the data cache should be preserved.
How should the Data Engineer configure the compute resources to support this dashboard?
Answer: B
Explanation:
Explanation
This option is the best way to configure the compute resources to support this dashboard. By assigning all queries to a multi-cluster virtual warehouse set to maximized mode, the Data Engineer can ensure that there is enough compute capacity to handle thousands of concurrent queries from different analysts. A multi-cluster virtual warehouse can scale up or down by adding or removing clusters based on the load. A maximized scaling policy ensures that there is always at least one cluster running and that new clusters are added as soon as possible whenneeded. By monitoring the utilization and performance of the virtual warehouse, the Data Engineer can determine the smallest suitable number of clusters that can meet the responsiveness requirement and minimize costs.
NEW QUESTION # 378
A company is using Amazon Redshift as a data warehousing service. It is running analytical queries on large amounts of data using ODBC connections. The operations team is observing that some queries are getting completed while others are getting hung. They are seeking your suggestions to resolve this issue.
What setting can be done?
Answer: A
Explanation:
Maximum Transmission Unit (MTU) determines the maximum size in packet bytes that can be transferred in a single Ethernet frame between two hosts.
Customers may face query hang issues when the MTU size in a network path between two IP hosts is mismatched. To resolve the query hang issue for Redshift ODBC connections, the MTU size can be reduced.
Reference:
https://docs.aws.amazon.com/redshift/latest/dg/queries-troubleshooting.html#queries-troubleshooting-query-hangs
https://docs.aws.amazon.com/redshift/latest/mgmt/connecting-drop-issues.html
NEW QUESTION # 379
What is the purpose of the BUILD_FILE_URL function in Snowflake?
Answer: A
Explanation:
Explanation
The BUILD_FILE_URL function in Snowflake generates a temporary URL for accessing a file in a stage. The function takes two arguments: the stage name and the file path. The generated URL is valid for 24 hours and can be used to download or view the file contents. The other options are incorrect because they do not describe the purpose of the BUILD_FILE_URL function.
NEW QUESTION # 380
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