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| Section | Weight | Objectives |
|---|---|---|
| Data Pipeline Orchestration & Automation | 20% | - Integrate with external orchestration tools - Build, schedule, monitor, and manage workflows - Error handling, retry logic, and dependency management |
| Performance Optimization & Scalability | 15% | - Optimize warehouses, clustering, partitioning, and query performance - Monitor and tune workloads and resource usage - Design scalable compute and storage solutions |
| Data Ingestion | 25% | - Handle file formats, compression, encoding, and schema detection - Ingest from various sources: cloud storage, on-premises, APIs, data lakes - Use COPY INTO, external tables, Snowpipe, Snowpipe Streaming |
| Data Governance, Security & Sharing | 10% | - Track lineage, audit, and maintain data integrity - Apply access control, masking, encryption, and compliance - Share data securely across accounts and platforms |
| 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 |
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NEW QUESTION # 123
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: A
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 # 124
A large table with 200 columns contains two years of historical data. When queried. the table is filtered on a single day Below is the Query Profile:
Using a size 2XL virtual warehouse, this query look over an hour to complete What will improve the query performance the MOST?
Answer: A
Explanation:
Explanation
Adding a date column as a cluster key on the table will improve the query performance by reducing the number of micro-partitions that need to be scanned. Since the table is filtered on a single day, clustering by date will make the query more selective and efficient.
NEW QUESTION # 125
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 # 126
A company uses a data lake that is based on an Amazon S3 bucket. To comply with regulations, the company must apply two layers of server-side encryption to files that are uploaded to the S3 bucket. The company wants to use an AWS Lambda function to apply the necessary encryption.
Which solution will meet these requirements?
Answer: D
Explanation:
https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingDSSEncryption.html
NEW QUESTION # 127
A company receives a daily file that contains customer data in .xls format. The company stores the file in Amazon S3. The daily file is approximately 2 GB in size.
A data engineer concatenates the column in the file that contains customer first names and the column that contains customer last names. The data engineer needs to determine the number of distinct customers in the file.
Which solution will meet this requirement with the LEAST operational effort?
Answer: A
Explanation:
AWS Glue DataBrew: AWS Glue DataBrew is a visual data preparation tool that allows data engineers and data analysts to clean and normalize data without writing code. Using DataBrew, a data engineer could create a recipe that includes the concatenation of the customer first and last names and then use the COUNT_DISTINCT function. This would not require complex code and could be performed through the DataBrew user interface, representing a lower operational effort.
NEW QUESTION # 128
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