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| Section | Weight | Objectives |
|---|---|---|
| Data Ingestion and Transformation | 34% | - Ingest and transform data using AWS services - Build and manage data pipelines |
| Data Store Management | 26% | - Select appropriate data storage solutions - Optimize storage performance and cost |
| Data Security and Governance | 18% | - Apply governance and compliance best practices - Implement data security controls |
| Data Operations and Support | 22% | - Troubleshoot data workflow issues - Monitor and maintain data pipelines |
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問題 #94
A company runs a data pipeline that uses AWS Step Functions to orchestrate AWS Lambda functions and AWS Glue jobs. The Lambda functions and AWS Glue jobs require access to multiple Amazon RDS databases. The Lambda functions and AWS Glue jobs already have access to the VPC that hosts the RDS databases.
Which solution will meet these requirements in the MOST secure way?
答案：C
解題說明：
Comprehensive and Detailed Explanation (150-250 words)
AWS security best practices require avoiding long-term credentials and enforcing the principle of least privilege. Using IAM roles with role assumption is the most secure approach for granting temporary, scoped access to AWS resources.
In this solution, an IAM role is created with permissions to access the Amazon RDS databases. A second IAM role is assigned to the AWS Lambda functions and AWS Glue jobs with permission to assume the RDS- access role. This approach eliminates hard-coded credentials, enables automatic credential rotation through AWS Security Token Service (AWS STS), and provides clear separation of duties.
Using the root user or long-term access keys is explicitly discouraged by AWS. Creating IAM users for services violates best practices, as AWS services should use IAM roles, not users. Embedding credentials in JDBC connection strings exposes sensitive information and increases the risk of credential leakage.
Therefore, using IAM role assumption provides the strongest security posture, auditability, and compliance with AWS Well-Architected Framework guidance.
問題 #95
A data engineer is using an AWS Glue ETL job to remove outdated customer records from a table that contains customer account information. The data engineer is using the following SQL command to remove customers that exist in a table named monthly_accounts_update from the customer accounts table:
MERGE INTO accounts t USING monthly_accounts_update s ON t.customer = s.customer WHEN MATCHED THEN DELETE What will happen when the data engineer runs the SQL command?
答案：C
解題說明：
Option A is correct. The MERGE INTO statement is used to conditionally update, insert, or delete rows based on a match condition between a target table and a source table. In this statement, the target table is accounts and the source table is monthly_accounts_update. The join condition is t.customer = s.customer. Because the statement uses WHEN MATCHED THEN DELETE, every row in accounts that has a matching customer value in monthly_accounts_update will be deleted from the target table.
AWS documentation for MERGE INTO states that it conditionally updates, deletes, or inserts rows into an Apache Iceberg table, and the syntax explicitly includes WHEN MATCHED THEN DELETE. AWS Glue guidance and examples for Iceberg also show MERGE INTO as a supported pattern for row-level changes in Glue ETL workflows. This means the syntax is valid for supported Glue-Iceberg use cases, so option D is incorrect. Options B and C are also incorrect because the statement does not retain only matching rows and does not delete the entire table. It deletes only the rows in the target table that satisfy the match condition.
問題 #96
A data engineer needs to make tabular data available in an Amazon S3-based data lake. Users must be able to query the data by using SQL queries in Amazon Redshift, Amazon Athena, and Amazon EMR. The data is updated daily. The data engineer must ensure that updates and deletions are reflected in the data lake.
Which solution will meet these requirements with the LEAST operational overhead?
答案：C
解題說明：
Comprehensive and Detailed Explanation (150-250 words)
Apache Iceberg is a table format designed for large-scale data lakes that supports ACID transactions, schema evolution, time travel, and row-level updates and deletes. Using S3 Tables with Apache Iceberg provides a fully managed experience that integrates natively with Amazon Athena, Amazon Redshift, and Amazon EMR.
By using AWS Glue with the Iceberg catalog, the data engineer can perform daily updates and deletions without managing Spark clusters, compaction scheduling, or metadata cleanup manually. Iceberg handles snapshots, file pruning, and unreferenced file removal automatically, significantly reducing operational overhead.
Apache Hudi requires Amazon EMR clusters, Spark jobs, and manual compaction orchestration, increasing complexity. The Parquet-only approaches in options C and D do not support updates or deletes efficiently and would require full rewrites of datasets, which is not scalable.
Therefore, using S3 Tables with Apache Iceberg provides the most efficient, scalable, and low-maintenance solution that satisfies all query and update requirements.
問題 #97
A company has a data warehouse that contains a table that is named Sales. The company stores the table in Amazon Redshift The table includes a column that is named city_name. The company wants to query the table to find all rows that have a city_name that starts with "San" or "El." Which SQL query will meet this requirement?
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