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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 |
| Topic 2: Data Pipeline Orchestration & Automation | 20% | - Integrate with external orchestration tools - Error handling, retry logic, and dependency management - Build, schedule, monitor, and manage workflows |
| Topic 3: Data Transformation & Processing | 30% | - Design and implement transformations: SQL, stored procedures, user-defined functions - Use streams, tasks, dynamic tables for pipelines and CDC - Implement data quality, validation, and schema evolution - Work with semi-structured and unstructured data |
| Topic 4: Performance Optimization & Scalability | 15% | - Design scalable compute and storage solutions - Optimize warehouses, clustering, partitioning, and query performance - Monitor and tune workloads and resource usage |
| Topic 5: 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 |
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NEW QUESTION # 100
A data engineer needs to debug an AWS Glue job that reads from Amazon S3 and writes to Amazon Redshift. The data engineer enabled the bookmark feature for the AWS Glue job.
The data engineer has set the maximum concurrency for the AWS Glue job to 1.
The AWS Glue job is successfully writing the output to Amazon Redshift. However, the Amazon S3 files that were loaded during previous runs of the AWS Glue job are being reprocessed by subsequent runs.
What is the likely reason the AWS Glue job is reprocessing the files?
Answer: B
Explanation:
https://docs.aws.amazon.com/glue/latest/dg/glue-troubleshooting-errors.html#error-job- bookmarks-reprocess-data
NEW QUESTION # 101
Which are false statements about Star Schema?
Answer: C
NEW QUESTION # 102
A company has AWS resources in multiple AWS Regions. The company has an Amazon EFS file system in each Region where the company operates. The company's data science team operates within only a single Region. The data that the data science team works with must remain within the team's Region.
A data engineer needs to create a single dataset by processing files that are in each of the company's Regional EFS file systems. The data engineer wants to use an AWS Step Functions state machine to orchestrate AWS Lambda functions to process the data.
Which solution will meet these requirements with the LEAST effort?
Answer: D
Explanation:
AWS DataSync is designed for transferring data between AWS storage services across regions, such as EFS, S3, and other storage systems. By using DataSync, you can efficiently transfer data from each of the Regional EFS file systems to the EFS file system in the data science team's Region.
This allows the data to remain within the data science team's Region while being processed, which meets the requirement that data cannot leave the Region.
Once the data is transferred, Lambda functions in the same Region as the data science team can mount the local EFS file system and process the data, minimizing complexity and the risk of network-related latency or failures from cross-region data processing.
This approach ensures minimal operational effort because DataSync automates the transfer process and works seamlessly with EFS, while keeping the data localized for processing by Lambda functions in the same Region.
NEW QUESTION # 103
A company needs to implement a data mesh architecture in which domains for trading, risk, and compliance teams each have own their data. The teams need to share specific views with one another. The teams have over 1,000 tables across 50 databases in AWS Glue Data Catalog. All three teams use Amazon Athena to perform on-demand analysis. The teams use Amazon Redshift to generate complex reports. The compliance team must audit all data access. Access to personally identifiable information (PII) data must be restricted.
The company requires a scalable solution to meet the team requirements. The solution must provide the ability to perform analysis across team domains.
Which solution will meet these requirements?
Answer: D
Explanation:
AWS Lake Formation provides centralized, fine-grained governance (LF-Tags, column/row-level permissions) across domains, integrates with Athena and Redshift for cross-domain analytics, and supports comprehensive auditing via Lake Formation access logs and CloudTrail meeting scalability, PII restriction, and audit requirements.
NEW QUESTION # 104
A company analyzes data in a data lake every quarter to perform inventory assessments. A data engineer uses AWS Glue DataBrew to detect any personally identifiable formation (PII) about customers within the data. The company's privacy policy considers some custom categories of information to be PII. However, the categories are not included in standard DataBrew data quality rules.
The data engineer needs to modify the current process to scan for the custom PII categories across multiple datasets within the data lake.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: D
Explanation:
AWS Glue DataBrew allows users to create custom data quality rules, including rules for detecting non-standard PII categories. By implementing custom rules, the data engineer can automate the detection of PII (including the custom categories) across multiple datasets without needing to manually review or develop custom scripts. This provides a solution with the least operational overhead, as it leverages the existing DataBrew framework and automation capabilities.
Manually reviewing the data is highly labor-intensive, error-prone, and not scalable, especially across multiple datasets.
While possible, writing custom Python scripts adds operational overhead, as it requires developing, maintaining, and integrating scripts with DataBrew.
This increases complexity compared to implementing custom rules directly within DataBrew.
While regex can be useful, incorporating it directly into ETL processes adds operational complexity and doesn't leverage the capabilities of DataBrew, which is already designed for data cleaning and quality validation.
NEW QUESTION # 105
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