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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Ingestion and Transformation | 34% | - Build and manage data pipelines - Ingest and transform data using AWS services |
| Topic 2: Data Security and Governance | 18% | - Apply governance and compliance best practices - Implement data security controls |
| Topic 3: Data Store Management | 26% | - Select appropriate data storage solutions - Optimize storage performance and cost |
| Topic 4: Data Operations and Support | 22% | - Troubleshoot data workflow issues - Monitor and maintain data pipelines |
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NEW QUESTION # 200
A retail company is developing a data lake solution on Amazon S3 to analyze historical sales data. The solution needs to support frequent schema changes as new product attributes are added. The company must also be able to query point-in-time historical data snapshots for compliance reporting. The solution must provide atomicity, consistency, isolation, and durability (ACID) transaction guarantees for concurrent write operations.
Which solution will meet these requirements?
Answer: D
Explanation:
The correct answer is B because Apache Iceberg is designed for analytical data lake tables that need schema evolution, transactional consistency, and historical snapshot access. AWS Glue supports Apache Iceberg for data lakes and can read and write Iceberg tables in Amazon S3 through the AWS Glue Data Catalog. The AWS Glue documentation also shows Iceberg tables being created with TBLPROPERTIES ( " format-version " = " 2 " ), which is the correct table format direction for modern Iceberg capabilities. CSV with scheduled conversion does not provide ACID transactions or point-in-time table snapshots. S3 Versioning tracks object versions but does not provide table-level ACID semantics. DynamoDB is not the best fit for an S3-based analytical data lake requirement.
NEW QUESTION # 201
A company extracts approximately 1 TB of data every day from data sources such as SAP HANA, Microsoft SQL Server, MongoDB, Apache Kafka, and Amazon DynamoDB. Some of the data sources have undefined data schemas or data schemas that change.
A data engineer must implement a solution that can detect the schema for these data sources. The solution must extract, transform, and load the data to an Amazon S3 bucket. The company has a service level agreement (SLA) to load the data into the S3 bucket within 15 minutes of data creation.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: C
Explanation:
AWS Glue is a fully managed service that provides a serverless data integration platform. It can automatically discover and categorize data from various sources, including SAP HANA, Microsoft SQL Server, MongoDB, Apache Kafka, and Amazon DynamoDB. It can also infer the schema of the data and store it in the AWS Glue Data Catalog, which is a central metadata repository. AWS Glue can then use the schema information to generate and run Apache Spark code to extract, transform, and load the data into an Amazon S3 bucket. AWS Glue can also monitor and optimize the performance and cost of the data pipeline, and handle any schema changes that may occur in the source data. AWS Glue can meet the SLA of loading the data into the S3 bucket within 15 minutes of data creation, as it can trigger the data pipeline based on events, schedules, or on-demand. AWS Glue has the least operational overhead among the options, as it does not require provisioning, configuring, or managing any servers or clusters. It also handles scaling, patching, and security automatically. References:
* AWS Glue
* [AWS Glue Data Catalog]
* [AWS Glue Developer Guide]
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
NEW QUESTION # 202
A company currently uses a provisioned Amazon EMR cluster that includes general purpose Amazon EC2 instances. The EMR cluster uses EMR managed scaling between one to five task nodes for the company's long-running Apache Spark extract, transform, and load (ETL) job. The company runs the ETL job every day.
When the company runs the ETL job, the EMR cluster quickly scales up to five nodes. The EMR cluster often reaches maximum CPU usage, but the memory usage remains under 30%.
The company wants to modify the EMR cluster configuration to reduce the EMR costs to run the daily ETL job.
Which solution will meet these requirements MOST cost-effectively?
Answer: B
Explanation:
The company's Apache Spark ETL job on Amazon EMR uses high CPU but low memory, meaning that compute-optimized EC2 instances would be the most cost-effective choice. These instances are designed for high-performance compute applications, where CPU usage is high, but memory needs are minimal, which is exactly the case here.
Compute Optimized Instances:
Compute-optimized instances, such as the C5 series, provide a higher ratio of CPU to memory, which is more suitable for jobs with high CPU usage and relatively low memory consumption.
Switching from general-purpose EC2 instances to compute-optimized instances can reduce costs while improving performance, as these instances are optimized for workloads like Spark jobs that perform a lot of computation.
Reference:
Managed Scaling: The EMR cluster's scaling is currently managed between 1 and 5 nodes, so changing the instance type will leverage the current scaling strategy but optimize it for the workload.
Alternatives Considered:
A (Increase task nodes to 10): Increasing the number of task nodes would increase costs without necessarily improving performance. Since memory usage is low, the bottleneck is more likely the CPU, which compute-optimized instances can handle better.
B (Memory optimized instances): Memory-optimized instances are not suitable since the current job is CPU-bound, and memory usage remains low (under 30%).
D (Reduce scaling cooldown): This could marginally improve scaling speed but does not address the need for cost optimization and improved CPU performance.
Amazon EMR Cluster Optimization
Compute Optimized EC2 Instances
NEW QUESTION # 203
A company has a gaming application that stores data in Amazon DynamoDB tables. A data engineer needs to ingest the game data into an Amazon OpenSearch Service cluster. Data updates must occur in near real time.
Which solution will meet these requirements?
Answer: B
Explanation:
Problem Analysis:
The company uses DynamoDB for gaming data storage and needs to ingest data into Amazon OpenSearch Service in near real time.
Data updates must propagate quickly to OpenSearch for analytics or search purposes.
Key Considerations:
DynamoDB Streams provide near-real-time capture of table changes (inserts, updates, and deletes).
Integration with AWS Lambda allows seamless processing of these changes.
OpenSearch offers APIs for indexing and updating documents, which Lambda can invoke.
Solution Analysis:
Option A: Step Functions with Periodic Export
Not suitable for near-real-time updates; introduces significant latency.
Operationally complex to manage periodic exports and S3 data ingestion.
Option B: AWS Glue Job
AWS Glue is designed for ETL workloads but lacks real-time processing capabilities.
Option C: DynamoDB Streams + Lambda
DynamoDB Streams capture changes in near real time.
Lambda can process these streams and use the OpenSearch API to update the index.
This approach provides low latency and seamless integration with minimal operational overhead.
Option D: Custom OpenSearch Plugin
Writing a custom plugin adds complexity and is unnecessary with existing AWS integrations.
Implementation Steps:
Enable DynamoDB Streams for the relevant DynamoDB tables.
Create a Lambda function to process stream records:
Parse insert, update, and delete events.
Use OpenSearch APIs to index or update documents based on the event type.
Set up a trigger to invoke the Lambda function whenever there are changes in the DynamoDB Stream.
Monitor and log errors for debugging and operational health.
Reference:
Amazon DynamoDB Streams Documentation
AWS Lambda and DynamoDB Integration
Amazon OpenSearch Service APIs
NEW QUESTION # 204
A company that operates globally must follow regulations that require data from an AWS Region to be accessible only within that Region.
A data engineer is creating a data pipeline that will create resources in the Region where the data engineer works. The data pipeline should have access to data only from the Region where the data engineer works. The pipeline uses Active Directory as an identity and authentication system. The pipeline uses a custom identity broker application to verify that employees are signed in to Active Directory and to obtain temporary credentials by using the AssumeRole API operation.
Which solution will meet the locality requirements with the LEAST administrative effort?
Answer: A
Explanation:
Option A minimizes administration because it keeps one base IAM role for the pipeline and enforces regional locality at session time by attaching a session policy when the broker calls AssumeRole. This aligns with the security best practice of applying only what is necessary, because permissions should be limited to the exact scope required for the session. The material explicitly emphasizes the principle of least privilege, stating that permissions "should apply only to what is necessary," and the most appropriate approach is to grant only the minimum actions needed for the job.
With session policies, the organization avoids creating and maintaining separate per-Region roles (Option B), avoids managing users and group memberships for federated sessions (Option C), and avoids individually attaching policies per engineer (Option D). Instead, the broker can dynamically apply a Region-specific restriction for each login, ensuring the temporary credentials are valid only for the engineer's working Region. This design reduces ongoing operational burden while still enforcing strict locality controls through temporary, scoped credentials and least-privilege boundaries.
NEW QUESTION # 205
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