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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Operations and Support | 22% | - Troubleshoot data workflow issues - Monitor and maintain data pipelines |
| Topic 2: Data Security and Governance | 18% | - Implement data security controls - Apply governance and compliance best practices |
| Topic 3: Data Store Management | 26% | - Optimize storage performance and cost - Select appropriate data storage solutions |
| Topic 4: Data Ingestion and Transformation | 34% | - Ingest and transform data using AWS services - Build and manage data pipelines |
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NEW QUESTION # 147
A data engineer develops an AWS Glue Apache Spark ETL job to perform transformations on a dataset.
When the data engineer runs the job, the job returns an error that reads, "No space left on device." The data engineer needs to identify the source of the error and provide a solution.
Which combinations of steps will meet this requirement MOST cost-effectively? (Select TWO.)
Answer: B,D
Explanation:
A "No space left on device" error typically results fromdata skeworlarge shuffle stages. The best actions are:
* B. Monitor using Spark UI and Glue metricsto find skewed partitions or executor issues.
* D. Use --write-shuffle-files-to-s3to offload intermediate data to S3 instead of local disk, andapply saltingto reduce skew.
"You can reduce the impact of data skew and large shuffle operations by monitoring with Spark UI and enabling the --write-shuffle-files-to-s3 option. Salting can help rebalance the skewed keys."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf Scaling out workers (A, C) is more costly and less efficient if the root cause (skew) is not fixed.
NEW QUESTION # 148
A company currently uses a provisioned Amazon EMR cluster that includes general purpose Amazon EC2 instances. The EMR cluster uses EMR managed scaling betweenone 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: A
Explanation:
The company's Apache Spark ETL job on Amazon EMR uses high CPU but low memory, meaning that compute-optimized EC2 instanceswould 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 canreduce costs while improving performance, as these instances are optimized for workloads like Spark jobs that perform a lot of computation.
Reference:Amazon EC2 Compute Optimized Instances
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 usageis 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.
References:
Amazon EMR Cluster Optimization
Compute Optimized EC2 Instances
NEW QUESTION # 149
A retail company needs to implement a solution to capture data updates from multiple Amazon Aurora MySQL databases. The company needs to make the updates available for analytics in near real time. The solution must be serverless and require minimal maintenance.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: C
Explanation:
Comprehensive and Detailed Explanation (150-250 words)
Aurora zero-ETL integration with Amazon Redshift Serverless is specifically designed to provide near real- time analytics on Aurora transactional data without the need to build or manage data pipelines. This integration continuously replicates data changes from Aurora MySQL into Redshift Serverless automatically.
The solution is fully serverless, requires no infrastructure provisioning, and eliminates the need for schema conversion, replication tasks, or streaming frameworks. Data is kept in sync with low latency, making it immediately available for analytical queries in Redshift Serverless.
AWS DMS-based solutions require task configuration, monitoring, and maintenance. Amazon MSK with Debezium introduces significant operational complexity and is not serverless. Kinesis-based replication pipelines require additional services and ongoing operational management.
Aurora zero-ETL integration is the lowest-maintenance, highest-efficiency solution and is explicitly recommended by AWS for this use case.
NEW QUESTION # 150
A company uses Amazon Redshift as its data warehouse. Data encoding is applied to the existing tables of the data warehouse. A data engineer discovers that the compression encoding applied to some of the tables is not the best fit for the data.
The data engineer needs to improve the data encoding for the tables that have sub-optimal encoding.
Which solution will meet this requirement?
Answer: A
Explanation:
To improve data encoding for Amazon Redshift tables where sub-optimal encoding has been applied, the correct approach is to analyze the table to determine the optimal encoding based on the data distribution and characteristics.
Option B: Run the ANALYZE COMPRESSION command against the identified tables. Manually update the compression encoding of columns based on the output of the command.
The ANALYZE COMPRESSION command in Amazon Redshift analyzes the columnar data and suggests the best compression encoding for each column. The output provides recommendations for changing the current encoding to improve storage efficiency and query performance. After analyzing, you can manually apply the recommended encoding to the columns.
Option A (ANALYZE command) is incorrect because it is primarily used to update statistics on tables, not to analyze or suggest compression encoding.
Options C and D (VACUUM commands) deal with reclaiming disk space and reorganizing data, not optimizing compression encoding.
Reference:
Amazon Redshift ANALYZE COMPRESSION Command
NEW QUESTION # 151
A retail company is expanding its operations globally. The company needs to use Amazon QuickSight to accurately calculate currency exchange rates for financial reports. The company has an existing dashboard that includes a visual that is based on an analysis of a dataset that contains global currency values and exchange rates.
A data engineer needs to ensure that exchange rates are calculated with a precision of four decimal places.
The calculations must be precomputed. The data engineer must materialize results in QuickSight super-fast, parallel, in-memory calculation engine (SPICE).
Which solution will meet these requirements?
Answer: A
NEW QUESTION # 152
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