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| Section | Weight | Objectives |
|---|---|---|
| Data Security and Governance | 18% | - Implement access control and authentication
- Encrypt data at rest and in transit - Enforce compliance and data governance
|
| Data Operations and Support | 22% | - Ensure reliability and scalability - Automate operational tasks - Backup, restore, and disaster recovery - Monitor and troubleshoot data pipelines
|
| Data Ingestion and Transformation | 34% | - Transform and enrich data
|
| Data Store Management | 26% | - Optimize storage performance and cost - Design and implement data storage solutions
|
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NEW QUESTION # 118
A data engineer is building a data pipeline. A large data file is uploaded to an Amazon S3 bucket once each day at unpredictable times. An AWS Glue workflow uses hundreds of workers to process the fileand load the data into Amazon Redshift. The company wants to process the file as quickly as possible.
Which solution will meet these requirements?
Answer: C
Explanation:
The best solution for fast, event-driven processing of unpredictable file uploads is to useS3 event notifications
,CloudTrail, andEventBridgeto automatically trigger the AWS Glue workflow:
"You can configure S3 PutObject events to be captured by CloudTrail and forwarded through EventBridge to trigger an AWS Glue job or workflow. This allows Glue to begin processing as soon as the file arrives, with minimal latency."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf This option provides the lowest latency and least manual overhead compared to polling or scheduling solutions.
NEW QUESTION # 119
A technology company currently uses Amazon Kinesis Data Streams to collect log data in real time. The company wants to use Amazon Redshift for downstream real-time queries and to enrich the log data.
Which solution will ingest data into Amazon Redshift with the LEAST operational overhead?
Answer: A
Explanation:
The most efficient and low-operational-overhead solution for ingesting data into Amazon Redshift from Amazon Kinesis Data Streams is to useAmazon Redshift streaming ingestion. This feature allows Redshift to directly ingest streaming data from Kinesis Data Streams and process it in real-time.
* Amazon Redshift Streaming Ingestion:
* Redshift supports native streaming ingestion from Kinesis Data Streams, allowing real-time data to be queried usingmaterialized views.
* This solution reduces operational complexity because you don't need intermediary services like Amazon Kinesis Data Firehose or S3 for batch loading.
Reference:Amazon Redshift Streaming Ingestion
Alternatives Considered:
A (Data Firehose to Redshift): This option is more suitable for batch processing but incurs additional operational overhead with the Firehose setup.
B (Firehose to S3): This involves an intermediate step, which adds complexity and delays the real-time requirement.
C (Managed Service for Apache Flink): This would work but introduces unnecessary complexity compared to Redshift's native streaming ingestion.
References:
Amazon Redshift Streaming Ingestion from Kinesis
Materialized Views in Redshift
NEW QUESTION # 120
A manufacturing company collects sensor data from its factory floor to monitor and enhance operational efficiency. The company uses Amazon Kinesis Data Streams to publish the data that the sensors collect to a data stream. Then Amazon Kinesis Data Firehose writes the data to an Amazon S3 bucket.
The company needs to display a real-time view of operational efficiency on a large screen in the manufacturing facility.
Which solution will meet these requirements with the LOWEST latency?
Answer: B
Explanation:
This solution will meet the requirements with the lowest latency because it uses Amazon Managed Service for Apache Flink to process the sensor data in real time and write it to Amazon Timestream, a fast, scalable, and serverless time series database. Amazon Timestream is optimized for storing and analyzing time series data, such as sensor data, and can handle trillions of events per day with millisecond latency. By using Amazon Timestream as a source, you can create an Amazon QuickSight dashboard that displays a real-time view of operational efficiency on a large screen in the manufacturing facility. Amazon QuickSight is a fully managed business intelligence service that can connect to various data sources, including Amazon Timestream, and provide interactive visualizations and insights123.
The other options are not optimal for the following reasons:
A. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Use a connector for Apache Flink to write data to an Amazon Timestream database.
Use the Timestream database as a source to create a Grafana dashboard. This option is similar to option C, but it uses Grafana instead of Amazon QuickSight to create the dashboard. Grafana is an open source visualization tool that can also connect to Amazon Timestream, but it requires additional steps to set up and configure, such as deploying a Grafana server on Amazon EC2, installing the Amazon Timestream plugin, and creating an IAM role for Grafana to access Timestream. These steps can increase the latency and complexity of the solution.
B. Configure the S3 bucket to send a notification to an AWS Lambda function when any new object is created. Use the Lambda function to publish the data to Amazon Aurora. Use Aurora as a source to create an Amazon QuickSight dashboard. This option is not suitable for displaying a real-time view of operational efficiency, as it introduces unnecessary delays and costs in the data pipeline. First, the sensor data is written to an S3 bucket by Amazon Kinesis Data Firehose, which can have a buffering interval of up to 900 seconds.
Then, the S3 bucket sends a notification to a Lambda function, which can incur additional invocation and execution time. Finally, the Lambda function publishes the data to Amazon Aurora, a relational database that is not optimized for time series data and can have higher storage and performance costs than Amazon Timestream .
D. Use AWS Glue bookmarks to read sensor data from the S3 bucket in real time. Publish the data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard. This option is also not suitable for displaying a real-time view of operational efficiency, as it uses AWS Glue bookmarks to read sensor data from the S3 bucket. AWS Glue bookmarks are a feature that helps AWS Glue jobs and crawlers keep track of the data that has already been processed, so that they can resume from where they left off. However, AWS Glue jobs and crawlers are not designed for real-time data processing, as they can have a minimum frequency of 5 minutes and a variable start-up time. Moreover, this option also uses Grafana instead of Amazon QuickSight to create the dashboard, which can increase the latency and complexity of the solution .
1: Amazon Managed Streaming for Apache Flink
2: Amazon Timestream
3: Amazon QuickSight
Analyze data in Amazon Timestream using Grafana
Amazon Kinesis Data Firehose
Amazon Aurora
AWS Glue Bookmarks
AWS Glue Job and Crawler Scheduling
NEW QUESTION # 121
A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling.
Which solution will meet this requirement?
Answer: D
Explanation:
Concurrency scaling is a feature that allows you to support thousands of concurrent users and queries, with consistently fast query performance. When you turn on concurrency scaling, Amazon Redshift automatically adds query processing power in seconds to process queries without any delays. You can manage which queries are sent to the concurrency-scaling cluster by configuring WLM queues. To turn on concurrency scaling for a queue, set the Concurrency Scaling mode value to auto. The other options are either incorrect or irrelevant, as they do not enable concurrency scaling for the existing Redshift cluster on RA3 nodes. References:
Working with concurrency scaling - Amazon Redshift
Amazon Redshift Concurrency Scaling - Amazon Web Services
Configuring concurrency scaling queues - Amazon Redshift
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 6, page 163)
NEW QUESTION # 122
An ecommerce company processes millions of orders each day. The company uses AWS Glue ETL to collect data from multiple sources, clean the data, and store the data in an Amazon S3 bucket in CSV format by using the S3 Standard storage class. The company uses the stored data to conduct daily analysis.
The company wants to optimize costs for data storage and retrieval.
Which solution will meet this requirement?
Answer: C
Explanation:
Apache Parquet is a columnar storage format that is much more space-efficient than row-based formats like CSV, especially for analytics workloads. Transforming data from CSV to Parquet significantly reduces storage costs and improves query performance. According to the study guide:
"Parquet is a columnar storage file format that is optimized for use with analytics workloads, providing efficient storage and fast query performance."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf By switching to Parquet, the company can reduce both storage size and retrieval times, making it the optimal choice for cost-effective data analysis.
NEW QUESTION # 123
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