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| Section | Weight | Objectives |
|---|---|---|
| Data Store Management | 26% | - Optimize storage performance and cost - Select appropriate data storage solutions |
| Data Security and Governance | 18% | - Implement data security controls - Apply governance and compliance best practices |
| Data Operations and Support | 22% | - Troubleshoot data workflow issues - Monitor and maintain data pipelines |
| Data Ingestion and Transformation | 34% | - Build and manage data pipelines - Ingest and transform data using AWS services |
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NEW QUESTION # 156
A company wants to analyze sales records that the company stores in a MySQL database. The company wants to correlate the records with sales opportunities identified by Salesforce.
The company receives 2 GB erf sales records every day. The company has 100 GB of identified sales opportunities. A data engineer needs to develop a process that will analyze and correlate sales records and sales opportunities. The process must run once each night.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: C
Explanation:
* Problem Analysis:
* The company processes 2 GB of daily sales records and 100 GB of Salesforce sales opportunities.
* The goal is to analyze and correlate the two datasets with low operational overhead.
* The process must run once nightly.
* Key Considerations:
* Amazon AppFlow simplifies data integration with Salesforce.
* AWS Glue can extract data from MySQL and perform ETL operations.
* Step Functions can orchestrate workflows with minimal manual intervention.
* Apache Airflow and Flink add complexity, which conflicts with the requirement for low operational overhead.
* Solution Analysis:
* Option A: MWAA + Lambda + Step Functions
* Requires custom Lambda code for dataset correlation, increasing development and operational complexity.
* Option B: AppFlow + Glue + MWAA
* MWAA adds orchestration overhead compared to the simpler Step Functions.
* Option C: AppFlow + Glue + Step Functions
* AppFlow fetches Salesforce data, Glue extracts MySQL data, and Step Functions orchestrate the entire process.
* Minimal setup and operational overhead, making it the best choice.
* Option D: AppFlow + Kinesis + Flink + Step Functions
* Using Kinesis and Flink for batch processing introduces unnecessary complexity.
* Final Recommendation:
* Use Amazon AppFlow to fetch Salesforce data, AWS Glue to process MySQL data, and Step Functions for orchestration.
:
Amazon AppFlow Overview
AWS Glue ETL Documentation
AWS Step Functions
NEW QUESTION # 157
A company needs to implement a data mesh architecture for trading, risk, and compliance teams. Each team has its own data but needs to share views. They have 1,000+ tables in 50 Glue databases. All teams use Athena and Redshift, and compliance requires full auditing and PII access control.
Answer: A
Explanation:
A data mesh approach in AWS typically uses Lake Formation for domain-level access control and Athena for cross-domain querying through federated views. CloudTrail ensures auditing.
"For data mesh architectures, use AWS Lake Formation for fine-grained access control and Athena views for cross-domain analysis. Enable CloudTrail to audit access."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf This ensures scalability, security, and compliance across domains.
NEW QUESTION # 158
A data engineer must build an extract, transform, and load (ETL) pipeline to process and load data from 10 source systems into 10 tables that are in an Amazon Redshift database. All the source systems generate .csv, JSON, or Apache Parquet files every 15 minutes. The source systems all deliver files into one Amazon S3 bucket. The file sizes range from 10 MB to 20 GB. The ETL pipeline must function correctly despite changes to the data schema.
Which data pipeline solutions will meet these requirements? (Choose two.)
Answer: B,C
Explanation:
Using an Amazon EventBridge rule to run an AWS Glue job or invoke an AWS Glue workflow job every 15 minutes are two possible solutions that will meet the requirements. AWS Glue is a serverless ETL service that can process and load data from various sources to various targets, including Amazon Redshift. AWS Glue can handle different data formats, such as CSV, JSON, and Parquet, and also support schema evolution, meaning it can adapt to changes in the data schema over time. AWS Glue can also leverage Apache Spark to perform distributed processing and transformation of large datasets. AWS Glue integrates with Amazon EventBridge, which is a serverless event bus service that can trigger actions based on rules and schedules. By using an Amazon EventBridge rule, you can invoke an AWS Glue job or workflow every 15 minutes, and configure the job or workflow to run an AWS Glue crawler and then load the data into the Amazon Redshift tables. This way, you can build a cost-effective and scalable ETL pipeline that can handle data from 10 source systems and function correctly despite changes to the data schema.
The other options are not solutions that will meet the requirements. Option C, configuring an AWS Lambda function to invoke an AWS Glue crawler when a file is loaded into the S3 bucket, and creating a second Lambda function to run the AWS Glue job, is not a feasible solution, as it would require a lot of Lambda invocations and coordination. AWS Lambda has some limits on the execution time, memory, and concurrency, which can affect the performance and reliability of the ETL pipeline. Option D, configuring an AWS Lambda function to invoke an AWS Glue workflow when a file is loaded into the S3 bucket, is not a necessary solution, as you can use an Amazon EventBridge rule to invoke the AWS Glue workflow directly, without the need for a Lambda function. Option E, configuring an AWS Lambda function to invoke an AWS Glue job when a file is loaded into the S3 bucket, and configuring the AWS Glue job to put smaller partitions of the DataFrame into an Amazon Kinesis Data Firehose delivery stream, is not a cost-effective solution, as it would incur additional costs for Lambda invocations and data delivery. Moreover, using Amazon Kinesis Data Firehose to load data into Amazon Redshift is not suitable for frequent and small batches of data, as it can cause performance issues and data fragmentation. Reference:
AWS Glue
Amazon EventBridge
Using AWS Glue to run ETL jobs against non-native JDBC data sources
[AWS Lambda quotas]
[Amazon Kinesis Data Firehose quotas]
NEW QUESTION # 159
A data engineer configured an AWS Glue Data Catalog for data that is stored in Amazon S3 buckets. The data engineer needs to configure the Data Catalog to receive incremental updates.
The data engineer sets up event notifications for the S3 bucket and creates an Amazon Simple Queue Service (Amazon SQS) queue to receive the S3 events.
Which combination of steps should the data engineer take to meet these requirements with LEAST operational overhead? (Select TWO.)
Answer: C,E
Explanation:
The requirement is to update the AWS Glue Data Catalog incrementally based on S3 events. Using an S3 event-based approach is the most automated and operationally efficient solution.
A . Create an S3 event-based AWS Glue crawler:
An event-based Glue crawler can automatically update the Data Catalog when new data arrives in the S3 bucket. This ensures incremental updates with minimal operational overhead.
Reference:
C . Use an AWS Lambda function to directly update the Data Catalog:
Lambda can be triggered by S3 events delivered to the SQS queue and can directly update the Glue Data Catalog, ensuring that new data is reflected in near real-time without running a full crawler.
Alternatives Considered:
B (Time-based schedule): Scheduling a crawler to run periodically adds unnecessary latency and operational overhead.
D (Manual crawler initiation): Manually starting the crawler defeats the purpose of automation.
E (AWS Step Functions): Step Functions add complexity that is not needed when Lambda can handle the updates directly.
AWS Glue Event-Driven Crawlers
Using AWS Lambda to Update Glue Catalog
NEW QUESTION # 160
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: D
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 .
References:
* 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 # 161
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