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| Section | Weight | Objectives |
|---|---|---|
| Data Operations and Support | 22% | - Manage and troubleshoot data processes
|
| Data Store Management | 26% | - Manage data lifecycle
|
| Data Ingestion and Transformation | 34% | - Orchestrate data pipelines
|
| Data Security and Governance | 18% | - Manage data privacy and compliance
|
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NEW QUESTION # 52
A company uses a variety of AWS and third-party data stores. The company wants to consolidate all the data into a central data warehouse to perform analytics. Users need fast response times for analytics queries.
The company uses Amazon QuickSight in direct query mode to visualize the data. Users normally run queries during a few hours each day with unpredictable spikes.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: C
Explanation:
Problem Analysis:
The company requires a centralized data warehouse for consolidating data from various sources.
They use Amazon QuickSight in direct query mode, necessitating fast response times for analytical queries.
Users query the data intermittently, with unpredictable spikes during the day.
Operational overhead should be minimal.
Key Considerations:
The solution must support fast, SQL-based analytics.
It must handle unpredictable spikes efficiently.
Must integrate seamlessly with QuickSight for direct querying.
Minimize operational complexity and scaling concerns.
Solution Analysis:
Option A: Amazon Redshift Serverless
Redshift Serverless eliminates the need for provisioning and managing clusters.
Automatically scales compute capacity up or down based on query demand.
Reduces operational overhead by handling performance optimization.
Fully integrates with Amazon QuickSight, ensuring low-latency analytics.
Reduces costs as it charges only for usage, making it ideal for workloads with intermittent spikes.
Option B: Amazon Athena with S3 (Apache Parquet)
Athena supports querying data directly from S3 in Parquet format.
While it's cost-effective, performance depends on the size and complexity of the data.
It is not optimized for high-speed analytics needed by QuickSight in direct query mode.
Option C: Amazon Redshift Provisioned Clusters
Requires manual cluster provisioning, scaling, and maintenance.
Higher operational overhead compared to Redshift Serverless.
Option D: Amazon Aurora PostgreSQL
Aurora is optimized for transactional databases, not data warehousing or analytics.
Does not meet the requirement for fast analytics queries.
Final Recommendation:
Amazon Redshift Serverless is the best choice for this use case because it provides fast analytics, integrates natively with QuickSight, and minimizes operational complexity while efficiently handling unpredictable spikes.
Reference:
Amazon Redshift Serverless Overview
Amazon QuickSight and Redshift Integration
NEW QUESTION # 53
A company is planning to migrate on-premises Apache Hadoop clusters to Amazon EMR. The company also needs to migrate a data catalog into a persistent storage solution.
The company currently stores the data catalog in an on-premises Apache Hive metastore on the Hadoop clusters. The company requires a serverless solution to migrate the data catalog.
Which solution will meet these requirements MOST cost-effectively?
Answer: D
Explanation:
AWS Database Migration Service (AWS DMS) is a service that helps you migrate databases to AWS quickly and securely. You can use AWS DMS to migrate the Hive metastore from the on-premises Hadoop clusters into Amazon S3, which is a highlyscalable, durable, and cost-effective object storage service. AWS Glue Data Catalog is a serverless, managed service that acts as a central metadata repository for your data assets. You can use AWS Glue Data Catalog to scan the Amazon S3 bucket that contains the migrated Hive metastore and create a data catalog that is compatible with Apache Hive and other AWS services. This solution meets the requirements of migrating the data catalog into a persistent storage solution and using a serverless solution.
This solution is also the most cost-effective, as it does not incur any additional charges for running Amazon EMR or Amazon Aurora MySQL clusters. The other options are either not feasible or not optimal.
Configuring a Hive metastore in Amazon EMR (option B) or an external Hive metastore in Amazon EMR (option C) would require running and maintaining Amazon EMR clusters, which would incur additional costs and complexity. Using Amazon Aurora MySQL to store the company's data catalog (option C) would also incur additional costs and complexity, as well as introduce compatibility issues with Apache Hive.
Configuring a new Hive metastore in Amazon EMR (option D) would not migrate the existing data catalog, but create a new one, which would result in data loss and inconsistency. References:
Using AWS Database Migration Service
Populating the AWS Glue Data Catalog
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 4: Data Analysis and Visualization, Section 4.2: AWS Glue Data Catalog
NEW QUESTION # 54
A data engineer must orchestrate a data pipeline that consists of one AWS Lambda function and one AWS Glue job. The solution must integrate with AWS services.
Which solution will meet these requirements with the LEAST management overhead?
Answer: B
Explanation:
AWS Step Functions is a service that allows you to coordinate multiple AWS services into serverless workflows. You can use Step Functions to create state machines that define the sequence and logic of the tasks in your workflow. Step Functions supports various types of tasks, such as Lambda functions, AWS Glue jobs, Amazon EMR clusters, Amazon ECS tasks, etc. You can use Step Functions to monitor and troubleshoot your workflows, as well as to handle errors and retries.
Using an AWS Step Functions workflow that includes a state machine to run the Lambda function and then the AWS Glue job will meet the requirements with the least management overhead, as it leverages the serverless and managed capabilities of Step Functions. You do not need to write any code to orchestrate the tasks in your workflow, as you can use the Step Functions console or the AWS Serverless Application Model (AWS SAM) to define and deploy your state machine. You also do not need to provision or manage any servers or clusters, as Step Functions scales automatically based on the demand.
The other options are not as efficient as using an AWS Step Functions workflow. Using an Apache Airflow workflow that is deployed on an Amazon EC2 instance or on Amazon Elastic Kubernetes Service (Amazon EKS) will require more management overhead, as you will need to provision, configure, and maintain the EC2 instance or the EKS cluster, as well as the Airflow components. You will also need to write and maintain the Airflow DAGs to orchestrate the tasks in your workflow. Using an AWS Glue workflow to run the Lambda function and then the AWS Glue job will not work, as AWS Glue workflows only support AWS Glue jobs and crawlers as tasks, not Lambda functions. Reference:
AWS Step Functions
AWS Glue
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 6: Data Integration and Transformation, Section 6.3: AWS Step Functions
NEW QUESTION # 55
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 AmazonTimestream 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
4: Analyze data in Amazon Timestream using Grafana
5: Amazon Kinesis Data Firehose
6: Amazon Aurora
7: AWS Glue Bookmarks
8: AWS Glue Job and Crawler Scheduling
NEW QUESTION # 56
A data engineer must implement Amazon Redshift Serverless as a data warehouse for a company. The data engineer needs to integrate multiple Amazon Aurora MySQL databases into Amazon Redshift. The solution must maintain near real-time latency and minimize infrastructure management as much as possible.
Which solution will meet these requirements?
Answer: D
Explanation:
Amazon Aurora zero-ETL integration with Amazon Redshift Serverless is designed specifically to provide near real-time analytics on transactional data with minimal operational overhead. This integration continuously replicates changes from Aurora MySQL databases into Redshift Serverless without requiring data pipelines, replication tasks, or infrastructure management.
Because the requirement emphasizes near real-time latency and minimal management, zero-ETL integration is the optimal choice. It eliminates the need to configure and monitor AWS DMS tasks, manage Glue jobs, or build and scale Lambda-based ingestion logic. Schema changes, data synchronization, and replication are handled automatically by AWS.
AWS DMS Serverless, while managed, still requires task configuration and monitoring and typically introduces more latency than zero-ETL integrations. AWS Glue jobs and Lambda functions require custom code, scheduling, and error handling, increasing both complexity and maintenance effort.
Therefore, setting up Aurora zero-ETL integration with Amazon Redshift Serverless best satisfies the requirements and aligns with AWS best practices for modern analytics architectures.
NEW QUESTION # 57
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