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| Section | Weight | Objectives |
|---|---|---|
| Data Security and Governance | 18% | - Encrypt data at rest and in transit - Implement access control and authentication
- Enforce compliance and data governance
|
| Data Ingestion and Transformation | 34% | - Transform and enrich data
- Ingest data from various sources
|
| Data Operations and Support | 22% | - Automate operational tasks - Monitor and troubleshoot data pipelines
- Ensure reliability and scalability |
| Data Store Management | 26% | - Design and implement data storage solutions
- Optimize storage performance and cost |
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NEW QUESTION # 155
A company has five offices in different AWS Regions. Each office has its own human resources (HR) department that uses a unique IAM role. The company stores employee records in a data lake that is based on Amazon S3 storage.
A data engineering team needs to limit access to the records. Each HR department should be able to access records for only employees who are within the HR department's Region.
Which combination of steps should the data engineering team take to meet this requirement with the LEAST operational overhead? (Choose two.)
Answer: D,E
Explanation:
AWS Lake Formation is a service that helps you build, secure, and manage data lakes on Amazon S3. You can use AWS Lake Formation to register the S3 path as a data lake location, and enable fine-grained access control to limit access to the records based on the HR department's Region. You can use data filters to specify which S3 prefixes or partitions each HR department can access, and grant permissions to the IAM roles of the HR departments accordingly. This solution will meet the requirement with the least operational overhead, as it simplifies the data lake management and security, and leverages the existing IAM roles of the HR departments12.
The other options are not optimal for the following reasons:
* A. Use data filters for each Region to register the S3 paths as data locations. This option is not possible, as data filters are not used to register S3 paths as data locations, but to grant permissions to access specific S3 prefixes or partitions within a data location. Moreover, this option does not specify how to limit access to the records based on the HR department's Region.
* C. Modify the IAM roles of the HR departments to add a data filter for each department's Region. This option is not possible, as data filters are not added to IAM roles, but to permissions granted by AWS Lake Formation. Moreover, this option does not specify how to register the S3 path as a data lake location, or how to enable fine-grained access control in AWS Lake Formation.
* E. Create a separate S3 bucket for each Region. Configure an IAM policy to allow S3 access. Restrict access based on Region. This option is not recommended, as it would require more operational overhead to create and manage multiple S3 buckets, and to configure and maintain IAM policies for each HR department. Moreover, this option does not leverage the benefits of AWS Lake Formation, such as data cataloging, data transformation, and data governance.
1: AWS Lake Formation
2: AWS Lake Formation Permissions
3: AWS Identity and Access Management
4: Amazon S3
NEW QUESTION # 156
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 .
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 # 157
A transportation company wants to track vehicle movements by capturing geolocation records. The records are 10 bytes in size. The company receives up to 10,000 records every second. Data transmission delays of a few minutes are acceptable because of unreliable network conditions.
The transportation company wants to use Amazon Kinesis Data Streams to ingest the geolocation data. The company needs a reliable mechanism to send data to Kinesis Data Streams. The company needs to maximize the throughput efficiency of the Kinesis shards.
Which solution will meet these requirements in the MOST operationally efficient way?
Answer: B
Explanation:
* Problem Analysis:
* The company ingests geolocation records (10 bytes each) at 10,000 records per second into Kinesis Data Streams.
* Data transmission delays are acceptable, but the solution must maximize throughput efficiency.
* Key Considerations:
* TheKinesis Producer Library (KPL)batches records and uses aggregation to optimize shard throughput.
* Efficiently handles high-throughput scenarios with minimal operational overhead.
* Solution Analysis:
* Option A: Kinesis Agent
* Designed for file-based ingestion; not optimized for geolocation records.
* Option B: KPL
* Aggregates records into larger payloads, significantly improving shard throughput.
* Suitable for applications generating small, high-frequency records.
* Option C: Kinesis Firehose
* Firehose is for delivery to destinations like S3 or Redshift and is not optimized for direct ingestion to Kinesis Data Streams.
* Option D: Kinesis SDK
* The SDK lacks advanced features like aggregation, resulting in lower throughput efficiency.
* Final Recommendation:
* UseKinesis Producer Library (KPL)for its built-in aggregation and batching capabilities.
:
Kinesis Producer Library (KPL) Overview
Best Practices for Amazon Kinesis
NEW QUESTION # 158
A data engineer is troubleshooting an AWS Glue workflow that occasionally fails. The engineer determines that the failures are a result of data quality issues. A business reporting team needs to receive an email notification any time the workflow fails in the future.
Which solution will meet this requirement?
Answer: C
NEW QUESTION # 159
A retail company uses an Amazon Redshift data warehouse and an Amazon S3 bucket. The company ingests retail order data into the S3 bucket every day.
The company stores all order data at a single path within the S3 bucket. The data has more than 100 columns.
The company ingests the order data from a third-party application that generates more than 30 files in CSV format every day. Each CSV file is between 50 and 70 MB in size.
The company uses Amazon Redshift Spectrum to run queries that select sets of columns. Users aggregate metrics based on daily orders. Recently, users have reported that the performance of the queries has degraded.
A data engineer must resolve the performance issues for the queries.
Which combination of steps will meet this requirement with LEAST developmental effort? (Select TWO.)
Answer: A,D
Explanation:
The performance issue in Amazon Redshift Spectrum queries arises due to the nature of CSV files, which are row-based storage formats. Spectrum is more optimized for columnar formats, which significantly improve performance by reducing the amount of data scanned. Also, partitioning data based on relevant columns like order date can further reduce the amount of data scanned, as queries can focus only on the necessary partitions.
* A. Configure the third-party application to create the files in a columnar format:
* Columnar formats (like Parquet or ORC) store data in a way that is optimized for analytical queries because they allow queries to scan only the columns required, rather than scanning all columns in a row-based format like CSV.
* Amazon Redshift Spectrum works much more efficiently with columnar formats, reducing the amount of data that needs to be scanned, which improves query performance.
NEW QUESTION # 160
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