| Question.11 A data engineer needs Amazon Athena queries to finish faster. The data engineer notices that all the files the Athena queries use are currently stored in uncompressed .csv format. The data engineer also notices that users perform most queries by selecting a specific column. Which solution will MOST speed up the Athena query performance? (A)Change the data format from .csv to JSON format. Apply Snappy compression. (B)Compress the .csv files by using Snappy compression. (C)Change the data format from .csv to Apache Parquet. Apply Snappy compression. (D) Compress the .csv files by using gzip compression. |
11. Click here to View Answer
Answer: C
Explanation:
The correct answer is C because it combines columnar storage and compression, which are both highly effective in optimizing Athena query performance, especially given the scenario described.
Here’s a detailed justification:
- Columnar Storage (Parquet): Athena works best with columnar storage formats like Apache Parquet. In a columnar format, data for each column is stored contiguously. When users query a specific column, Athena only needs to read the data for that column, significantly reducing I/O operations compared to row-oriented formats like CSV. This is especially relevant because the data engineer observed that users primarily select specific columns.
- Compression (Snappy): Compression reduces the size of the data that needs to be read from storage, leading to faster query execution. Snappy compression is known for its high speed and is a good compromise between compression ratio and processing speed. Applying Snappy compression alongside Parquet ensures efficient storage and retrieval of data.
- Why other options are less optimal:
- A (JSON and Snappy): JSON is a row-oriented format. It doesn’t offer the same performance benefits as columnar storage when querying specific columns.
- B (CSV and Snappy): Compressing CSV files will reduce the storage footprint and improve read speeds to some extent, but it doesn’t address the inefficiencies of the row-oriented CSV format when querying specific columns.
- D (CSV and Gzip): Gzip provides better compression than Snappy, but it’s generally slower. While it would reduce storage costs further, the improved compression comes at the cost of processing speed, which is less desirable than Parquet + Snappy. More importantly, it does not address the fundamental performance issue of a row-oriented format.
In summary, changing the data format to Apache Parquet allows Athena to efficiently read only the necessary columns. Applying Snappy compression reduces data volume, further accelerating query performance. Therefore, converting to Parquet and applying Snappy compression is the most effective approach.
Authoritative Links:
- Amazon Athena Best Practices: https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/
- Apache Parquet: https://parquet.apache.org/
- Snappy Compression: https://github.com/google/snappy
| Question.12 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? (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. (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. (C)Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Create a new Data Firehose delivery stream to publish data directly to an Amazon Timestream database. Use the Timestream database as a source to create an Amazon QuickSight dashboard. (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. |
12. Click here to View Answer
Answer: A
Explanation:
The most suitable solution for displaying real-time operational efficiency data with the lowest latency involves a combination of Flink, Timestream, and Grafana. Here’s a detailed justification:
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.
- Lowest Latency Processing: Apache Flink excels at real-time stream processing. Using Flink to analyze sensor data directly from Kinesis Data Streams minimizes delays compared to batch-oriented approaches or triggering processes upon file creation in S3. It processes data as it arrives.
- Time Series Database: Amazon Timestream is specifically designed for time-series data, making it ideal for storing sensor data collected over time. Its optimized for high ingestion rates and fast queries on time-based data.
- Real-Time Visualization: Grafana is well-suited for visualizing time-series data from Timestream. It offers flexible dashboarding capabilities for real-time monitoring.
Why other options are less suitable:
- B. S3 notifications + Lambda + Aurora + QuickSight: This approach introduces latency due to the involvement of S3 event notifications, Lambda execution, and writing to Aurora. Aurora is a general-purpose relational database and less optimized for the characteristics of time series data.
- C. Flink + Data Firehose to Timestream + QuickSight: Although this option uses Flink and Timestream, inserting Firehose between Flink and Timestream increases latency. Firehose is designed for buffering and batching data, which goes against the need for real-time updates. Firehose introduces latency because it buffers data before writing it to the destination.
- D. Glue bookmarks + S3 + Timestream + Grafana: Glue bookmarks are not designed for real-time or near-real-time data extraction. Glue is a batch-oriented ETL service that polls for changes to the data source.
Conclusion:
Option A uses a combination of services that are specifically designed for real-time stream processing (Flink), time-series data storage (Timestream), and real-time visualization (Grafana), resulting in the lowest latency.
Authoritative Links:
Grafana: https://grafana.com/
Amazon Managed Service for Apache Flink: https://aws.amazon.com/flink/
Amazon Timestream: https://aws.amazon.com/timestream/
| Question.13 A company stores daily records of the financial performance of investment portfolios in .csv format in an Amazon S3 bucket. A data engineer uses AWS Glue crawlers to crawl the S3 data. The data engineer must make the S3 data accessible daily in the AWS Glue Data Catalog. Which solution will meet these requirements? (A) Create an IAM role that includes the AmazonS3FullAccess policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler’s data store. Create a daily schedule to run the crawler. Configure the output destination to a new path in the existing S3 bucket. (B) Create an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler’s data store. Create a daily schedule to run the crawler. Specify a database name for the output. (C) Create an IAM role that includes the AmazonS3FullAccess policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler’s data store. Allocate data processing units (DPUs) to run the crawler every day. Specify a database name for the output. (D) Create an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler’s data store. Allocate data processing units (DPUs) to run the crawler every day. Configure the output destination to a new path in the existing S3 bucket. |
13. Click here to View Answer
Answer: B
Explanation:
The correct answer is B. Here’s why:
D: While using the AWSGlueServiceRole is correct, allocating DPUs daily isn’t directly related to setting the schedule for the crawler execution. The issue is about scheduling, not about resource allocation. The crawler updates metadata, so specifying a new output path is incorrect.
AWS Glue Service Role: AWS Glue requires specific permissions to access data stores like S3 and write metadata to the AWS Glue Data Catalog. The AWSGlueServiceRole IAM policy provides these necessary permissions. Using AmazonS3FullAccess is overly permissive and violates the principle of least privilege. https://docs.aws.amazon.com/glue/latest/dg/glue-security.html
Crawler Configuration: The AWS Glue crawler needs to know the location of the data. Specifying the S3 bucket path as the crawler’s data store tells the crawler where to find the .csv files.
Scheduled Execution: The requirement is to make the data accessible daily. Scheduling the crawler to run daily ensures that any changes to the .csv files in S3 are reflected in the Glue Data Catalog.
Data Catalog Output: Specifying a database name for the output tells the crawler where to store the metadata (table definitions) discovered from the .csv files. This makes the data accessible for querying and other data processing tasks using services like Athena or Redshift Spectrum.
Why other options are incorrect:
A and C: Using AmazonS3FullAccess is overly permissive. Allocating DPUs daily isn’t directly related to scheduling; DPUs determine the computational power for the crawler. While allocating DPUs is necessary for the crawler to function, the question is about the best way to keep the catalog updated daily. Specifying a new path is also incorrect, since the crawler updates metadata.
| Question.14 A company loads transaction data for each day into Amazon Redshift tables at the end of each day. The company wants to have the ability to track which tables have been loaded and which tables still need to be loaded. A data engineer wants to store the load statuses of Redshift tables in an Amazon DynamoDB table. The data engineer creates an AWS Lambda function to publish the details of the load statuses to DynamoDB. How should the data engineer invoke the Lambda function to write load statuses to the DynamoDB table? (A) Use a second Lambda function to invoke the first Lambda function based on Amazon CloudWatch events. (B) Use the Amazon Redshift Data API to publish an event to Amazon EventBridge. Configure an EventBridge rule to invoke the Lambda function. (C) Use the Amazon Redshift Data API to publish a message to an Amazon Simple Queue Service (Amazon SQS) queue. Configure the SQS queue to invoke the Lambda function. (D) Use a second Lambda function to invoke the first Lambda function based on AWS CloudTrail events. |
14. Click here to View Answer
Answer: B
Explanation:
Here’s a detailed justification for why option B is the most appropriate solution for invoking the Lambda function and updating the DynamoDB table with Redshift load statuses:
Option B: Use the Amazon Redshift Data API to publish an event to Amazon EventBridge. Configure an EventBridge rule to invoke the Lambda function.
This approach offers a loosely coupled, event-driven architecture that is highly scalable and maintainable. Here’s a breakdown of why this is the best solution:
- Redshift Data API for Event Emission: The Redshift Data API enables you to interact with Redshift clusters programmatically without the need for direct JDBC/ODBC connections within your Lambda function. Critically, it can be configured to publish events on command completion. This allows Redshift to signal when a table load operation completes, making it an ideal trigger for our workflow.
- Amazon EventBridge for Event Routing: EventBridge is a serverless event bus service that simplifies the building of event-driven applications. It allows you to define rules that match specific event patterns and route them to different targets, like Lambda functions. In this scenario, we can configure EventBridge to listen for events emitted by the Redshift Data API upon completion of a load operation for a specific table.
- Lambda Invocation by EventBridge: Once EventBridge receives an event matching our defined rule, it will automatically invoke the Lambda function. The event data (containing information about the table, load status, timestamp, etc.) will be passed to the Lambda function as an argument.
- DynamoDB Update: Inside the Lambda function, the event data can be parsed, and the corresponding load status for the specific table can be written to the DynamoDB table.
Why other options are not optimal:
- A. Use a second Lambda function to invoke the first Lambda function based on Amazon CloudWatch events. While CloudWatch can monitor Redshift, triggering a second Lambda based on metrics or logs might lead to delays or inaccuracies. Directly leveraging the Redshift Data API for events is more accurate and timely. CloudWatch events are better suited for monitoring cluster health than specific table load operations.
- C. Use the Amazon Redshift Data API to publish a message to an Amazon Simple Queue Service (Amazon SQS) queue. Configure the SQS queue to invoke the Lambda function. While this approach can also work, it introduces an extra component (SQS) when EventBridge offers a more direct event-driven solution. EventBridge provides richer event filtering and routing capabilities, making it better suited for this scenario. SQS adds complexity without significant benefit.
- D. Use a second Lambda function to invoke the first Lambda function based on AWS CloudTrail events. CloudTrail records API calls made to AWS services. It is not the intended mechanism for tracking table load statuses in Redshift. CloudTrail events can be too verbose, and relying on them for this purpose would be less reliable and more difficult to manage. Furthermore, it’s less directly related to the event of a table load completing.
In summary:
Option B leverages the strengths of the Redshift Data API for event emission, EventBridge for efficient event routing, and Lambda for serverless processing, providing a scalable, reliable, and manageable solution for updating the DynamoDB table with Redshift load statuses.
Authoritative Links:
- Amazon Redshift Data API: https://docs.aws.amazon.com/redshift-data-api/latest/APIReference/Welcome.html
- Amazon EventBridge: https://aws.amazon.com/eventbridge/
- AWS Lambda: https://aws.amazon.com/lambda/
- Amazon DynamoDB: https://aws.amazon.com/dynamodb/
| Question.15 A data engineer needs to securely transfer 5 TB of data from an on-premises data center to an Amazon S3 bucket. Approximately 5% of the data changes every day. Updates to the data need to be regularly proliferated to the S3 bucket. The data includes files that are in multiple formats. The data engineer needs to automate the transfer process and must schedule the process to run periodically. Which AWS service should the data engineer use to transfer the data in the MOST operationally efficient way? (A) AWS DataSync (B) AWS Glue (C) AWS Direct Connect (D) Amazon S3 Transfer Acceleration |
15. Click here to View Answer
Answer:A
Explanation:
Here’s a detailed justification for why AWS DataSync is the most operationally efficient service for transferring the described dataset to S3, considering the constraints:
AWS DataSync is specifically designed for efficiently and securely transferring large datasets between on-premises storage and AWS storage services like S3. Its incremental transfer capability is key here. Because only 5% of the data changes daily, DataSync can identify and transfer only the modified data, minimizing transfer time and costs. AWS Glue is more suitable for ETL (Extract, Transform, Load) operations, data cataloging, and generating code for data transformations, and isn’t optimized for bulk data transfer like this scenario.
AWS Direct Connect establishes a dedicated network connection between your on-premises environment and AWS. While it can improve network performance and security compared to transferring data over the public internet, it doesn’t provide the data transfer management features of DataSync. You’d still need a separate tool to handle the actual data transfer. Amazon S3 Transfer Acceleration utilizes the AWS global network to accelerate transfers to S3. However, it is mainly for transferring data over the public internet and does not provide automated scheduling or incremental transfer functionalities. Therefore, DataSync handles the crucial requirements of periodic, automated, incremental data transfer.
DataSync offers built-in scheduling, allowing the engineer to automate the transfer process to run at regular intervals, which satisfies the “schedule the process to run periodically” requirement. It also handles various file formats, allowing easy handling of this variety data. DataSync also handles encryption and integrity verification during transfer, making it a secure solution. The service reduces the operational overhead significantly because it automates most aspects of data transfer.
Therefore, because DataSync automates and schedules the transfer, incrementally transfers updates, and is designed specifically to handle on-premises to cloud migrations, it is the most operationally efficient solution compared to the other options.
Reference Links:
Amazon S3 Transfer Acceleration: https://aws.amazon.com/s3/transfer-acceleration/
AWS DataSync: https://aws.amazon.com/datasync/
AWS Glue: https://aws.amazon.com/glue/
AWS Direct Connect: https://aws.amazon.com/directconnect/