A large company has a central data lake to run analytics across different departments. Each department uses a separate AWS account and stores its data in anAmazon S3 bucket in that account. Each AWS account uses the AWS Glue Data Catalog as its data catalog. There are different data lake access requirements based on roles. Associate analysts should only have read access to their departmental data. Senior data analysts can have access in multiple departments including theirs, but for a subset of columns only. Which solution achieves these required access patterns to minimize costs and administrative tasks?
Answer(s): C
A concise explanation: Lake Formation in a dedicated central data lake account provides centralized, fine-grained access control across cross-account data locations, enabling column-level permissions for senior analysts while maintaining department isolation for associates.A) Incorrect: Consolidation and migrating catalogs increases cost and operational risk; cross-account Glue permissions alone don’t provide centralized fine-grained authorization and can complicate governance.B) Incorrect: Retains per-account catalogs; cross-account Glue scanning adds complexity and potential delay in schema updates; does not inherently deliver centralized column-level controls across accounts.D) Incorrect: Blueprint moves data instead of leveraging existing cross-account permissions; still requires managing policies per bucket and lacks streamlined cross-account column-level governance across all data.C) Correct: Lake Formation centralizes cataloging, cross-account location management, and fine-grained table/column permissions via service-linked role, meeting access patterns with minimized administration and cost.
A company wants to improve user satisfaction for its smart home system by adding more features to its recommendation engine. Each sensor asynchronously pushes its nested JSON data into Amazon Kinesis Data Streams using the Kinesis Producer Library (KPL) in Java. Statistics from a set of failed sensors showed that, when a sensor is malfunctioning, its recorded data is not always sent to the cloud. The company needs a solution that offers near-real-time analytics on the data from the most updated sensors.Which solution enables the company to meet these requirements?
Answer(s): B
B) Correct: Using PutRecord/PutRecords with Kinesis Data Streams ensures low-latency ingestion from each sensor, and routing through KDA for near-real-time enrichment followed by a Firehose to Amazon OpenSearch Service enables streaming analytics with near-immediate search/visualization capabilities.A) Incorrect: Disabling buffering on the sensor side is not sufficient alone; fan-out to multiple streams and enrichment via KDA with Firehose to Redshift adds latency and complexity; OpenSearch is not directly addressed here.C) Incorrect: Disabling buffering to zero can cause data loss under failures; streaming to multiple Firehose streams and S3 then loading to Redshift introduces unnecessary steps and latency for near-real-time analytics.D) Incorrect: Using Glue/KCL and Lambda to push to Elasticsearch adds ETL overhead and higher latency; lacks direct, low-latency streaming path from Kinesis Data Streams to a searchable cluster.
A global company has different sub-organizations, and each sub-organization sells its products and services in various countries. The company's senior leadership wants to quickly identify which sub-organization is the strongest performer in each country. All sales data is stored in Amazon S3 in Parquet format.Which approach can provide the visuals that senior leadership requested with the least amount of effort?
Answer(s): A
A) Using QuickSight with Athena as the data source and heat maps provides efficient querying of Parquet data in S3 and visually contrasts sub-organizations by country, enabling quick identification of top performer per country with minimal ETL. Athena natively queries Parquet on S3; QuickSight dashboards can render heat maps for regional comparisons.B) Incorrect: data source should be Athena for serverless querying of Parquet; using S3 as a data source is not directly queryable by QuickSight without a catalog/query layer.C) Incorrect: pivot tables are less effective for cross-country, cross-sub-organization comparisons at a glance and require more interactions.D) Incorrect: using S3 as the data source bypasses the optimized cataloged queries Athena provides, increasing setup and manual effort.
A company has 1 million scanned documents stored as image les in Amazon S3. The documents contain typewritten application forms with information including the applicant rst name, applicant last name, application date, application type, and application text. The company has developed a machine learning algorithm to extract the metadata values from the scanned documents. The company wants to allow internal data analysts to analyze and nd applications using the applicant name, application date, or application text. The original images should also be downloadable. Cost control is secondary to query performance.Which solution organizes the images and metadata to drive insights while meeting the requirements?
A concise, scalable search solution is to index metadata and S3 locations in OpenSearch and use Dashboards for interactive querying and filtering, meeting performance-focused analysis needs.A) Object tags are not ideal for complex, multi-field queries and large-scale ad-hoc analytics; tagging at scale is limited and querying across 1M images becomes inefficient.B) Correct: OpenSearch provides indexed search across multiple fields (name, date, text) with fast, interactive dashboards; handles large datasets; direct access to image S3 locations preserves downloadable originals.C) Redshift is columnar but not optimized for full-text search on image-derived metadata at this scale; ad-hoc BI queries would be slower and less flexible.D) Parquet with Glue catalog + Athena suits analytics but lacks the built-in, user-friendly, interactive search dashboards and near real-time filtering that OpenSearch Dashboards offers.
A mobile gaming company wants to capture data from its gaming app and make the data available for analysis immediately. The data record size will be approximately 20 KB. The company is concerned about achieving optimal throughput from each device. Additionally, the company wants to develop a data stream processing application with dedicated throughput for each consumer.Which solution would achieve this goal?
Kinesis Data Streams with enhanced fan-out provides dedicated throughput per consumer, reducing contention and enabling low-latency delivery for per-device data 20 KB records. A) Using PutRecords with enhanced fan-out on KDS best matches immediate availability and scalable throughput per consumer. B) PutRecordBatch to Kinesis Data Firehose is for delivery to S3/Redshift/ElasticSearch with batch and buffering; Firehose does not provide per-consumer dedicated throughput and is not optimal for real-time streaming analysis. C) KPL to Firehose also routes through Firehose buffering; does not guarantee per-consumer throughput isolation. D) KDS with EC2 autoscaling lacks dedicated throughput per consumer and requires manual scaling for processing, reducing isolation.
A marketing company wants to improve its reporting and business intelligence capabilities. During the planning phase, the company interviewed the relevant stakeholders and discovered that:The operations team reports are run hourly for the current month's data.The sales team wants to use multiple Amazon QuickSight dashboards to show a rolling view of the last 30 days based on several categories. The sales team also wants to view the data as soon as it reaches the reporting backend. The nance team's reports are run daily for last month's data and once a month for the last 24 months of data. Currently, there is 400 TB of data in the system with an expected additional 100 TB added every month. The company is looking for a solution that is as cost- effective as possible.Which solution meets the company's requirements?
A concise explanation: B is correct because it optimizes cost by keeping recent data (last 2 months) in Redshift for fast, hourly/daily access, while aging data resides in S3 and is queried via Redshift Spectrum for scalable, cost-effective analytics. This supports QuickSight dashboards over Redshift for performance and spectrum for broader 24-month window without provisioning excessive Redshift storage. A overprovisions by storing 24 months in Redshift, increasing cost and not leveraging S3 for older data. C uses S3 with Redshift Spectrum but does not provide fast on-demand access for the two months critical to operations. D introduces an EMR Spark cluster, adding cost and maintenance for relatively simple BI workloads.
A media company wants to perform machine learning and analytics on the data residing in its Amazon S3 data lake. There are two data transformation requirements that will enable the consumers within the company to create reports:Daily transformations of 300 GB of data with different le formats landing in Amazon S3 at a scheduled time. One-time transformations of terabytes of archived data residing in the S3 data lake. Which combination of solutions cost-effectively meets the company's requirements for transforming the data? (Choose three.)
Answer(s): A,D,E
Daily transformations should leverage serverless or managed ETL and scalable processing for cost efficiency; Glue crawlers identify schema on ingest, Glue jobs execute transform logic, and EMR is suitable for large-scale archived data transformations.A) Correct: Glue crawlers scan data in S3 to infer schema, enabling downstream analytics preprocessing.D) Correct: Glue workflows and Glue jobs orchestrate and run ETLTransformations at scale for daily data.E) Correct: EMR provides scalable, cost-effective distributed processing for large, archived datasets.B) Incorrect: Athena is a query engine, not an ETL transformer or schema cataloger at ingest time.C) Incorrect: Redshift is a data warehouse; not ideal for daily ETL on S3; lacks serverless flexibility.F) Incorrect: SageMaker is ML-focused and not suited for bulk data transformation pipelines on S3.
A hospital uses wearable medical sensor devices to collect data from patients. The hospital is architecting a near-real-time solution that can ingest the data securely at scale. The solution should also be able to remove the patient's protected health information (PHI) from the streaming data and store the data in durable storage.Which solution meets these requirements with the least operational overhead?
Answer(s): D
D) Ingest the data using Amazon Kinesis Data Firehose to write the data to Amazon S3. Implement a transformation AWS Lambda function that parses the sensor data to remove all PHI.Kinesis Data Firehose provides fully managed, near-real-time ingestion with built-in delivery to S3 and serverless data transformation via Lambda, meeting secure, scalable, low-ops requirements and enabling PHI removal during transformation. It minimizes operational overhead by eliminating manual shard management and Lambda orchestration.A) uses KCL with Data Streams requiring custom consumer management and manual PHI removal; higher operational overhead. B) S3-triggered Lambda adds extra S3 write/read and delayed processing; less seamless for real-time transform. C) Data Streams with Lambda inside the stream requires managing consumers and potential scaling gaps; more complex.
https://aws.amazon.com/blogs/big-data/persist-streaming-data-to-amazon-s3-using-amazon-kinesis- rehose-and-aws-lambda/
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