A university intends to use Amazon Kinesis Data Firehose to collect JSON-formatted batches of water quality readings in Amazon S3. The readings are from 50 sensors scattered across a local lake. Students will query the stored data using Amazon Athena to observe changes in a captured metric over time, such as water temperature or acidity. Interest has grown in the study, prompting the university to reconsider how data will be stored.Which data format and partitioning choices will MOST signi cantly reduce costs? (Choose two.)
Answer(s): B,D
Using Parquet with Snappy and partitioning by time minimizes storage and query costs in S3/Athena. Parquet is columnar and compression-friendly, reducing scanned data size for analytic queries. Snappy provides fast, efficient compression suitable for repeated scans. Partitioning by year, month, and day limits the amount of data read by time-based queries, lowering cost.A) Avro with Snappy reduces size less effectively for columnar scans in Athena; Avro is row-based, so less favorable for projection/predicate pushdown.C) ORC without compression removes a major cost-saving factor; ORC helps columnar storage but no compression increases data size.E) Partitioning by sensor adds high-cardinality partitions, increasing metadata/maintenance without proportional query cost reduction.
https://docs.aws.amazon.com/ rehose/latest/dev/record-format-conversion.html
A healthcare company uses AWS data and analytics tools to collect, ingest, and store electronic health record (EHR) data about its patients. The raw EHR data is stored in Amazon S3 in JSON format partitioned by hour, day, and year and is updated every hour. The company wants to maintain the data catalog and metadata in an AWS Glue Data Catalog to be able to access the data using Amazon Athena or Amazon Redshift Spectrum for analytics.When de ning tables in the Data Catalog, the company has the following requirements:Choose the catalog table name and do not rely on the catalog table naming algorithm. Keep the table updated with new partitions loaded in the respective S3 bucket pre xes.Which solution meets these requirements with minimal effort?
Answer(s): C
To meet explicit table naming and partition maintenance with minimal effort, using the Glue API CreateTable to explicitly define the catalog table, then pairing with a crawler as the source ensures known naming while leveraging Glue’s partition management. A) Crawler alone creates tables with automatic naming, not honoring a fixed name. B) Manual table plus Lambda scheduling is higher maintenance and error-prone for partition updates. C) CreateTable via API grants explicit naming, and crawler as source keeps metadata in sync without custom coding. D) EMR Hive catalog adds complexity and migration overhead, not minimal effort. A) and B) fail naming/maintenance criteria; D) adds unnecessary steps.
https://docs.aws.amazon.com/glue/latest/dg/tables-described.html
A large university has adopted a strategic goal of increasing diversity among enrolled students. The data analytics team is creating a dashboard with data visualizations to enable stakeholders to view historical trends. All access must be authenticated using Microsoft Active Directory. All data in transit and at rest must be encrypted.Which solution meets these requirements?
Answer(s): B
A concise justification: Enterprise edition with SAML 2.0 federation and default encryption satisfies domain requirements: AD-integrated authentication via SAML 2.0 for centralized access control and AWS-managed encryption at rest and in transit. Enterprise edition supports more advanced security and governance features suitable for an academic environment with diverse stakeholders.A) Standard edition with SAML 2.0 federation lacks the enterprise-grade features needed for full AD integration and governance.C) Standard edition with AD Connector and customer-managed (KMS) keys introduces complexity and does not align with required enterprise access and governance.D) Enterprise edition with AD Connector is inconsistent with using SAML-based federation for AD-backed authentication and may not leverage native SSO capabilities.
https://docs.aws.amazon.com/quicksight/latest/user/WhatsNew.html
An airline has been collecting metrics on ight activities for analytics. A recently completed proof of concept demonstrates how the company provides insights to data analysts to improve on-time departures. The proof of concept used objects in Amazon S3, which contained the metrics in .csv format, and used AmazonAthena for querying the data. As the amount of data increases, the data analyst wants to optimize the storage solution to improve query performance.Which options should the data analyst use to improve performance as the data lake grows? (Choose three.)
Answer(s): C,D,F
As the data lake grows, using columnar storage and region/throughput optimizations improves Athena query performance.C) Compress the objects to reduce data transfer I/O and storage I/O, decreasing scan costs and speeding reads.D) Use an S3 bucket in the same Region as Athena to minimize cross-region latency and data transfer costs.F) Preprocess the .csv data to Apache Parquet to enable column pruning, encoding efficiencies, and reduced I/O for predicates.A) Adding a randomized string to S3 keys does not improve Athena performance and can complicate partitioning and metadata.B) Bucket locality (same account) doesn’t affect Athena performance.E) Converting to JSON increases row-level I/O and negates predicate pushdown benefits inherent to columnar formats.
A company uses the Amazon Kinesis SDK to write data to Kinesis Data Streams. Compliance requirements state that the data must be encrypted at rest using a key that can be rotated. The company wants to meet this encryption requirement with minimal coding effort.How can these requirements be met?
Using server-side encryption (SSE) with a KMS CMK allows at-rest encryption with key rotation managed by AWS KMS, requiring minimal coding when writing data via the Kinesis SDK. Enabling SSE on the stream with the CMK alias ensures encryption at rest and automatic key rotation handled by KMS.A) Incorrect: AWS Encryption SDK handles client-side encryption; not needed when SSE is configured on the stream, and rotation is managed by KMS, not by the SDK.C) Incorrect: Requires custom Lambda for encryption/decryption, increasing code and maintenance; rotation still managed by KMS if using SSE with CMK.D) Incorrect: Using the default KMS key for Kinesis may not meet the requirement if a specific CMK rotation policy is desired; explicit CMK is preferred.
https://aws.amazon.com/kinesis/data-streams/faqs/
A company wants to enrich application logs in near-real-time and use the enriched dataset for further analysis. The application is running on Amazon EC2 instances across multiple Availability Zones and storing its logs using Amazon CloudWatch Logs. The enrichment source is stored in an Amazon DynamoDB table.Which solution meets the requirements for the event collection and enrichment?
Answer(s): A
Enables near-real-time enrichment by streaming logs from CloudWatch Logs through Kinesis Data Firehose with Lambda transforming and enriching using DynamoDB, then delivering to S3 for analysis.A) Correct: CloudWatch Logs subscription → Kinesis Data Firehose → Lambda transform/enrich with DynamoDB → S3. Real-time pipeline fits near-real-time enrichment and uses DynamoDB as enrichment source.B) Incorrect: Batch export hourly with Glue; adds latency and lacks real-time streaming, plus Spark ETL is heavier and not as seamless for near-real-time enrichment.C) Incorrect: Involves Kinesis Data Analytics join with DynamoDB via SQL; complex and less deterministic for enrichment and storage flow; Kinesis Firehose not used for final delivery.D) Incorrect: EMR with Spark SQL introduces batch processing latency and orchestration overhead; not suitable for near-real-time enrichment with CloudWatch Logs.
A company uses Amazon Redshift as its data warehouse. A new table has columns that contain sensitive data. The data in the table will eventually be referenced by several existing queries that run many times a day. A data analyst needs to load 100 billion rows of data into the new table. Before doing so, the data analyst must ensure that only members of the auditing group can read the columns containing sensitive data.How can the data analyst meet these requirements with the lowest maintenance overhead?
The correct answer is B because column-level permissions in Redshift can be granted via GRANT for read-only access to specific columns, enabling selective visibility of sensitive data with minimal maintenance on a large, frequently queried table. A) Requires duplicating data into a second table, increasing storage and maintenance while not leveraging per-column grants. C) Attaching an IAM policy is not applicable for fine-grained Redshift column access control and would be misaligned with Redshift’s permission model. D) Creating a view excludes sensitive columns but adds maintenance and can alter query plans; GRANT on specific columns is more efficient for ongoing queries.
A banking company wants to collect large volumes of transactional data using Amazon Kinesis Data Streams for real-time analytics. The company usesPutRecord to send data to Amazon Kinesis, and has observed network outages during certain times of the day. The company wants to obtain exactly once semantics for the entire processing pipeline.What should the company do to obtain these characteristics?
A) A unique ID per record allows downstream processing to detect and de-duplicate duplicates introduced by network outages, achieving idempotent processing and effectively ensuring exactly-once semantics in the pipeline.B) Kinesis Data Analytics provides at-least-once or effectively-once processing depending on the source and operators; it does not guarantee exactly-once across the whole pipeline when using PutRecord and potential retries.C) Preventing multiple ingestions is impractical since outages can cause retries; without idempotent processing, duplicates may still propagate.D) EMR with Flink/Spark can offer exactly-once processing, but not as a guarantee across the entire Kinesis-based pipeline unless end-to-end stateful exactly-once guarantees are implemented; relying on framework semantics alone is insufficient.
https://docs.aws.amazon.com/streams/latest/dev/kinesis-record-processor-duplicates.html
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