Amazon AWS Certified Data Engineer – Associate DEA-C01 Exam Dumps and Practice Test Questions Set 4 Q61-80

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Question 61:

Your organization wants to ingest large-scale logs from multiple applications, transform them in real-time, and make them queryable for dashboards. Which architecture is best?

A) CloudWatch Logs → Kinesis Data Firehose → S3 + OpenSearch
B) CloudTrail → S3 + Athena
C) SQS → RDS
D) SNS → Redshift

Answer: A) CloudWatch Logs → Kinesis Data Firehose → S3 + OpenSearch

Explanation 

Option A, CloudWatch Logs → Kinesis Data Firehose → S3 + OpenSearch, is ideal for centralized log ingestion and real-time analytics. CloudWatch collects logs from multiple applications or AWS accounts. Firehose ingests and buffers logs, optionally transforming them (e.g., compression, JSON to Parquet), before delivering to S3 for durable storage and OpenSearch for low-latency search and visualization. Kibana provides dashboards and analytics. This architecture is serverless, scalable, and fault-tolerant, handling high-throughput data ingestion efficiently.

Option B, CloudTrail → S3 + Athena, is primarily suited for historical audit and compliance queries rather than real-time log analytics. CloudTrail records AWS API calls and stores them in S3, which Athena can query using SQL. While this enables powerful ad hoc queries and auditing capabilities, it introduces latency because data must first be written to S3 and then queried. Athena is a query-based service, not a streaming analytics platform, so near real-time insights are not possible with this setup. This makes it unsuitable for operational monitoring or situations that require immediate detection of anomalies or security events.

Option C, SQS → RDS, is designed for message queuing and transactional data storage, not high-throughput log ingestion. Amazon SQS ensures reliable message delivery, but it is a queue system rather than a real-time streaming platform. RDS, while a powerful relational database for structured data, cannot efficiently handle the massive write volumes and low-latency queries required for real-time log analytics. Using this combination would result in bottlenecks and delays, making it impractical for monitoring or analytics workloads that depend on immediate log visibility.

Option D, SNS → Redshift, is suitable for event-driven pipelines and batch analytics. SNS can distribute messages to multiple subscribers, and Redshift can store large volumes of structured data for complex analytical queries. However, Redshift is a data warehouse optimized for batch-oriented workloads rather than real-time ingestion or querying. Data must be loaded into Redshift tables using ETL processes, which introduces additional latency and prevents instantaneous analytics on streaming logs. This architecture is better suited for scheduled reporting or historical trend analysis rather than real-time operational monitoring.

In contrast, CloudWatch + Firehose + S3 + OpenSearch provides a robust, scalable, and serverless solution for real-time log analytics. CloudWatch captures and centralizes logs from AWS resources, applications, and services. Kinesis Data Firehose reliably streams these logs to destinations such as S3 for durable storage and OpenSearch for low-latency indexing and querying. Firehose handles buffering, batching, and optional transformations using Lambda, ensuring high-throughput, resilient delivery. S3 provides durable, cost-effective storage for historical logs, while OpenSearch enables near real-time search, visualization, and analytics using Kibana or OpenSearch Dashboards.

This architecture is fully managed and serverless, removing the operational overhead of managing clusters, scaling compute resources, or handling ETL pipelines manually. It supports high-volume streaming data, ensures durability and fault tolerance, and allows teams to gain actionable insights immediately, making it the best practice for real-time log analytics in AWS environments.

Question 62:

You want to query raw S3 datasets using SQL without moving the data or provisioning servers. Which service is most appropriate?

A) Amazon Athena
B) Amazon Redshift
C) Amazon EMR
D) AWS Glue

Answer: A) Amazon Athena

Explanation 

Option A, Athena, is a serverless query service that can query S3 objects directly. It supports structured formats (CSV, Parquet) and semi-structured formats (JSON, ORC, Avro). Integration with Glue Data Catalog allows automatic schema discovery and queryability across multiple datasets. Athena charges per query based on the amount of data scanned, reducing cost and eliminating infrastructure management.

Amazon Athena is a fully managed, serverless interactive query service that allows users to analyze data stored in Amazon S3 using standard SQL. Athena’s architecture eliminates the need to provision, configure, or manage any infrastructure, making it an ideal solution for ad hoc analytics. Users simply define the schema for their datasets stored in S3, write SQL queries, and Athena processes the data on-demand. This serverless model provides immense flexibility, cost efficiency, and scalability, especially when compared to other AWS services such as Redshift, EMR, or Glue.

Option B, Amazon Redshift, is a fully managed data warehouse designed for large-scale analytics on structured datasets. While Redshift offers high-performance query capabilities and supports complex analytics, it requires data to be loaded into tables, cluster provisioning, and ongoing management. This introduces significant operational overhead, particularly for ad hoc or exploratory queries on raw datasets stored in S3. Redshift Spectrum extends Redshift’s capabilities by enabling queries directly against S3, but it still requires a Redshift cluster and management of compute nodes. In contrast, Athena is fully serverless and allows immediate querying without any infrastructure setup, reducing operational complexity and costs. Additionally, Athena’s pay-per-query pricing model is advantageous for workloads with intermittent or unpredictable query requirements, whereas Redshift clusters incur continuous costs regardless of usage.

Option C, Amazon EMR, is a managed big data platform that supports frameworks such as Apache Spark, Hive, HBase, and Presto. EMR can process large datasets in S3 using distributed computing, making it suitable for batch processing or complex transformations. However, EMR requires cluster provisioning, configuration, and maintenance, which increases operational complexity and introduces latency. While EMR can perform SQL-style queries using Spark SQL or Hive, these queries are not instantaneous and require time to spin up clusters and execute jobs. For users seeking serverless, ad hoc queries on raw S3 datasets, EMR’s operational overhead and latency make it less suitable compared to Athena, which can execute queries immediately without cluster management.

Option D, AWS Glue, is primarily an ETL (Extract, Transform, Load) and data catalog service. Glue excels at data preparation, cleaning, and transformation, enabling datasets to be structured and ready for analytics or machine learning. While Glue can catalog metadata, transform JSON, CSV, Parquet, or other data formats, and even schedule ETL workflows, it does not provide an interactive, SQL-based query interface for raw S3 data. Users looking for on-demand analytics would need to either output transformed data to a storage or analytics service like Redshift or Athena for querying. Athena, on the other hand, allows direct querying of raw data in S3 without any prior transformation or ETL pipeline, providing immediate insights for ad hoc analytics.

Athena also offers additional features that make it ideal for scalable, cost-effective querying. It integrates seamlessly with the AWS Glue Data Catalog, which provides a centralized metadata repository for datasets stored in S3. This allows Athena users to manage schemas, track data lineage, and enforce governance across large data lakes. Athena supports partitioning and compression of datasets, which significantly improves query performance while reducing the amount of data scanned and lowering costs. It also supports a wide variety of data formats, including structured (CSV, Parquet, ORC) and semi-structured formats (JSON, Avro), making it highly flexible for diverse workloads.

Furthermore, Athena can be integrated with analytics and visualization tools like Amazon QuickSight, enabling users to create dashboards and reports directly from S3 data. It also supports federated queries, allowing access to data across other AWS sources and even on-premises databases, providing a unified view for business intelligence. Because it is fully serverless, Athena scales automatically to handle multiple concurrent queries without user intervention, making it suitable for both small teams and enterprise-scale workloads.

In summary, Amazon Athena provides a cost-efficient, fully serverless, and scalable solution for querying S3 datasets directly. Unlike Redshift, EMR, or Glue, Athena eliminates infrastructure management, allows instant SQL-based ad hoc queries, and integrates seamlessly with the AWS ecosystem. Its combination of flexibility, pay-per-query pricing, support for multiple data formats, and integration with AWS analytics tools makes it the most practical and efficient choice for organizations seeking immediate, scalable, and low-maintenance querying of raw S3 data.

Question 63:

You want to ingest high-volume IoT telemetry data, transform it in real-time, and store it for time-series analytics. Which AWS architecture is most suitable?

A) Amazon Kinesis Data Streams + AWS Lambda + Amazon Timestream
B) Amazon SQS + Amazon RDS
C) Amazon SNS + DynamoDB
D) Amazon Redshift + Kinesis Data Firehose

Answer: A) Amazon Kinesis Data Streams + AWS Lambda + Amazon Timestream

Explanation 

Option A, Kinesis Data Streams + Lambda + Timestream, is a serverless, fully managed real-time ingestion and analytics pipeline. Kinesis Data Streams ingests high-velocity IoT telemetry with durable, ordered delivery. Lambda performs real-time transformations, enrichment, and filtering. Timestream is a purpose-built time-series database, optimized for storing large-scale time-series data and performing queries like trend analysis, aggregation, and interpolation. This architecture is scalable, fault-tolerant, and minimizes operational overhead, allowing instant insights from IoT data.

Option B, SQS + RDS, is unsuitable for high-volume streaming. SQS queues messages but does not process streams in real-time. RDS cannot efficiently store massive time-series datasets or perform analytics at IoT scale.

Option C, SNS + DynamoDB, supports event-driven ingestion but lacks native time-series analytics. DynamoDB is optimized for key-value operations, not large-scale time-series queries.

Option D, Redshift + Firehose, works for batch analytics. Firehose delivers data periodically to Redshift, introducing latency, making it unsuitable for real-time IoT analytics.

Thus, KDS + Lambda + Timestream is the best practice for real-time IoT ingestion, transformation, and time-series analytics.

Question 64:

You want to catalog S3 datasets, making them discoverable and queryable in Athena and Redshift Spectrum automatically. Which AWS service should you use?

A) AWS Glue
B) Amazon EMR
C) Amazon RDS
D) Amazon Redshift

Answer: A) AWS Glue

Explanation 

Option A, AWS Glue, provides serverless Data Catalog and schema discovery. Glue crawlers scan datasets in S3, infer schema, and populate the Glue Data Catalog, making data immediately queryable via Athena or Redshift Spectrum. Glue supports structured and semi-structured formats like CSV, JSON, Parquet, and ORC. Glue ETL jobs allow data transformation, enrichment, and integration across AWS services.

Option B, EMR, can process data at scale but does not automatically catalog datasets. Manual metadata management is needed for Athena or Redshift.

Option C, RDS, is for transactional workloads, not suitable for data lake cataloging or discovery.

Option D, Redshift, can query external S3 data via Spectrum but cannot automatically discover new datasets without Glue. Manual schema definition is required.

Thus, AWS Glue is the best solution for automated cataloging, schema discovery, and query integration in a serverless, scalable way.

Question 65:

You want to orchestrate multiple ETL workflows with conditional branching, retries, and parallel execution across AWS services. Which service is most suitable?

A) AWS Step Functions
B) AWS Glue
C) Amazon EMR
D) Amazon Data Pipeline

Answer: A) AWS Step Functions

Explanation 

Option A, Step Functions, is a serverless orchestration service. It enables workflows with sequential, parallel, and conditional execution, integrates with Glue, Lambda, and EMR, and supports retries, error handling, and branching. Step Functions provides visual workflow monitoring, state tracking, and centralized management for complex ETL pipelines, reducing operational overhead and improving reliability.

Option B, Glue, performs ETL but has limited orchestration capabilities. Glue Workflows allow chaining jobs, but advanced conditional logic and parallel execution are better handled by Step Functions.

Option C, EMR, can execute distributed processing jobs but does not handle orchestration, retries, or conditional logic natively. External scripting or orchestration is required.

Option D, Data Pipeline, is a legacy orchestration tool that is not fully serverless and lacks modern workflow features like parallel execution, retries, and visual monitoring.

Thus, Step Functions is the best practice choice for orchestrating complex ETL workflows with retries, parallelism, and conditional branching.

Question 66:

You want to ingest high-volume clickstream data, perform real-time transformations, and store the processed data for immediate dashboarding. Which AWS architecture is most appropriate?

A) Amazon Kinesis Data Streams + Kinesis Data Analytics + Amazon OpenSearch Service
B) Amazon SQS + Amazon RDS
C) Amazon SNS + Amazon Redshift
D) Amazon EMR + Amazon S3

Answer: A) Amazon Kinesis Data Streams + Kinesis Data Analytics + Amazon OpenSearch Service

Explanation 

Option A, Kinesis Data Streams (KDS) + Kinesis Data Analytics (KDA) + OpenSearch, is the most suitable architecture for real-time clickstream analytics. Kinesis Data Streams handles high-throughput, real-time ingestion with durable and ordered delivery, allowing multiple consumers to process the same data simultaneously. KDA enables real-time transformations, filtering, and aggregation using SQL queries or Apache Flink applications, providing a serverless environment for analytics. Processed data can be ingested into OpenSearch, which allows low-latency querying, full-text search, and visualizations via Kibana dashboards. This architecture scales horizontally without manual provisioning and supports high availability with fault-tolerant shards.

Option B, SQS + RDS, is unsuitable for real-time analytics. SQS is a message queue for decoupled applications, but it lacks stream processing capabilities, and RDS is optimized for transactional workloads. Using SQS + RDS would require polling and batch processing to mimic streaming, increasing latency and operational complexity.

Option C, SNS + Redshift, supports event-driven batch ingestion. Redshift is a data warehouse optimized for structured data analytics, but its ingestion is micro-batch oriented. Real-time dashboarding is challenging because data would have to be staged and loaded periodically, introducing latency that is not acceptable for immediate insight.

Option D, EMR + S3, can process large-scale data via Spark or Hadoop but is optimized for batch processing. EMR clusters need provisioning, scaling, and management, introducing delays and additional operational overhead. It is not ideal for sub-second or near real-time analytics, making it less suitable for streaming clickstream data intended for dashboards.

In practice, KDS + KDA + OpenSearch provides a serverless, fault-tolerant, and low-latency architecture. The architecture supports adaptive scaling, automatic shard management, and integration with other AWS services for monitoring and alerting. For example, CloudWatch metrics can monitor shard utilization, Kinesis Data Analytics can detect anomalies in traffic, and OpenSearch visualizations provide near real-time insights into user behavior. Organizations can process millions of events per second without infrastructure overhead, ensuring continuous analytics that support operational decisions. This pattern also aligns with AWS best practices for real-time streaming analytics, providing flexibility for enrichment, filtering, and multi-target delivery (e.g., sending processed streams to S3 for archival, Redshift for analytics, and OpenSearch for dashboards).

Question 67:

You need to store IoT time-series data and query trends with minimal operational overhead. Which service is best?

A) Amazon Timestream
B) Amazon DynamoDB
C) Amazon Redshift
D) Amazon RDS

Answer: A) Amazon Timestream

Explanation 

Option A, Amazon Timestream, is a purpose-built time-series database that automatically manages data lifecycle, tiered storage, and compression. Timestream separates data into hot and cold storage, allowing rapid queries on recent data while archiving historical data cost-effectively. It supports time-series functions such as interpolation, aggregation, and smoothing, which are essential for analyzing IoT telemetry. Timestream is serverless, automatically scaling to handle millions of events per second, eliminating operational concerns like provisioning or patching clusters. Its native query language supports aggregations by time intervals, making trend analysis straightforward.

Option B, DynamoDB, is a NoSQL key-value store optimized for high throughput and low latency. While it can store IoT data, it lacks built-in time-series querying functions. Designing queries for historical trends often requires creating complex secondary indexes, TTLs, or additional tables, increasing operational complexity and maintenance.

Option C, Redshift, is a structured data warehouse suitable for analytics but requires ETL jobs to load IoT data from S3 or streams. Continuous ingestion at IoT scale introduces operational overhead, and querying high-volume time-series data in Redshift can be costlier and slower due to its batch-oriented nature.

Option D, RDS, is designed for transactional workloads. It cannot efficiently handle large-scale time-series datasets with high ingestion frequency, and complex queries for historical trends may cause performance bottlenecks.

In practice, Timestream simplifies IoT analytics by abstracting data retention, scaling, and performance tuning. Developers can focus on data ingestion, analysis, and dashboarding without managing infrastructure. Timestream also integrates with services like Kinesis Data Streams for real-time ingestion and QuickSight for visualization. Organizations can implement predictive analytics, anomaly detection, and trend monitoring using serverless, highly available, and cost-efficient storage. Compared to alternatives, Timestream is uniquely suited for time-series workloads, providing a combination of automatic scaling, query optimization, and minimal operational effort.

Question 68:

You want to automate ETL workflows that extract data from S3, transform it, and load it into Redshift with retry logic and scheduling. Which service is most appropriate?

A) AWS Glue
B) Amazon EMR
C) AWS Step Functions
D) Amazon Athena

Answer: A) AWS Glue

Explanation 

Option A, AWS Glue, is a serverless ETL service that automates extraction, transformation, and loading of data. Glue crawlers can automatically detect schema changes in S3 datasets and populate the Glue Data Catalog, making data queryable in Athena or Redshift Spectrum. Glue ETL jobs written in Python or Scala can perform complex transformations, filter records, and aggregate data. Glue supports job scheduling, retries, dependency management, and monitoring, enabling fully automated nightly ETL pipelines. Its serverless architecture eliminates the need for cluster provisioning, scaling, or maintenance, reducing operational complexity.

Option B, EMR, is a distributed processing platform for batch analytics. While EMR can process ETL workloads, it requires cluster provisioning, tuning, and scaling, increasing operational effort. Continuous automation, scheduling, and retries must be implemented manually using scripts or Step Functions.

Option C, Step Functions, is an orchestration service, not an ETL engine. Step Functions can coordinate Glue, Lambda, or EMR jobs with conditional logic, but the transformation itself must occur in another service like Glue.

Option D, Athena, is an ad-hoc SQL query service. It can read S3 data, but it cannot schedule ETL workflows or load data into Redshift automatically. Athena is not designed as an ETL engine.

In practice, AWS Glue provides a robust, fully managed ETL solution. Organizations can schedule ETL jobs daily, monitor execution with CloudWatch, handle retries on failures, and dynamically scale with workload demands. Glue also supports job bookmarks, which ensure incremental processing, reducing duplicate data ingestion. By integrating with Redshift, Athena, and S3, Glue allows seamless end-to-end data pipelines. Its serverless nature reduces operational burden and costs, making it the preferred service for automated ETL workflows.

Question 69:

You need to query large structured and semi-structured datasets in S3 using SQL without provisioning infrastructure. Which service should you use?

A) Amazon Athena
B) Amazon Redshift
C) Amazon EMR
D) AWS Glue

Answer: A) Amazon Athena

Explanation 

Option A, Athena, is a serverless SQL query service that directly queries S3 datasets. Athena supports structured formats (CSV, Parquet) and semi-structured formats (JSON, ORC, Avro). Integration with the Glue Data Catalog allows automatic schema discovery, and partitioning or columnar formats reduces query cost and improves performance. Athena uses a pay-per-query model, charging only for the data scanned, eliminating infrastructure and provisioning concerns.

Option B, Redshift, is a data warehouse optimized for analytics but requires loading data into tables and cluster management. Redshift Spectrum allows querying external S3 data, but Athena provides a simpler, fully serverless solution for ad-hoc queries without cluster provisioning.

Option C, EMR, can query S3 using Spark or Hive, but cluster provisioning, scaling, and startup latency make it less suitable for ad-hoc, serverless querying.

Option D, Glue, is primarily an ETL and cataloging service. While it can prepare and transform datasets, it does not provide direct SQL-based ad-hoc query capabilities.

Athena’s combination of serverless architecture, automatic schema discovery, low cost, and scalability makes it the most practical choice for querying raw S3 datasets. Organizations can perform analytics and generate insights without infrastructure management, which is ideal for ad-hoc exploration or integrating with dashboards like QuickSight.

Question 70:

You want to orchestrate multiple ETL workflows with conditional branching, retries, and parallel execution across AWS services. Which service is best?

A) AWS Step Functions
B) AWS Glue
C) Amazon EMR
D) Amazon Data Pipeline

Answer: A) AWS Step Functions

Explanation 

Option A, AWS Step Functions, is a serverless orchestration service that allows workflows with sequential, parallel, and conditional execution. Step Functions integrates with Glue, Lambda, and EMR, supporting retries, error handling, and branching. It provides visual workflow monitoring, state tracking, and centralized management for complex ETL pipelines. Organizations can implement conditional logic to handle failures, execute parallel tasks for efficiency, and maintain high reliability with minimal operational overhead.

Option B, Glue, performs ETL but has limited orchestration capabilities. Glue Workflows allow chaining jobs but cannot handle complex conditional logic, parallel execution, or retries as robustly as Step Functions.

Option C, EMR, executes distributed workloads but does not provide orchestration, retries, or visual workflow monitoring. Workflow management must be implemented externally, adding complexity.

Option D, Data Pipeline, is a legacy service that is not fully serverless and lacks modern orchestration features like parallel execution, retries, and integrated monitoring. Step Functions is the recommended modern solution for orchestrating complex ETL workflows across AWS services.

Question 71:

You want to ingest streaming telemetry data from IoT devices, perform real-time transformations, and store the data for searchable analytics. Which architecture is best?

A) Amazon Kinesis Data Streams + AWS Lambda + Amazon OpenSearch Service
B) Amazon SQS + Amazon RDS
C) Amazon SNS + DynamoDB
D) Amazon Redshift + Kinesis Data Firehose

Answer: A) Amazon Kinesis Data Streams + AWS Lambda + Amazon OpenSearch Service

Explanation 

Option A, Kinesis Data Streams (KDS) + Lambda + OpenSearch, is the most appropriate for real-time ingestion and analytics of IoT telemetry. Kinesis Data Streams supports high-volume ingestion with ordered, durable delivery, allowing multiple consumers to process the data concurrently. AWS Lambda provides serverless compute for real-time transformation, filtering, and enrichment without requiring server provisioning. OpenSearch stores the processed data for fast search, analytics, and visualization through Kibana dashboards. This architecture is fully serverless, scalable, and fault-tolerant, ensuring continuous, near real-time analytics with minimal operational overhead.

Option B, SQS + RDS, is designed for decoupled, asynchronous message processing. SQS cannot handle streaming ingestion with real-time transformations, and RDS is not optimized for high-frequency telemetry data. Using this combination would require polling and batch processing, which introduces latency and complicates operational management.

Option C, SNS + DynamoDB, enables event-driven messaging, but DynamoDB lacks native support for time-series analytics or complex transformations. Querying large-scale telemetry or performing aggregations in real-time would require additional design patterns and manual management.

Option D, Redshift + Kinesis Data Firehose, is better suited for batch or micro-batch processing. Firehose buffers data before delivery to Redshift, introducing latency that prevents instant analytics or real-time dashboarding. Redshift is optimized for structured analytics workloads, but it is not ideal for high-velocity telemetry data with sub-second processing requirements.

In practice, KDS + Lambda + OpenSearch provides a robust, scalable, and serverless solution. Organizations can ingest millions of telemetry events per second, transform them in real-time, and provide immediate insights for operational monitoring, anomaly detection, and reporting. This architecture aligns with AWS best practices for real-time IoT data pipelines, offering automatic scaling, fault tolerance, and integration with other services like CloudWatch for monitoring and SNS for alerting.

Question 72:

You need to catalog new S3 datasets automatically, making them discoverable and queryable in Athena and Redshift Spectrum. Which service should you use?

A) AWS Glue
B) Amazon EMR
C) Amazon RDS
D) Amazon Redshift

Answer: A) AWS Glue

Explanation 

Option A, AWS Glue, provides a serverless data catalog and automatic schema discovery. Glue crawlers scan S3 datasets, infer schema, and populate the Glue Data Catalog. This metadata can be queried immediately through Athena or Redshift Spectrum without manual schema management. Glue supports structured, semi-structured, and nested formats such as CSV, JSON, Parquet, and ORC. Glue ETL jobs allow transformation, enrichment, and integration across AWS services, making it a complete serverless solution for automated ETL and cataloging.

Option B, EMR, is a distributed cluster computing platform capable of processing large-scale datasets. While EMR can analyze S3 data using Spark or Hive, it does not provide automated cataloging or schema discovery. Metadata must be managed manually or integrated with Glue, adding operational complexity.

Option C, RDS, is a relational database service for transactional workloads. It is not designed for data lake cataloging or automatic schema discovery, making it unsuitable for large-scale S3 datasets intended for analytics.

Option D, Redshift, can query external S3 datasets via Redshift Spectrum, but it cannot automatically detect new datasets. Manual schema updates or Glue integration are required, which introduces operational overhead.

In practice, AWS Glue is the preferred service because it combines serverless architecture, automated cataloging, and ETL capabilities. Organizations can ensure consistent metadata across Athena, Redshift Spectrum, and other analytics services, reducing manual effort, preventing schema drift, and enabling real-time query capabilities. Glue also supports incremental processing and job bookmarks, which minimize redundant data processing and improve pipeline efficiency.

Question 73:

You need to orchestrate multiple ETL workflows with conditional logic, retries, and parallel execution. Which AWS service is most suitable?

A) AWS Step Functions
B) AWS Glue
C) Amazon EMR
D) Amazon Data Pipeline

Answer: A) AWS Step Functions

Explanation 

Option A, AWS Step Functions, is a serverless orchestration service designed to coordinate multiple tasks and workflows across AWS services. Step Functions allows sequential, parallel, and conditional execution, supports retries, error handling, and integrates with Glue, Lambda, EMR, and Redshift. It provides visual workflow monitoring and state tracking, simplifying the management of complex ETL pipelines. Step Functions ensures that workflows execute reliably, even in the event of failures, and enables parallel execution for efficiency.

Option B, Glue, is primarily an ETL engine. Glue Workflows can chain jobs, but its orchestration capabilities are limited. Complex conditional logic, retries, or parallel execution are better handled by Step Functions.

Option C, EMR, executes distributed data processing jobs but does not provide native orchestration, retry logic, or workflow visualization. Workflow management must be implemented externally, increasing operational complexity.

Option D, Data Pipeline, is a legacy service for workflow orchestration. While it can orchestrate ETL tasks, it is not fully serverless and lacks modern features like parallel execution, retries, and integrated monitoring.

In practice, Step Functions is the recommended modern solution for orchestrating complex ETL pipelines across multiple AWS services. It reduces operational overhead, increases reliability, and provides a scalable, serverless orchestration layer that is easy to monitor and maintain. Organizations can define workflows with conditional branching, automatic retries, and parallel execution, ensuring efficient and resilient ETL operations.

Question 74:

You want to query raw S3 datasets using SQL without provisioning servers, paying only for data scanned. Which service is best?

A) Amazon Athena
B) Amazon Redshift
C) Amazon EMR
D) AWS Glue

Answer: A) Amazon Athena

Explanation 

Option A, Athena, is a serverless SQL query service that queries S3 data directly. It supports structured formats like CSV, Parquet, ORC, and semi-structured formats such as JSON or Avro. Athena integrates with Glue Data Catalog for schema management and automatic discovery, allowing immediate query capabilities. Athena charges per query based on data scanned, providing cost-efficient, on-demand analytics. Its serverless architecture eliminates the need for provisioning, scaling, or managing infrastructure, making it ideal for ad-hoc analytics on raw S3 datasets.

Option B, Redshift, is a data warehouse that requires loading data into tables and cluster provisioning. While Redshift Spectrum allows querying S3 directly, Athena is simpler, fully serverless, and more cost-efficient for ad-hoc exploration.

Option C, EMR, supports querying S3 with Spark SQL or Hive but requires cluster management, scaling, and startup time, which introduces delays and operational overhead. EMR is better suited for batch analytics than for ad-hoc, serverless querying.

Option D, Glue, is primarily an ETL and cataloging service. While Glue can transform and catalog data, it does not provide direct SQL query capabilities on raw S3 datasets.

Athena’s combination of serverless architecture, pay-per-query pricing, integration with Glue, and scalability makes it the best solution for querying large-scale S3 datasets efficiently without managing infrastructure. It is ideal for ad-hoc analysis, dashboards, and interactive analytics workflows.

Question 75:

You want to store IoT time-series data and efficiently query trends with minimal management overhead. Which service is most appropriate?

A) Amazon Timestream
B) Amazon DynamoDB
C) Amazon Redshift
D) Amazon RDS

Answer: A) Amazon Timestream

Explanation 

Option A, Amazon Timestream, is a purpose-built, serverless time-series database optimized for storing and analyzing IoT telemetry. Timestream automatically manages data retention, compression, and tiered storage (hot and cold), ensuring efficient storage and cost optimization. It supports time-series query functions like aggregations, interpolations, and smoothing, allowing rapid analysis of trends and anomalies. Timestream scales automatically to ingest millions of events per second without manual infrastructure management.

Option B, DynamoDB, is a key-value store optimized for high throughput but lacks native time-series query support. Implementing trend analysis would require complex table design, secondary indexes, or additional ETL, increasing operational complexity.

Option C, Redshift, is a data warehouse optimized for analytics. While it can store time-series data, ingestion requires ETL pipelines, cluster management, and performance tuning. Querying high-frequency IoT data is less efficient and costlier compared to Timestream.

Option D, RDS, is designed for transactional workloads. It is not optimized for high-volume time-series ingestion or analytics. Complex queries over historical IoT data can cause performance bottlenecks.

In practice, Timestream provides a serverless, scalable, and cost-efficient solution for IoT analytics. Organizations can store massive telemetry datasets, perform real-time and historical trend analysis, and integrate with visualization tools like QuickSight or Grafana. Timestream abstracts operational tasks such as scaling, indexing, and partitioning, allowing teams to focus on analytics and insights rather than infrastructure management. Compared to DynamoDB, Redshift, or RDS, Timestream is uniquely suited for time-series workloads, providing automated query optimization and seamless integration with streaming ingestion services like Kinesis Data Streams.

Question 76:

You want to ingest streaming application logs from multiple AWS accounts, transform them in real-time, and make them searchable immediately. Which architecture is best?

A) CloudWatch Logs → Kinesis Data Firehose → S3 + OpenSearch
B) CloudTrail → S3 + Athena
C) SQS → RDS
D) SNS → Redshift

Answer: A) CloudWatch Logs → Kinesis Data Firehose → S3 + OpenSearch

Explanation 

Option A, CloudWatch Logs → Kinesis Data Firehose → S3 + OpenSearch, is the best architecture for real-time log ingestion and analytics. CloudWatch Logs aggregates logs from multiple AWS accounts, applications, and services. Kinesis Data Firehose provides streaming ingestion, transformation, and buffering, allowing data to be delivered simultaneously to multiple destinations. S3 provides durable storage, while OpenSearch enables fast, low-latency search and visualization with Kibana dashboards. This architecture is serverless, fully managed, and scalable, supporting high-volume log ingestion without infrastructure overhead.

Option B, CloudTrail → S3 + Athena, is suitable for historical audit analysis but not near real-time log analytics. CloudTrail logs AWS API calls but does not collect application-level logs, and Athena introduces query latency since it operates in a query-on-demand model.

Option C, SQS → RDS, is not optimized for streaming ingestion. SQS queues messages asynchronously, and RDS is transactional, not designed for high-volume, real-time log analytics. This combination would require additional ETL layers and introduces latency.

Option D, SNS → Redshift, supports event-driven batch ingestion. Redshift is a data warehouse optimized for structured, batch analytics, not near real-time log searching. Firehose or custom ETL is still needed, and the latency makes it unsuitable for immediate dashboards.

In practice, CloudWatch + Firehose + OpenSearch provides organizations with a scalable, serverless, and low-latency solution for centralized log analytics. Firehose allows optional transformation (e.g., filtering, format conversion), and OpenSearch supports full-text search, aggregation, and visualization in near real-time. It aligns with AWS best practices for centralized logging, monitoring, and operational visibility across multiple AWS accounts and applications.

Question 77:

You need to automate ETL pipelines that extract S3 data, transform it, and load it into Redshift with minimal operational overhead and built-in retries. Which service is best?

A) AWS Glue
B) Amazon EMR
C) AWS Step Functions
D) Amazon Athena

Answer: A) AWS Glue

Explanation 

Option A, AWS Glue, is a serverless ETL service designed to automate extraction, transformation, and loading of data. Glue crawlers can automatically detect schema changes in S3 and populate the Glue Data Catalog, making datasets immediately queryable through Athena or Redshift Spectrum. Glue ETL jobs, written in Python or Scala, allow complex transformations, filtering, and enrichment, and support job scheduling, retries, and monitoring. Being serverless, Glue eliminates the need for cluster provisioning or scaling, reducing operational overhead.

Option B, EMR, is a distributed processing platform capable of running Spark or Hadoop jobs. While EMR can process ETL workloads, it requires manual cluster management, scaling, and tuning, increasing operational effort. Automated retries and scheduling must be implemented separately, often via Step Functions or external scripts.

Option C, Step Functions, is an orchestration tool, not an ETL engine. Step Functions can coordinate Glue or Lambda jobs but cannot transform or load data directly. It is used for workflow orchestration, not data processing itself.

Option D, Athena, is a serverless SQL query service for ad-hoc analytics on S3 data. It cannot perform automated ETL workflows, load data into Redshift, or manage retries and scheduling. Athena is optimized for queries, not transformation pipelines.

In practice, AWS Glue provides a fully managed ETL solution with serverless architecture, automated retries, incremental processing, and integration with Redshift and Athena. Organizations can build pipelines that automatically ingest and transform data without worrying about infrastructure. Glue workflows support dependency management, ensuring jobs run in sequence or parallel as needed, with job bookmarks to track processed data and avoid duplication. This makes Glue the preferred service for automated ETL pipelines requiring reliability and minimal operational maintenance.

Question 78:

You want to query raw S3 datasets with SQL without provisioning servers, paying only for the data scanned. Which service should you use?

A) Amazon Athena
B) Amazon Redshift
C) Amazon EMR
D) AWS Glue

Answer: A) Amazon Athena

Explanation 

Option A, Athena, is a serverless SQL query service that queries S3 objects directly. It supports structured data formats such as CSV, Parquet, ORC, and semi-structured formats like JSON and Avro. Athena integrates with the Glue Data Catalog, enabling automatic schema discovery and making S3 datasets immediately queryable. Athena is pay-per-query, meaning users only pay for the data scanned, providing cost efficiency. Its serverless nature eliminates cluster provisioning and management, ideal for ad-hoc analysis and interactive dashboards.

Option B, Redshift, is a data warehouse optimized for structured analytics. Querying raw S3 data requires loading or using Redshift Spectrum, which still introduces additional complexity and potential overhead. Redshift clusters need provisioning, management, and scaling, making Athena simpler for serverless, ad-hoc queries.

Option C, EMR, allows S3 queries using Spark SQL or Hive, but cluster provisioning, scaling, and startup times introduce latency and operational overhead. EMR is more suitable for batch analytics or large-scale ETL, not lightweight, ad-hoc queries.

Option D, Glue, is primarily an ETL and cataloging service. While it can transform and catalog datasets, it does not provide direct SQL querying on raw S3 objects without creating an ETL job or exporting to Athena.

In practice, Athena provides the fastest, most cost-efficient, and fully serverless solution for querying raw S3 datasets. It allows analysts and engineers to explore and analyze data interactively without worrying about infrastructure, scaling, or cluster management. Organizations can use Athena to support dashboards, reporting, and ad-hoc queries across large-scale S3 data lakes efficiently.

Question 79:

You want to store IoT time-series data efficiently and perform trend analysis over time. Which service is best?

A) Amazon Timestream
B) Amazon DynamoDB
C) Amazon Redshift
D) Amazon RDS

Answer: A) Amazon Timestream

Explanation 

Option A, Amazon Timestream, is a serverless time-series database designed for storing and analyzing IoT telemetry and other time-series data. Timestream automatically manages data retention, tiered storage, and compression, separating hot and cold storage to reduce cost while maintaining query performance. It supports time-series functions like interpolation, aggregation, smoothing, and trend analysis. Timestream scales automatically to ingest millions of events per second, providing real-time insights without manual scaling or infrastructure management.

Option B, DynamoDB, is a high-performance key-value store. While it can store IoT data, it lacks native time-series querying. Trend analysis would require complex schema design, secondary indexes, or additional ETL, adding operational overhead.

Option C, Redshift, is optimized for structured analytics but requires ETL pipelines to load time-series data. Continuous ingestion of high-volume IoT data can be inefficient, and querying trends may be slower due to batch-oriented architecture.

Option D, RDS, is designed for transactional workloads. It cannot efficiently handle high-frequency time-series data, and complex analytical queries may cause performance bottlenecks.

In practice, Timestream simplifies IoT analytics by automatically handling scaling, indexing, and storage management. Organizations can focus on analyzing trends, performing anomaly detection, and generating insights without worrying about infrastructure. Timestream integrates with Kinesis Data Streams for ingestion and visualization tools like QuickSight or Grafana for reporting, making it the preferred choice for serverless, scalable time-series workloads.

Question 80:

You want to orchestrate multiple ETL workflows with conditional execution, retries, and parallel processing across AWS services. Which service is best?

A) AWS Step Functions
B) AWS Glue
C) Amazon EMR
D) Amazon Data Pipeline

Answer: A) AWS Step Functions

Explanation 

Option A, AWS Step Functions, is a serverless orchestration service that enables workflows with sequential, parallel, and conditional execution. Step Functions integrates with Glue, Lambda, EMR, and Redshift, providing retries, error handling, and workflow branching. It includes visual monitoring, state tracking, and centralized management, simplifying orchestration of complex ETL pipelines. Step Functions ensures reliable execution of multi-step workflows even in the event of failures, and parallel processing improves efficiency for large-scale ETL operations.

Option B, Glue, is a managed ETL service. While Glue Workflows allow chaining ETL jobs, advanced orchestration features such as conditional branching and parallel execution are limited compared to Step Functions.

Option C, EMR, executes distributed data processing jobs but does not provide orchestration, retries, or conditional execution. Workflow logic must be implemented externally, increasing operational complexity.

Option D, Data Pipeline, is a legacy orchestration tool. It is not fully serverless, lacks modern workflow features such as robust parallel execution, and has limited monitoring capabilities compared to Step Functions.

In practice, Step Functions is the preferred solution for orchestrating complex ETL pipelines across multiple AWS services. It provides serverless execution, error handling, parallelism, and monitoring, allowing organizations to build scalable, reliable, and maintainable data pipelines with minimal operational overhead.