Amazon AWS Certified Data Engineer – Associate DEA-C01 Exam Dumps and Practice Test Questions Set 7 Q121-140

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Question 121:

You want to ingest IoT sensor data at high velocity, process it in real-time, and store it for analytics and monitoring dashboards. Which architecture is best?

A) Amazon Kinesis Data Streams + AWS Lambda + Amazon Timestream
B) Amazon SQS + Amazon RDS
C) Amazon SNS + Amazon Redshift
D) Amazon EMR + Amazon S3

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

Explanation

Option A, Amazon Kinesis Data Streams (KDS) + AWS Lambda + Amazon Timestream, represents a fully serverless, scalable, and resilient architecture for ingesting, processing, and analyzing high-frequency IoT sensor data. Kinesis Data Streams acts as the ingestion layer, capable of capturing hundreds of thousands of events per second from distributed IoT devices, telemetry systems, or application logs. Streams are divided into shards, which provide parallelism for high-throughput processing and allow multiple consumers to read the same data independently. This ensures data durability, order preservation, and reliable delivery, which are critical for real-time telemetry pipelines where timing and accuracy are essential.

Once data is ingested into Kinesis, AWS Lambda provides a serverless, event-driven processing layer. Lambda functions are triggered automatically by new records in the Kinesis stream, enabling on-the-fly transformations, filtering, enrichment, or anomaly detection. Lambda’s serverless nature eliminates the need to provision or manage compute resources and automatically scales to match the volume of incoming data. This capability is crucial for IoT workloads where the volume of incoming telemetry data can vary significantly over time. Lambda can normalize sensor readings, filter out invalid data, compute derived metrics, or enrich events with contextual information, preparing the data for downstream analytics without operational overhead.

The processed data is then stored in Amazon Timestream, a purpose-built, serverless time-series database optimized for storing and querying telemetry and IoT data. Timestream automatically manages hot and cold storage tiers, moving recent, frequently accessed data to memory-optimized storage for low-latency queries while archiving older data in cost-efficient magnetic storage. This tiered storage model ensures cost-effective long-term retention while maintaining fast query performance for recent events. Timestream also provides native time-series query functions, including aggregations, interpolation, smoothing, and anomaly detection, which are essential for analyzing trends, monitoring device performance, and generating alerts in real time.

This architecture supports low-latency analytics, operational dashboards, and alerts, enabling organizations to monitor IoT devices and detect issues as they occur. Visualization tools like Amazon QuickSight or Grafana can directly query Timestream, allowing interactive dashboards for operations, maintenance, or business analytics. With KDS + Lambda + Timestream, organizations can implement real-time monitoring pipelines that are fully serverless, reducing operational complexity, scaling automatically with demand, and providing immediate insights from millions of events per second.

By contrast, Option B, SQS + RDS, is poorly suited for real-time IoT pipelines. SQS is asynchronous and provides reliable message delivery but does not guarantee the low-latency or ordered processing required for high-frequency telemetry. RDS is a relational database optimized for transactional workloads, not high-throughput streaming ingestion. Implementing real-time analytics with SQS + RDS would require polling, batching, and manual inserts, introducing latency and operational overhead. Aggregating or analyzing time-series data in RDS would also require significant schema design and additional application logic, making it inefficient for dashboards or anomaly detection.

Option C, SNS + Redshift, supports event-driven batch ingestion but is optimized for analytical workloads on structured datasets. Redshift excels at batch queries over large datasets but is not designed for continuous, high-frequency streaming data. Micro-batch loading introduces latency, making it unsuitable for real-time IoT dashboards, alerts, or trend analysis. While Redshift can store and analyze historical telemetry, it cannot provide the instantaneous insights required for operational monitoring.

Option D, EMR + S3, is designed primarily for batch processing and large-scale analytics. EMR clusters require provisioning and management, which introduces latency and operational complexity. S3, while durable, is not optimized for frequent, high-velocity writes, and batch processing introduces delays that prevent near-real-time analytics. This architecture is appropriate for large-scale historical analytics or ETL workflows but fails to meet the low-latency requirements of IoT telemetry pipelines.

In practice, KDS + Lambda + Timestream is the best architecture for real-time IoT analytics. It provides a fully serverless, scalable, and fault-tolerant pipeline capable of ingesting, transforming, and storing millions of events per second. Analysts and operators can build dashboards, perform trend analysis, and set up alerts with minimal operational effort. This design aligns with AWS best practices for serverless, real-time IoT pipelines, maximizing both efficiency and flexibility while reducing infrastructure management and operational overhead.

Question 122:

You need to catalog 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, is a fully managed, serverless ETL (Extract, Transform, Load) and data catalog service that simplifies data preparation, transformation, and metadata management in modern cloud environments. One of Glue’s primary strengths is its ability to automatically scan datasets stored in Amazon S3 using Glue Crawlers. Crawlers can detect new datasets, identify schema changes, and populate the Glue Data Catalog with up-to-date metadata. This allows analytics services such as Amazon Athena and Redshift Spectrum to query datasets immediately, without requiring manual schema definitions or updates. By providing a centralized, consistent metadata repository, Glue ensures data governance, schema consistency, and efficient query execution across a dynamic, evolving data lake environment.

Glue supports a wide variety of data formats, including structured formats like CSV, Parquet, and ORC, as well as semi-structured formats such as JSON and Avro. This versatility is critical in modern analytics workflows, where data can come from multiple sources with varying schemas and structures. Glue ETL jobs allow data engineers to perform transformations, cleaning, enrichment, and aggregation of raw datasets prior to analytics. For example, nested JSON logs can be flattened into tabular formats, missing or inconsistent fields can be standardized, and derived metrics or calculated columns can be added automatically. ETL jobs can be written in Python or Scala, or created visually using Glue Studio, enabling both code-first and low-code development approaches.

In addition, Glue supports job scheduling, workflow orchestration, and dependency management, allowing fully automated ETL pipelines. These pipelines can run on a recurring schedule, trigger on events, or integrate with workflow dependencies, enabling organizations to automate complex data preparation tasks while ensuring data is consistently curated for downstream analytics. Its serverless nature eliminates the need for provisioning and managing infrastructure, providing seamless scalability to handle large-scale or variable workloads.

Option B, Amazon EMR, is a managed big data platform that supports distributed processing frameworks such as Apache Spark, Hive, Presto, and HBase. EMR can process large datasets stored in S3 using Spark SQL or Hive queries and is highly flexible for batch processing. However, EMR does not provide automatic metadata cataloging or schema detection. Metadata management in EMR requires manual configuration of the Hive metastore or integration with AWS Glue, which adds operational overhead and increases complexity. Additionally, spinning up EMR clusters and managing scaling can be time-consuming and resource-intensive, making it less suitable for agile, serverless ETL or dynamic data lake environments.

Option C, Amazon RDS, is a relational database service designed for transactional workloads. While RDS is reliable for structured, relational data, it cannot automatically detect, catalog, or query datasets stored in S3. Users must manually load data and maintain schemas, which introduces operational complexity and limits its applicability for large-scale, evolving analytics workflows or ad hoc queries.

Option D, Amazon Redshift, is a fully managed data warehouse optimized for analytical queries over structured datasets. Redshift can query external datasets stored in S3 using Redshift Spectrum, which extends the warehouse’s query engine to access data outside the cluster. However, Redshift does not automatically detect new datasets or schema changes in S3. Manual schema updates or integration with Glue are required to ensure that queries run correctly, increasing operational effort and reducing agility for rapidly changing datasets.

In practice, AWS Glue provides the most efficient solution for modern data lake architectures. Its automated crawlers, serverless scalability, and ETL capabilities reduce operational complexity while ensuring metadata consistency and immediate queryability. Analysts and data scientists can focus on exploring and analyzing datasets without waiting for schema updates or managing clusters. By integrating seamlessly with Athena, Redshift Spectrum, and other analytics tools, Glue enables organizations to implement dynamic, self-service, and fully automated analytics pipelines.

In summary, AWS Glue ensures automated cataloging, metadata consistency, and serverless scalability, making it the optimal choice for preparing, managing, and querying S3 datasets. Compared to EMR, RDS, or Redshift alone, Glue minimizes operational overhead, provides centralized metadata management, and accelerates time-to-insight in dynamic, large-scale data lake environments.

Question 123:

You want to orchestrate multiple ETL workflows with conditional execution, retries, and parallel tasks. 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, AWS Step Functions, is a fully managed serverless workflow orchestration service that allows organizations to coordinate multiple AWS services into robust, automated pipelines. Step Functions provides a visual workflow interface, enabling developers to define ETL processes as a series of steps with built-in error handling, retries, and conditional logic. This is particularly useful for ETL workloads, which often require sequential and dependent operations, such as extracting raw data from S3, transforming it with Glue or Lambda, and loading it into Redshift or Athena for analysis. Step Functions automatically manages execution order, parallelism, and state management, eliminating the need for custom orchestration code or complex external scheduling systems.

Step Functions excels in resilience and maintainability. Each step in a workflow can include retry policies, error handling, and catch mechanisms to handle failures without halting the entire pipeline. For example, if a Glue ETL job fails due to transient issues, Step Functions can automatically retry the job with configurable backoff policies. Conditional branching allows workflows to execute different paths based on runtime results or data validation checks, enabling more intelligent and flexible ETL pipelines. These features significantly reduce operational overhead and the risk of data pipeline failures, ensuring that data is consistently transformed, enriched, and delivered.

Step Functions also integrates seamlessly with a wide variety of AWS services commonly used in ETL pipelines. These include AWS Glue for data transformation, Lambda for lightweight processing, Redshift and Athena for analytics, S3 for data storage, SNS and SQS for messaging, and Timestream or DynamoDB for specialized storage needs. This deep integration allows organizations to build end-to-end ETL pipelines entirely within the AWS ecosystem, without relying on third-party orchestration tools or custom scripts. Additionally, because Step Functions is serverless, it scales automatically with demand and removes the need to provision or manage infrastructure, providing cost efficiency and simplicity for workflows of any size.

By contrast, Option C, Amazon EMR, is a distributed data processing platform that excels at large-scale batch processing using frameworks like Apache Spark, Hive, and Presto. While EMR is powerful for data transformations and analytics, it does not provide native workflow orchestration. Sequencing of jobs, retries on failure, and conditional branching must be implemented externally using scripts, cron jobs, or additional orchestration layers. This increases operational complexity and requires more effort to maintain pipelines that are robust, error-tolerant, and scalable. EMR clusters also require provisioning and management, adding further operational overhead compared to the serverless, fully managed Step Functions approach.

Option D, AWS Data Pipeline, is a legacy ETL orchestration tool that predates Step Functions. While it provides basic scheduling and task execution capabilities, it is not fully serverless and lacks the advanced monitoring, retry policies, and conditional execution features present in Step Functions. Data Pipeline also has limited support for parallel execution and modern workflow patterns, making it less efficient for handling complex ETL scenarios at scale. Organizations using Data Pipeline often need to supplement it with additional tools for logging, monitoring, and error handling, increasing both operational complexity and maintenance burden.

In practice, Step Functions enables organizations to build robust, maintainable, and scalable ETL pipelines. By providing built-in orchestration, error handling, retries, parallelism, and conditional logic, Step Functions reduces operational overhead and ensures consistent data processing. Workflows can be visually designed, monitored in real time, and updated easily as business requirements evolve. Step Functions also supports integration with serverless services like Lambda and Glue, allowing fully automated ETL pipelines without the need to manage clusters or servers.

In summary, AWS Step Functions is the preferred orchestration tool for modern ETL pipelines. Compared to EMR and Data Pipeline, it provides a serverless, scalable, and highly maintainable approach, enabling organizations to automate complex data workflows, handle failures gracefully, and ensure that data is processed reliably and efficiently. Its integration with the broader AWS ecosystem and its advanced orchestration features make it the best choice for building robust, real-time, or batch ETL pipelines.

Question 124:

You want to query raw S3 datasets using SQL without provisioning servers and pay only for 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 datasets directly. It supports structured (CSV, Parquet) and semi-structured (JSON, ORC, Avro) formats. Integration with the Glue Data Catalog allows automatic schema discovery and immediate query access. Athena is pay-per-query, providing cost efficiency without managing clusters or servers.

Option B, Redshift, requires cluster provisioning and ETL pipelines for data ingestion. Redshift Spectrum allows external querying, but Athena is simpler, fully serverless, and ideal for ad-hoc queries.

Option C, EMR, can query S3 with Spark SQL or Hive, but requires cluster provisioning, configuration, and scaling, introducing latency and operational complexity.

Option D, Glue, is primarily an ETL and cataloging service and cannot directly query S3 datasets using SQL without exporting or transforming the data.

In practice, Athena provides a serverless, scalable, and cost-efficient solution for querying S3 datasets. Analysts can generate reports, dashboards, or perform ad-hoc analytics immediately, making it ideal for organizations managing large S3 data lakes.

Question 125:

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

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

Answer: A) Amazon Timestream

Explanation

Option A, Timestream, is a serverless, time-series database optimized for IoT and telemetry workloads. It automatically manages tiered storage, retention policies, and compression, separating hot and cold data for cost optimization. Timestream supports time-series queries, including aggregations, interpolation, smoothing, and trend detection. It scales automatically to handle millions of events per second, providing low-latency analytics for dashboards and anomaly detection.

Option B, DynamoDB, is a high-throughput key-value store. While it can store IoT data, it lacks native time-series query capabilities, making trend analysis complex and requiring additional ETL or schema design.

Option C, Redshift, is optimized for batch analytics. Continuous ingestion and high-frequency time-series queries require ETL pipelines and cluster management, increasing latency and operational cost.

Option D, RDS, is transactional and cannot efficiently handle high-frequency time-series data or trend analysis.

In practice, Timestream provides a serverless, scalable, and cost-efficient platform for IoT telemetry. It enables real-time trend analysis, anomaly detection, and visualization integration with QuickSight or Grafana without infrastructure management, making it the ideal solution for time-series analytics workloads.

Question 126:

You want to stream clickstream data from your website, process it in real-time, and feed it to dashboards for immediate analytics. Which architecture is best?

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, provides a fully serverless, low-latency architecture for streaming clickstream data. KDS offers durable, ordered ingestion, allowing multiple consumers to read the same stream concurrently without conflicts. KDA enables real-time stream processing, filtering, aggregating, or enriching events using SQL or Apache Flink applications. OpenSearch allows low-latency visualization, search, and dashboards with Kibana, making it ideal for operational monitoring and analytics.

Option B, SQS + RDS, is asynchronous. SQS can queue events, and RDS can store relational data. However, processing high-frequency streams requires polling and batch inserts, introducing latency. Real-time dashboards and analytics are difficult to achieve with this architecture.

Option C, SNS + Redshift, supports event-driven batch ingestion. While Redshift Spectrum can query S3, micro-batch loading introduces latency. Redshift is designed for structured batch analytics, not real-time dashboards, making it less suitable for immediate clickstream analysis.

Option D, EMR + S3, is optimized for batch processing. EMR clusters require provisioning, and S3 has high latency for frequent updates. While this is suitable for historical analytics, it is not appropriate for low-latency, real-time dashboards.

In practice, KDS + KDA + OpenSearch is ideal for high-throughput, low-latency streaming analytics, allowing organizations to monitor website traffic, detect anomalies, and update dashboards in real time. Its serverless, scalable nature reduces operational overhead, automatically adjusts to workload, and aligns with AWS best practices for real-time data processing.

Question 127:

You need to automatically catalog new datasets in S3 and make them 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, is a serverless ETL and data cataloging service. Glue crawlers scan S3 datasets, detect schemas, and populate the Glue Data Catalog, enabling Athena and Redshift Spectrum queries. Glue supports structured (CSV, Parquet, ORC) and semi-structured formats (JSON, Avro). ETL jobs allow filtering, enrichment, and transformation. Glue ensures metadata consistency and eliminates manual schema management for dynamic datasets.

Option B, EMR, can process datasets using Spark or Hive, but does not automatically catalog data. Schema management must be performed manually or via integration with Glue, increasing operational complexity.

Option C, RDS, is a relational database optimized for transactional workloads. It cannot discover or catalog S3 datasets automatically, making it unsuitable for dynamic data lakes.

Option D, Redshift, can query S3 datasets via Spectrum but does not automatically detect new datasets. Manual schema updates or Glue integration are required, which increases operational overhead.

In practice, AWS Glue is the preferred solution for serverless, automated cataloging, reducing operational effort, ensuring schema consistency, and enabling immediate queries of newly added datasets. It supports dynamic data lake architectures and allows organizations to scale analytics efficiently.

Question 128:

You want to orchestrate complex ETL workflows with conditional execution, parallel tasks, and retries across multiple 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 enabling conditional branching, parallel execution, integrated retries, and error handling. It integrates with Glue, Lambda, EMR, and Redshift, providing visual monitoring, state tracking, and logging. Step Functions is ideal for building complex ETL workflows where tasks depend on upstream outcomes, need retries on failure, or require parallel execution to improve throughput.

Option B, Glue, is primarily an ETL service with basic workflows. While Glue Workflows can chain jobs, they cannot handle advanced conditional logic, parallel execution, or sophisticated error handling as robustly as Step Functions.

Option C, EMR, is optimized for distributed batch processing. EMR does not natively orchestrate workflows, requiring external logic for sequencing, retries, or conditional execution.

Option D, Data Pipeline, is a legacy orchestration service. It is not fully serverless, lacks modern monitoring, and does not support advanced parallelism or dynamic decision-making, limiting its suitability for complex ETL pipelines.

In practice, Step Functions enables organizations to orchestrate robust, scalable, maintainable ETL workflows. Its serverless design reduces operational overhead, and its integrations allow pipelines to respond dynamically to data, errors, or system events. It is the best choice for modern ETL orchestration on AWS.

Question 129:

You want to query raw datasets in S3 using SQL without provisioning infrastructure and pay only for the 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 for ad-hoc queries of S3 datasets. It supports structured formats (CSV, Parquet) and semi-structured formats (JSON, ORC, Avro). Integration with the Glue Data Catalog allows automatic schema discovery and immediate querying. Athena is pay-per-query, providing cost efficiency, with no need to provision servers or clusters.

Option B, Redshift, is a fully managed data warehouse requiring cluster provisioning. While Redshift Spectrum can query external S3 datasets, Athena is simpler, fully serverless, and ideal for interactive or ad-hoc queries.

Option C, EMR, can query S3 using Spark SQL or Hive. However, clusters must be provisioned and managed, and scaling introduces latency, making it unsuitable for quick ad-hoc queries.

Option D, Glue, is primarily an ETL and cataloging service. It cannot directly execute ad-hoc SQL queries without creating ETL jobs or exporting data to another service.

In practice, Athena provides a serverless, scalable, and cost-efficient solution for querying S3 datasets. Analysts can generate reports, create dashboards, or perform exploratory data analysis immediately. It is ideal for data lake architectures, reducing management overhead and operational costs.

Question 130:

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

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

Answer: A) Amazon Timestream

Explanation

Option A, Timestream, is a serverless time-series database designed for IoT and telemetry workloads. It automatically manages tiered storage, retention policies, and compression, separating hot and cold data to reduce cost. Timestream supports time-series query functions, including aggregation, smoothing, and interpolation, enabling real-time trend analysis. It scales automatically to ingest millions of events per second, providing low-latency analytics and operational dashboards.

Option B, DynamoDB, is a high-throughput key-value store. While it can store IoT data, it lacks native time-series queries, forcing the design of secondary indexes or additional ETL to analyze trends, increasing complexity.

Option C, Redshift, is optimized for batch analytics. Continuous ingestion and high-frequency time-series queries require ETL pipelines and cluster management, introducing latency and operational overhead.

Option D, RDS, is transactional and cannot efficiently handle high-frequency time-series workloads or real-time trend analysis.

In practice, Timestream provides a serverless, scalable, and cost-efficient platform for IoT telemetry. It supports real-time trend detection, anomaly monitoring, and integration with visualization tools like QuickSight or Grafana, making it the ideal solution for IoT and time-series analytics workloads.

Question 131:

You want to ingest high-frequency IoT sensor data, perform real-time anomaly detection, and store it for dashboards and alerts. Which architecture is best?

A) Amazon Kinesis Data Streams + AWS Lambda + Amazon Timestream
B) Amazon SQS + Amazon RDS
C) Amazon SNS + Amazon Redshift
D) Amazon EMR + Amazon S3

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

Explanation

Option A, Kinesis Data Streams (KDS) + Lambda + Timestream, is the optimal solution for real-time IoT ingestion and analytics. KDS provides durable, ordered streaming ingestion and supports high-throughput workloads, enabling multiple consumers to read the same data simultaneously. AWS Lambda allows serverless real-time processing, such as filtering, enrichment, and anomaly detection. Amazon Timestream is a purpose-built time-series database that manages tiered storage, retention policies, and compression automatically while providing time-series query functions for trend analysis and anomaly detection. This architecture supports low-latency analytics, operational dashboards, and alerts with minimal operational overhead, making it ideal for IoT telemetry and monitoring.

Option B, SQS + RDS, is asynchronous. While SQS queues events, RDS is a transactional relational database that is not optimized for high-frequency streaming data. Implementing real-time anomaly detection would require complex polling, batch processing, and ETL pipelines, introducing latency and complexity.

Option C, SNS + Redshift, supports event-driven batch ingestion. Redshift is a data warehouse designed for structured, batch-oriented analytics, not real-time ingestion. Micro-batch loading creates latency, making it unsuitable for near real-time dashboards and alerts.

Option D, EMR + S3, is designed for batch processing and historical analytics. EMR clusters require provisioning, and S3 is high-latency storage, which is not suitable for sub-second processing of streaming data. While it is efficient for historical trend analysis, it cannot deliver real-time monitoring or anomaly detection.

In practice, KDS + Lambda + Timestream provides a serverless, scalable, and fault-tolerant architecture for ingesting, processing, and analyzing IoT telemetry in near real time. Analysts and operational teams can monitor trends, detect anomalies immediately, and feed dashboards or alerts. Its serverless nature reduces operational overhead, automatically scales to handle fluctuating event volumes, and aligns with AWS best practices for real-time IoT analytics pipelines.

Question 132:

You need to catalog S3 datasets automatically and make them queryable in Athena and Redshift Spectrum. Which service is best?

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

Answer: A) AWS Glue

Explanation

Option A, AWS Glue, is a serverless ETL and data catalog service. Glue crawlers automatically scan S3 datasets, infer schemas, and populate the Glue Data Catalog, enabling immediate queries in Athena and Redshift Spectrum. Glue supports structured data (CSV, Parquet, ORC) and semi-structured formats (JSON, Avro). It also provides ETL capabilities for data transformation, cleaning, and enrichment, allowing analytics-ready datasets. Glue ensures metadata consistency and reduces manual schema management, which is critical for dynamic data lake environments.

Option B, EMR, is a distributed data processing platform suitable for large-scale analytics using Spark or Hive. However, EMR cannot automatically catalog new datasets. Metadata management must be manual or rely on Glue integration, increasing operational complexity.

Option C, RDS, is a transactional relational database service and cannot automatically detect or catalog S3 datasets, making it unsuitable for dynamic data lakes or ad-hoc analytics.

Option D, Redshift, can query external datasets via Spectrum but cannot automatically discover new datasets. Manual schema updates or Glue integration is required, increasing operational overhead and reducing agility.

In practice, AWS Glue is the recommended solution for serverless automated cataloging. It reduces operational effort, ensures schema consistency, and allows analysts to query newly added datasets immediately. Glue scales automatically, supports a variety of data formats, and integrates seamlessly with Athena and Redshift Spectrum, making it ideal for modern data lake architectures.

Question 133:

You want to orchestrate complex ETL workflows with conditional execution, parallel tasks, and retry logic. Which service should you use?

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 coordinates workflows across multiple AWS services. It supports sequential, parallel, and conditional execution, integrated retries, error handling, and state management. Step Functions integrates seamlessly with Glue, Lambda, EMR, and Redshift. Its visual workflow monitoring and logging allows teams to debug, optimize, and track ETL pipelines effectively. Parallel execution allows simultaneous tasks to run efficiently, while conditional logic enables dynamic decision-making based on data or processing outcomes.

Option B, Glue, provides ETL capabilities and basic workflows but cannot handle complex conditional logic or advanced parallel execution as effectively as Step Functions. Glue Workflows are limited in their ability to manage retries and dependencies dynamically.

Option C, EMR, is a distributed processing platform optimized for batch analytics. EMR does not provide native orchestration, so external solutions or scripts are required to sequence tasks, handle retries, and manage dependencies.

Option D, Data Pipeline, is a legacy orchestration service. It is not fully serverless, has limited monitoring and parallel execution capabilities, and lacks modern retry and error handling features.

In practice, Step Functions is the ideal choice for orchestrating robust, maintainable, and scalable ETL workflows. It reduces operational overhead, ensures reliable execution, integrates with other AWS services, and supports complex pipeline logic, aligning with best practices for modern ETL orchestration.

Question 134:

You want to query raw S3 datasets using SQL without managing infrastructure and pay only for the 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 directly queries S3 datasets. It supports structured formats (CSV, Parquet) and semi-structured formats (JSON, ORC, Avro). Integration with the Glue Data Catalog allows automatic schema discovery, enabling immediate queries. Athena is pay-per-query, making it cost-efficient. Analysts can perform ad-hoc queries, generate reports, and build dashboards without provisioning clusters or servers.

Option B, Redshift, is a managed data warehouse that requires cluster provisioning. While Redshift Spectrum allows querying S3 datasets, Athena is simpler, serverless, and ideal for interactive or ad-hoc queries.

Option C, EMR, allows querying S3 using Spark SQL or Hive. However, clusters must be provisioned and maintained. Scaling introduces latency and operational overhead, making it unsuitable for ad-hoc queries.

Option D, Glue, is primarily an ETL and cataloging service. It cannot directly perform ad-hoc SQL queries without moving data or creating ETL jobs, which introduces complexity.

In practice, Athena is the serverless, scalable, and cost-efficient solution for querying S3 datasets. Analysts can perform immediate queries, explore data lakes, and integrate results with visualization tools like QuickSight, supporting agile analytics workflows.

Question 135:

You want to store IoT time-series data efficiently and perform trend analysis and anomaly detection. Which service is most suitable?

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 optimized for IoT and telemetry workloads. It automatically manages tiered storage, retention policies, and compression, separating hot and cold data to optimize costs. Timestream provides native time-series query functions, including aggregations, smoothing, interpolation, and trend analysis, allowing real-time analytics and anomaly detection. It scales automatically to handle millions of events per second, supporting low-latency dashboards and operational monitoring.

Option B, DynamoDB, is a high-throughput key-value store. While it can store IoT data, it lacks native time-series query capabilities, requiring additional ETL, indexes, or data modeling to perform trend analysis, increasing complexity.

Option C, Redshift, is optimized for batch analytics. High-frequency, continuous time-series ingestion requires ETL pipelines and cluster management, introducing latency and operational overhead.

Option D, RDS, is transactional and cannot efficiently handle high-frequency time-series workloads or real-time trend analytics.

In practice, Timestream is the recommended solution for IoT telemetry and time-series analytics. It enables real-time trend detection, anomaly monitoring, and seamless integration with visualization tools like QuickSight or Grafana, making it the ideal choice for modern IoT and telemetry analytics pipelines.

Question 136:

You want to ingest streaming application logs, perform real-time filtering and aggregation, and make results available for dashboards and alerts. Which architecture is best?

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

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

Explanation

Option A, Kinesis Data Streams (KDS) + Lambda + OpenSearch, provides a serverless, low-latency architecture for ingesting and processing streaming logs. KDS ensures durable, ordered ingestion, allowing multiple consumers to process the same stream concurrently. Lambda can perform real-time transformations, filtering, aggregation, and anomaly detection, with minimal operational management. OpenSearch provides search, analytics, and dashboarding via Kibana, enabling near-instant monitoring and alerting. This combination is highly scalable, automatically adjusting to changing workloads and providing low-latency visibility into application logs.

Option B, SQS + RDS, is asynchronous. SQS can queue messages, and RDS stores structured data, but real-time filtering and dashboarding are difficult due to polling, batch inserts, and transactional overhead, introducing latency.

Option C, SNS + Redshift, is optimized for batch processing. Redshift is a data warehouse designed for structured queries, not real-time ingestion or dashboards, making it unsuitable for immediate analytics or alerting.

Option D, EMR + S3, is suited for batch analytics. EMR requires cluster provisioning, and S3 has high write latency. While useful for historical log analysis, it is not appropriate for real-time dashboards or alerts.

In practice, KDS + Lambda + OpenSearch allows organizations to ingest, process, and visualize logs in near real-time, enabling rapid detection of anomalies, operational insights, and automated alerts. Its serverless nature reduces operational overhead and ensures scalability, making it ideal for modern log analytics pipelines.

Question 137:

You want to automatically catalog datasets in S3 to make them discoverable for 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, is a serverless ETL and data catalog service. Glue crawlers automatically scan datasets in S3, infer schemas, and populate the Glue Data Catalog, making data immediately queryable via Athena and Redshift Spectrum. Glue supports structured formats (CSV, Parquet, ORC) and semi-structured formats (JSON, Avro). ETL jobs allow data transformation, filtering, and enrichment, ensuring analytics-ready datasets.

Option B, EMR, is excellent for large-scale processing using Spark or Hive but cannot automatically catalog new datasets. Manual Hive metastore management or Glue integration is needed, increasing operational complexity.

Option C, RDS, is transactional and cannot automatically detect or catalog S3 datasets, making it unsuitable for dynamic data lakes.

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

In practice, AWS Glue ensures automated cataloging, reduces manual intervention, maintains metadata consistency, and enables immediate queries for analysts. Its serverless, scalable nature is ideal for organizations managing dynamic S3 data lakes.

Question 138:

You want to orchestrate ETL workflows with conditional execution, parallel processing, and retries. 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 coordinates tasks across AWS services. It supports sequential, parallel, and conditional execution, integrates retries and error handling, and maintains state and execution history. Step Functions integrates with Lambda, Glue, EMR, and Redshift, allowing complex ETL pipelines to execute reliably. Parallel execution improves throughput, conditional logic enables dynamic decisions, and integrated retries ensure robustness. Visual workflow monitoring and logging make debugging and optimization straightforward.

Option B, Glue, provides ETL and basic workflow chaining but cannot handle complex conditional logic, retries, or parallel execution as effectively as Step Functions.

Option C, EMR, is designed for batch analytics but does not natively orchestrate workflows. External orchestration scripts are needed for task sequencing, retry handling, and dependencies.

Option D, Data Pipeline, is a legacy service. It is not fully serverless, has limited parallelism, and lacks modern error-handling and monitoring capabilities, making it less suitable for modern ETL orchestration.

In practice, Step Functions enables organizations to orchestrate robust, scalable ETL workflows with minimal operational overhead, integrating seamlessly with other AWS services and supporting complex workflow requirements.

Question 139:

You want to query S3 datasets using SQL without provisioning infrastructure and pay only for the 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 datasets directly. It supports structured (CSV, Parquet) and semi-structured (JSON, ORC, Avro) formats. Integration with Glue Data Catalog enables automatic schema discovery, allowing immediate query access. Athena is pay-per-query, eliminating infrastructure costs. Analysts can run ad-hoc queries, generate dashboards, and perform exploratory analytics without managing clusters or servers.

Option B, Redshift, requires cluster provisioning. While Redshift Spectrum can query S3, Athena is simpler, serverless, and ideal for interactive and ad-hoc queries, reducing operational overhead.

Option C, EMR, allows querying S3 via Spark SQL or Hive but requires cluster provisioning and management. This introduces latency and increases complexity, making it less suitable for immediate, ad-hoc analytics.

Option D, Glue, is primarily an ETL and cataloging service. It cannot directly perform ad-hoc SQL queries, requiring data movement or ETL jobs, which increases complexity and latency.

In practice, Athena is the serverless, scalable, and cost-efficient solution for querying S3 datasets. It enables analysts to explore data lakes immediately, generate dashboards, and support agile analytics workflows with minimal management overhead.

Question 140:

You want to store IoT time-series data efficiently and perform real-time trend analysis. Which service is most suitable?

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 optimized for IoT and telemetry workloads. It automatically handles tiered storage, retention policies, and compression, separating hot and cold data for cost efficiency. Timestream provides time-series query functions, including aggregation, interpolation, smoothing, and trend detection, enabling real-time analytics and anomaly detection. It scales automatically to handle millions of events per second, supporting dashboards and operational monitoring with low latency.

Option B, DynamoDB, is a high-throughput key-value store. While it can store IoT data, it lacks native time-series query functions, requiring additional ETL, indexes, or schema redesign to perform trend analysis.

Option C, Redshift, is optimized for batch analytics. Continuous high-frequency ingestion requires ETL pipelines and cluster management, introducing latency and operational overhead, which limits real-time trend analysis.

Option D, RDS, is transactional and not designed for high-frequency time-series workloads. It cannot efficiently handle real-time trend detection or telemetry analytics.

In practice, Timestream provides a serverless, scalable, and cost-efficient solution for IoT and time-series analytics. It supports real-time trend detection, anomaly monitoring, and integration with visualization tools like QuickSight or Grafana, making it the ideal choice for modern IoT telemetry pipelines.