Amazon AWS Certified Data Engineer – Associate DEA-C01 Exam Dumps and Practice Test Questions Set 6 Q101-120

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Question 101:

You want to stream IoT telemetry data, apply transformations in real-time, and store it for analytics and dashboards. Which AWS architecture is best?

A) Amazon Kinesis Data Streams + AWS Lambda + Amazon Timestream
B) Amazon SQS + Amazon RDS
C) Amazon SNS + Amazon 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 ideal for ingesting high-frequency IoT telemetry. Kinesis Data Streams provides durable, ordered, real-time ingestion, allowing multiple consumers to process events simultaneously. Lambda functions can perform real-time transformations, filtering, and enrichment, serverlessly. Timestream, a purpose-built time-series database, stores telemetry data efficiently, automatically manages tiered storage, and provides functions for trend analysis and aggregation. This combination ensures low-latency analytics for dashboards and anomaly detection with minimal operational overhead.

Option A, Amazon Kinesis Data Streams (KDS) + AWS Lambda + Amazon Timestream, is a purpose-built architecture for handling high-volume IoT and telemetry data in real time. Kinesis Data Streams provides a highly scalable, durable, and ordered platform for ingesting data from hundreds of thousands of devices simultaneously. Each stream is divided into shards, enabling parallel processing and allowing multiple consumers to read the same data independently. This ensures that data is not lost, arrives in the correct order, and can be processed reliably for real-time analytics. KDS also integrates seamlessly with other AWS services, enabling the construction of end-to-end, serverless streaming pipelines.

Once data is ingested into Kinesis, AWS Lambda acts as the processing layer. Lambda allows for serverless transformations, enrichment, or filtering of streaming data in near real time. Because it is serverless, there is no need to provision or manage compute resources; Lambda automatically scales to match the volume of incoming data. Lambda functions can normalize telemetry readings, enrich them with metadata, calculate derived metrics, or perform preliminary anomaly detection before sending the processed data to Timestream. This processing layer is crucial for ensuring that downstream analytics are accurate, meaningful, and optimized for querying.

Amazon Timestream is a serverless, purpose-built time-series database designed specifically for telemetry and IoT workloads. Unlike general-purpose databases, Timestream is optimized for high-frequency, time-stamped data and provides built-in functions for aggregations, interpolation, trend analysis, and anomaly detection. It automatically manages hot and cold storage, keeping recent data in memory-optimized storage for fast queries while moving historical data to cost-efficient storage tiers. This ensures organizations can retain long-term historical IoT data for analytics or compliance without incurring high storage costs. Timestream’s native support for time-series queries makes trend detection, forecasting, and monitoring straightforward, without the need for complex schema design or custom ETL.

Option B, SQS + RDS, is less suitable for real-time IoT analytics because SQS is a queue service that provides asynchronous message delivery. While it ensures durability and reliability, SQS does not provide ordered or low-latency ingestion suitable for high-frequency telemetry. Amazon RDS, being a relational database, is optimized for transactional workloads and structured data rather than high-throughput time-series ingestion. Attempting to use SQS and RDS for real-time IoT data would require additional processing layers, batching, or polling, introducing latency and increasing operational complexity. RDS also lacks native time-series functions, making real-time trend analysis cumbersome.

Option C, SNS + DynamoDB, is an event-driven architecture that can handle high-throughput ingestion. However, DynamoDB is a NoSQL database optimized for key-value and document storage, and it lacks native time-series querying capabilities. Performing trend analysis, aggregations, or interpolations on DynamoDB requires designing additional indexes, tables, or ETL processes, which increases complexity and operational overhead. While SNS can broadcast events to multiple consumers, it does not provide the low-latency, ordered streaming necessary for continuous IoT telemetry analytics.

Option D, Redshift + Firehose, is optimized for batch or micro-batch analytics rather than continuous real-time ingestion. Kinesis Data Firehose buffers incoming data before delivery, which introduces latency and prevents near-instant querying. Redshift, being a columnar data warehouse, is excellent for structured, analytical workloads over large datasets but is not designed for high-frequency, time-series ingestion. Loading data continuously into Redshift also requires ETL processes, creating delays and operational overhead that make it unsuitable for real-time telemetry analytics.

In practice, the Kinesis Data Streams + Lambda + Timestream architecture provides a serverless, scalable, and fault-tolerant solution for IoT telemetry. Organizations can ingest massive volumes of device data in real time, perform transformations and enrichments on-the-fly, and store the results in a purpose-built time-series database. Analysts and operations teams can perform near-instant trend analysis, anomaly detection, and monitoring dashboards using tools like Amazon QuickSight or Grafana without managing clusters, servers, or ETL pipelines. This architecture minimizes operational complexity, reduces latency, and ensures cost-efficient retention of historical data while providing immediate insights into device performance and operational metrics.

In summary, KDS + Lambda + Timestream is the recommended solution for real-time IoT analytics, combining durability, serverless scalability, low-latency processing, and time-series optimized querying. Other options like SQS + RDS, SNS + DynamoDB, or Redshift + Firehose either lack real-time capabilities, time-series functions, or require significant operational overhead, making them less suitable for high-frequency telemetry workloads.

If you want, I can also create a detailed visual architecture diagram showing data flowing from IoT devices into Kinesis, processed by Lambda, and stored in Timestream with dashboards in QuickSight or Grafana for real-time analytics. This would make the explanation even more practical.

Question 102:

You need to automatically discover S3 datasets 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 fully managed, serverless ETL (Extract, Transform, Load) service and metadata catalog that simplifies the preparation and transformation of data for analytics, machine learning, and reporting. One of Glue’s key strengths is its ability to automatically discover and catalog datasets stored in Amazon S3 through Glue Crawlers. Crawlers scan datasets, infer schemas, detect partitions, and populate the Glue Data Catalog, providing a centralized metadata repository. This allows analysts and data scientists to immediately query new datasets using services such as Amazon Athena or Redshift Spectrum without manual intervention, reducing operational overhead and accelerating time-to-insight. Glue supports both structured data formats like CSV and Parquet, and semi-structured formats such as JSON and ORC, making it highly flexible for modern data lake environments where data formats can vary or evolve.

Glue ETL jobs enable robust data transformation workflows. Users can write ETL scripts in Python or Scala, or use the visual Glue Studio interface to design transformations without writing code. Glue ETL allows filtering, cleaning, normalization, and enrichment of raw datasets before they are loaded into analytics platforms or warehouses. For example, JSON logs can be flattened into relational tables, missing values can be filled, and derived metrics can be computed during the ETL process. Glue also supports job scheduling, workflow orchestration, and dependency management, enabling fully automated, repeatable pipelines for nightly, hourly, or event-driven ETL workloads. This combination of automated cataloging and transformation allows organizations to maintain consistent metadata and structured datasets in a rapidly evolving data lake environment.

In comparison, Option B, Amazon EMR, is a managed big data platform that supports distributed computing frameworks such as Apache Spark, Hive, Presto, and HBase. EMR is highly flexible and capable of processing extremely large datasets, but it does not provide automated cataloging of data in S3. Schema management in EMR is largely manual, requiring users to define tables, partitions, and metadata in Hive Metastore or integrate with Glue Data Catalog. While EMR is suitable for complex batch processing and large-scale analytics, the operational overhead of managing clusters, scaling resources, and maintaining schema consistency makes it less efficient for agile, serverless ETL workflows compared to Glue.

Option C, Amazon RDS, is a managed relational database optimized for transactional workloads. While RDS excels at structured data storage and online transaction processing (OLTP), it does not natively support automated discovery, schema inference, or cataloging of S3 datasets. Data must be loaded explicitly, and schema updates require manual intervention. RDS is therefore unsuitable for dynamic data lake environments or ad hoc analytics, where datasets are frequently updated or newly added.

Option D, Amazon Redshift, is a data warehouse designed for high-performance analytical queries over structured data. Redshift can query external datasets stored in S3 using Redshift Spectrum, which extends its query engine to access data outside the cluster. However, Redshift does not automatically detect new datasets in S3; users must define external schemas or integrate with Glue to maintain metadata consistency. Manual schema updates or ETL workflows are required to ensure new datasets are queryable, increasing operational complexity and slowing time-to-insight compared to Glue’s fully automated cataloging.

In practice, AWS Glue provides a highly efficient, serverless solution for modern ETL and metadata management. Automated crawling and cataloging reduce operational overhead, ensure consistent metadata across datasets, and enable analysts to query newly added or updated data immediately. Its support for both structured and semi-structured data, combined with flexible ETL transformations, makes it ideal for data lake architectures where data formats and schemas are constantly evolving. By integrating seamlessly with Athena, Redshift Spectrum, and other analytics services, Glue enables agile, self-service analytics while maintaining metadata governance and consistency.

In summary, AWS Glue stands out because it combines automated metadata discovery, serverless scalability, ETL transformation capabilities, and integration with analytics services. Compared to EMR, RDS, and Redshift, Glue minimizes manual schema management, cluster provisioning, and operational overhead, enabling organizations to implement agile, efficient, and dynamic data pipelines. It is the preferred choice for centralized cataloging, schema inference, and serverless ETL workflows in modern cloud-based data architectures.

Question 103:

You want to orchestrate multiple ETL workflows with conditional branching, retries, and parallel execution. 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 that coordinates multiple AWS tasks. It supports sequential, parallel, and conditional execution, integrated retries, and error handling. Step Functions integrates with Glue, Lambda, EMR, and Redshift and provides visual monitoring, state tracking, and error diagnostics. Parallel execution allows multiple tasks to run simultaneously, improving throughput, and conditional branching supports dynamic workflows.

Option B, Glue, is a managed ETL service with limited orchestration. Glue Workflows can chain jobs but lack advanced features such as conditional logic, parallelism, and comprehensive monitoring.

Option C, EMR, is a distributed processing platform but does not natively orchestrate workflows, requiring external logic for sequencing, retries, and conditional execution.

Option D, Data Pipeline, is a legacy tool that is not fully serverless and lacks modern orchestration features like parallel execution, state tracking, and error handling.

In practice, Step Functions is ideal for building robust, maintainable, and scalable ETL pipelines, ensuring reliable execution of complex workflows with minimal operational overhead.

Question 104:

You want to query raw S3 datasets using SQL without provisioning servers 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) data. Athena integrates with the Glue Data Catalog, enabling automatic schema discovery and immediate query capability. Athena charges per query based on data scanned, providing a cost-effective, serverless solution without infrastructure management.

Option B, Redshift, requires cluster provisioning and ETL pipelines to load S3 data. Redshift Spectrum can query external datasets, but Athena is simpler, fully serverless, and ideal for ad-hoc queries.

Option C, EMR, can query S3 using Spark SQL or Hiv, but requires cluster management and startup time, which adds latency and operational complexity.

Option D, Glue, is primarily an ETL and cataloging service and cannot query S3 datasets directly without creating ETL jobs or exporting data elsewhere.

In practice, Athena provides a serverless, scalable, and cost-efficient solution for querying S3 datasets, ideal for dashboards, reporting, and ad-hoc analytics without infrastructure overhead.

Question 105:

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 telemetry workloads. It automatically manages tiered storage, compression, and data retention, separating hot and cold storage to optimize cost. Timestream provides time-series query functions, including aggregations, smoothing, and trend analysis, enabling real-time insights. It scales automatically to ingest millions of events per second, providing near-instant analytics with minimal operational overhead.

Option B, DynamoDB, can store IoT data but lacks native time-series querying capabilities, requiring additional ETL or schema design for trend analysis.

Option C, Redshift is optimized for batch analytics. Continuous high-frequency ingestion and trend analysis require ETL pipelines and cluster management, increasing latency and complexity.

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

In practice, Timestream provides a serverless, scalable, and cost-efficient solution for storing and analyzing IoT telemetry. It allows organizations to perform real-time trend analysis, anomaly detection, and to integrate with visualization tools like QuickSight or Grafana without managing infrastructure.

Question 106:

You want to ingest high-volume clickstream data, transform it in real-time, and store it for analytics dashboards with minimal latency. 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, is ideal for real-time clickstream ingestion and analytics. KDS provides durable, ordered, and high-throughput ingestion, allowing multiple consumers to process data simultaneously. KDA enables serverless real-time transformations, filtering, and aggregations using SQL or Apache Flink applications. OpenSearch allows low-latency search, aggregation, and visualization via Kibana dashboards. This architecture ensures near-instant insights into clickstream events while being fully serverless and scalable.

Option B, SQS + RDS, is asynchronous and transactional. RDS is optimized for structured relational workloads and does not support high-throughput streaming, making it unsuitable for real-time analytics.

Option C, SNS + Redshift, supports event-driven batch ingestion. Redshift is a data warehouse optimized for structured analytics, and micro-batch loading introduces latency, which is not suitable for real-time dashboards.

Option D, EMR + S3, is optimized for batch processing. EMR requires cluster management, and S3 is a high-latency object store. This architecture is better suited for large-scale batch analytics rather than near-real-time dashboards.

In practice, KDS + KDA + OpenSearch provides a serverless, scalable, and low-latency solution for clickstream analytics. It aligns with AWS best practices for real-time streaming analytics and dashboarding with minimal operational overhead.

Question 107:

You need to catalog S3 datasets automatically, making 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 S3 datasets, detect schema changes, and populate the Glue Data Catalog, enabling immediate queryability in Athena or Redshift Spectrum. Glue supports structured and semi-structured formats such as CSV, JSON, Parquet, and ORC. ETL jobs in Glue enable data transformations, filtering, and enrichment.

Option B, EMR, can process S3 data using Spark or Hive, but does not provide automated cataloging. Schema management must be handled manually or integrated with Glue.

Option C, RDS, is a transactional database and cannot automatically catalog S3 datasets.

Option D, Redshift, can query S3 data via Spectrum, but new datasets are not automatically detected. Without Glue integration, schema updates must be manual, increasing operational overhead.

In practice, Glue provides serverless, automated cataloging, ensuring metadata consistency and allowing analysts to query new datasets immediately, supporting dynamic data lake environments.

Question 108:

You want to orchestrate multiple ETL workflows with conditional execution, retries, and parallel processing. 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 tool that supports sequential, parallel, and conditional execution, integrated retries, and error handling. It integrates with Glue, Lambda, EMR, and Redshift, and provides visual monitoring, state tracking, and error diagnostics. Step Functions allows complex ETL pipelines to execute reliably, supporting parallel execution and conditional branching for dynamic workflows.

Option B, Glue, is a managed ETL service but has limited orchestration capabilities. Glue Workflows can chain jobs but cannot handle complex conditional logic, robust retries, or advanced parallel execution.

Option C, EMR, is a distributed processing platform but lacks orchestration capabilities. External workflow management is required, increasing operational complexity.

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

In practice, Step Functions is the preferred choice for orchestrating complex ETL pipelines with robust error handling, parallel execution, and conditional logic, providing scalable and maintainable workflows.

Question 109:

You want to query raw S3 datasets using SQL without provisioning servers 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 the Glue Data Catalog enables automatic schema discovery and immediate queryability. Athena charges per query based on data scanned, providing a cost-efficient serverless solution.

Option B, Redshift, requires cluster provisioning and ETL pipelines to load S3 data. While Redshift Spectrum can query external datasets, Athena is simpler, serverless, and optimized for ad-hoc queries.

Option C, EMR, can query S3 using Spark SQL or Hive, but requires cluster management and startup latency, which reduces efficiency for ad-hoc queries.

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

In practice, Athena provides a serverless, scalable, and cost-efficient solution for querying S3 datasets, suitable for dashboards, reporting, and ad-hoc analytics without infrastructure management.

Question 110:

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 optimized for IoT workloads. It automatically manages tiered storage, compression, and retention policies, separating hot and cold data for cost optimization. Timestream supports time-series query functions such as aggregations, interpolation, and smoothing, allowing near-real-time trend analysis. It scales automatically to handle millions of events per second, enabling low-latency analytics with minimal operational overhead.

Option B, DynamoDB, is a high-throughput key-value store. While it can store IoT data, it lacks native time-series querying, 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, introducing latency and operational overhead.

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 organizations to perform real-time trend analysis, anomaly detection, and to integrate with visualization tools like QuickSight or Grafana without managing infrastructure.

Question 111:

You want to ingest streaming financial transaction data, detect anomalies in real-time, and store results 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, is ideal for real-time ingestion and analytics of financial transactions. KDS provides durable, ordered, and high-throughput ingestion, enabling multiple consumers to process events simultaneously. Lambda functions can perform real-time transformation, enrichment, or anomaly detection using custom logic or libraries. OpenSearch provides low-latency search, aggregation, and visualization via Kibana dashboards. This architecture is fully serverless, scalable, and fault-tolerant, allowing near-instant detection of fraud or anomalies.

Option B, SQS + RDS, is asynchronous and transactional. SQS queues messages, and RDS is optimized for structured relational workloads. This setup introduces latency and cannot efficiently process high-frequency events in real-time, making it unsuitable for anomaly detection.

Option C, SNS + Redshift, is suitable for batch-oriented analytics. Redshift is a data warehouse, and micro-batch ingestion introduces latency, which is not appropriate for real-time dashboards or alerts.

Option D, EMR + S3, is a batch-processing architecture. EMR requires cluster provisioning and management, and S3 has high latency for frequent updates, making it unsuitable for real-time anomaly detection and dashboards.

In practice, KDS + Lambda + OpenSearch provides a serverless, scalable, and low-latency pipeline for financial transaction monitoring. It allows organizations to process millions of events per second, detect anomalies immediately, and visualize trends and alerts efficiently. This architecture aligns with AWS best practices for real-time streaming analytics and operational monitoring.

Question 112:

You want to catalog S3 datasets automatically and 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 S3 datasets, detect schema changes, and populate the Glue Data Catalog, making datasets 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 transformations, filtering, and enrichment before analytics.

Option B, EMR, can process S3 datasets using Spark or Hive, but does not provide automated cataloging. Schema management must be done manually or integrated with Glue, increasing operational overhead.

Option C, RDS, is a relational database optimized for transactional workloads. It cannot automatically detect or catalog S3 datasets.

Option D, Redshift, can query external S3 datasets via Spectrum, but new datasets are not automatically discovered. Manual schema updates or Glue integration are required.

In practice, Glue ensures automated cataloging, reduces manual intervention, maintains metadata consistency, and allows analysts to query new datasets immediately. This is essential for dynamic data lake environments where datasets are constantly added.

Question 113:

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 that coordinates workflows across multiple AWS services. It supports sequential, parallel, and conditional execution, integrated retries, and error handling. Step Functions integrates with Glue, Lambda, EMR, and Redshift, providing visual workflow monitoring and state tracking. Parallel execution allows multiple tasks to run simultaneously, improving throughput, and conditional branching supports dynamic decisions based on real-time events.

Option B, Glue, is primarily an ETL service with limited orchestration capabilities. Glue Workflows can chain jobs, but cannot handle complex conditional logic or robust parallel execution.

Option C, EMR, is optimized for distributed processing but lacks native orchestration features. Workflow sequencing, retries, and conditional logic must be implemented externally.

Option D, Data Pipeline, is a legacy tool that is not fully serverless and lacks modern orchestration features, including robust error handling and parallel execution.

In practice, Step Functions is the preferred choice for orchestrating complex ETL workflows with high reliability, maintainability, and scalability, ensuring tasks run efficiently with minimal operational overhead.

Question 114:

You want to query raw S3 datasets using SQL without provisioning infrastructure and pay only fothe r 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. Athena integrates with the Glue Data Catalog for automatic schema discovery and immediate queryability. Athena charges per query based on data scanned, providing a cost-efficient, serverless solution without infrastructure management.

Option B, Redshift, requires cluster provisioning and ETL pipelines to load S3 data. Redshift Spectrum allows external querying, but Athena is simpler, fully serverless, and ideal for ad-hoc queries.

Option C, EMR, can query S3 using Spark SQL or Hiv, but requires cluster provisioning and management, adding latency and operational complexity.

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

In practice, Athena provides a serverless, scalable, and cost-effective solution for querying S3 datasets, ideal for dashboards, reporting, and ad-hoc analytics without infrastructure overhead.

Question 115:

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 optimized for IoT telemetry workloads. It automatically manages tiered storage, compression, and retention policies, separating hot and cold data to optimize cost. Timestream supports time-series query functions, including aggregations, interpolation, and smoothing, enabling near-real-time trend analysis. It scales automatically to handle millions of events per second, providing low-latency analytics with minimal operational overhead.

Option B, DynamoDB, is a high-throughput key-value store. While it can store IoT data, it lacks native time-series querying, 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, introducing latency and operational overhead.

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 organizations to perform real-time trend analysis, anomaly detection, and to integrate with visualization tools like QuickSight or Grafana without managing infrastructure.

Question 116:

You want to ingest high-volume clickstream data, perform real-time analytics, and make it available for dashboards with minimal latency. 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, is the ideal architecture for real-time clickstream analytics. KDS provides durable, high-throughput, ordered ingestion, allowing multiple consumers to process events simultaneously. For example, multiple Lambda functions, KDA applications, or analytics consumers can read the same stream without conflicts. KDA provides serverless real-time stream processing, supporting SQL or Apache Flink applications for filtering, aggregation, and enrichment of clickstream events. OpenSearch, with Kibana, provides low-latency search, visualization, and dashboarding, enabling operational teams and analysts to react immediately to user behavior or traffic patterns.

Option B, SQS + RDS, is primarily asynchronous and transactional. While SQS can queue clickstream messages, RDS is optimized for structured relational workloads, not real-time streaming. To achieve real-time analytics, developers would need complex polling mechanisms, batch inserts, or triggers. This introduces latency, bottlenecks, and operational complexity, making it unsuitable for sub-second dashboard updates.

Option C, SNS + Redshift, supports event-driven batch ingestion. SNS can publish messages to multiple subscribers, but Redshift is a data warehouse designed for structured batch analytics. Micro-batch loading with Redshift Spectrum or COPY commands introduces latency, preventing real-time dashboards. It is better suited for historical trend analysis rather than continuous event monitoring.

Option D, EMR + S3, is optimized for batch processing of large datasets. EMR clusters are manually provisioned or auto-scaled, which introduces startup latency. S3, being object storage, has high read/write latency for real-time ingestion scenarios. While EMR can process large volumes of historical clickstream data efficiently, it is not suitable for low-latency, real-time analytics or operational dashboards.

In practice, KDS + KDA + OpenSearch provides a serverless, scalable, fault-tolerant architecture for ingesting millions of clickstream events per second. It enables real-time data transformations and immediate visualization on dashboards. Organizations can monitor user behavior, detect anomalies, and generate alerts instantly. The serverless nature ensures minimal operational overhead, automatic scaling, and cost efficiency because you pay only for what you use. This architecture aligns with AWS best practices for real-time analytics, low-latency dashboards, and operational observability, allowing analytics teams to respond instantly to emerging trends.

Question 117:

You need to catalog S3 datasets automatically, making 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 catalog service that can automatically discover datasets in S3 using crawlers. Glue crawlers scan directories, detect schemas, and infer table structures. Once crawlers are complete, the Glue Data Catalog is populated, allowing Athena or Redshift Spectrum to query the datasets immediately. Glue supports structured formats like CSV, Parquet, ORC, and semi-structured formats such as JSON and Avro. It also supports ETL transformations, filtering, and enrichment of datasets for analytics. For organizations managing dynamic data lakes, Glue enables automatic schema updates, reducing manual intervention and maintaining metadata consistency.

Option B, EMR, is suitable for processing large datasets using Spark or Hive, but does not automatically catalog new data. Users must manage Hive metastore manually or integrate with Glue to achieve automated cataloging. This increases operational complexity and can introduce metadata inconsistencies.

Option C, RDS, is a transactional database service and cannot automatically detect S3 datasets. While you could load data into RDS, doing so defeats the purpose of a serverless, query-on-demand architecture.

Option D, Redshift, can query external S3 datasets via Redshift Spectrum. However, new datasets are not automatically detected. Without Glue integration, schema updates must be performed manually, adding operational overhead.

In practice, AWS Glue is the recommended solution for automated cataloging in a data lake environment. It reduces operational overhead, maintains accurate metadata, and allows analysts and data scientists to query new datasets immediately. Organizations can manage large volumes of S3 datasets efficiently while keeping analytics pipelines agile. Glue’s serverless nature ensures scalability and cost efficiency, paying only for actual processing, making it the ideal choice for automated discovery and querying of S3 datasets.

Question 118:

You want to orchestrate multiple ETL workflows with conditional branching, 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 workflow orchestration service that coordinates tasks across multiple AWS services. It supports sequential, parallel, and conditional execution, integrated retries, error handling, and state management. Step Functions integrates with Glue, Lambda, EMR, and Redshift, providing visual workflow monitoring, logging, and debugging capabilities. For ETL workflows, Step Functions allows complex pipelines to execute reliably, enables dynamic decision-making, and supports parallel execution of tasks for improved throughput and efficiency.

Option B, Glue, is a managed ETL service with limited orchestration capabilities. Glue Workflows can chain jobs but cannot handle advanced conditional logic, parallelism, or integrated retry strategies as robustly as Step Functions.

Option C, EMR, is a distributed processing platform that executes Spark, Hive, or Presto jobs. While EMR is excellent for batch processing, it does not natively orchestrate workflows. External orchestration is required, increasing operational complexity.

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

In practice, Step Functions is ideal for orchestrating complex ETL workflows. It allows organizations to implement robust, scalable, and maintainable pipelines with built-in error handling, retry logic, and conditional execution. Teams can manage dynamic data pipelines efficiently while minimizing operational overhead, making it the preferred choice for modern ETL orchestration.

Question 119:

You want to query raw S3 datasets using SQL without provisioning servers 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 allows direct querying of S3 datasets. It supports structured formats (CSV, Parquet) and semi-structured formats (JSON, ORC, Avro). Integration with the Glue Data Catalog provides automatic schema discovery, enabling immediate query access. Athena is pay-per-query, allowing cost-efficient analysis without managing infrastructure or clusters. Analysts can run ad-hoc queries, generate reports, or create dashboards instantly.

Option B, Redshift, is a data warehouse that requires cluster provisioning. While Redshift Spectrum allows querying external S3 datasets, it is less flexible and introduces management overhead compared to Athena.

Option C, EMR, allows querying S3 with Spark SQL or Hive but requires cluster management, provisioning, and startup time, which reduces efficiency for ad-hoc queries.

Option D, Glue, is primarily an ETL and cataloging service. While it can prepare data for analysis, Glue cannot directly query S3 datasets using SQL without creating ETL jobs or moving data.

In practice, Athena is the best solution for serverless, ad-hoc querying of S3 datasets. It is scalable, fully managed, and cost-efficient. Analysts can perform interactive queries without waiting for cluster provisioning, making it ideal for dashboards, reporting, and exploration of S3 data.

Question 120:

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 telemetry. It automatically manages tiered storage, retention policies, and compression, separating hot and cold data to optimize cost. Timestream provides time-series query functions, including aggregations, smoothing, and interpolation, enabling near real-time trend analysis. It scales automatically to handle millions of events per second, supporting low-latency analytics with minimal operational overhead.

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

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

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. Organizations can analyze trends in real time, detect anomalies, and integrate with visualization tools like QuickSight or Grafana without managing infrastructure. Timestream is the recommended solution for time-series analytics workloads, supporting efficient storage, query performance, and operational simplicity.