Amazon AWS Certified Data Engineer – Associate DEA-C01 Exam Dumps and Practice Test Questions Set 5 Q81-100

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Question 81:

You need to ingest streaming sensor data, transform it in real-time, and store it for time-series analytics. Which AWS architecture is best?

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 is an ideal architecture for high-volume IoT, telemetry, or sensor data ingestion due to its ability to handle streaming data in real time while providing serverless, scalable, and fault-tolerant processing. Amazon Kinesis Data Streams serves as the backbone for ingestion, capable of collecting and processing hundreds of thousands of events per second from multiple sources. Each stream is divided into shards, which provide parallelism and ordered delivery, ensuring that data is durable, consistent, and can be consumed by multiple downstream applications simultaneously. This is crucial for IoT scenarios, where data often arrives continuously from geographically distributed devices and must be reliably captured for analysis.

Once data is ingested into Kinesis Data Streams, AWS Lambda acts as the processing layer, enabling serverless transformations, enrichment, or filtering in near real time. Lambda eliminates the need to provision or manage compute infrastructure, automatically scaling to handle variations in incoming data volume. Functions can perform transformations such as normalizing sensor readings, filtering out invalid data, aggregating metrics, or enriching streams with metadata like device location or type. This allows pipelines to prepare data dynamically for analytics or storage, minimizing operational overhead and enabling faster insights without the complexity of maintaining clusters or servers.

The processed data is then stored in Amazon Timestream, a serverless, purpose-built time-series database optimized for storing, retrieving, and analyzing time-stamped telemetry data. Timestream is uniquely designed to handle time-series workloads, offering functions like aggregations, interpolation, smoothing, and trend analysis natively. It automatically manages hot and cold storage, retaining frequently accessed data in high-performance memory while moving older data to cost-efficient, lower-tier storage. This tiered storage mechanism allows for cost-effective long-term retention of historical sensor data while maintaining fast query performance for recent events. Timestream’s integration with SQL-based query engines and visualization tools like Amazon QuickSight or Grafana enables near-instant insights and monitoring dashboards, which are essential for operational analytics, anomaly detection, predictive maintenance, and real-time decision-making.

In contrast, Option B, SQS + RDS, is less suitable for real-time IoT pipelines. While SQS provides reliable message queuing, it is asynchronous and does not guarantee the ordering of messages in a way that is optimal for time-series analysis. RDS, being a relational database, is designed for transactional workloads rather than high-frequency, high-volume time-series ingestion. Real-time transformations would require additional compute infrastructure to process messages from SQS before writing to RDS, increasing complexity and operational overhead. Furthermore, aggregating, interpolating, or performing time-series functions on RDS data would require significant schema design and manual processing, making it inefficient for near-instant analytics.

Option C, SNS + DynamoDB, supports an event-driven architecture that can handle high throughput, but it lacks native time-series query capabilities. While DynamoDB can store large volumes of telemetry data, performing aggregations, trend analysis, or interpolation requires custom application logic or secondary indexing strategies. This increases engineering complexity and reduces query performance for analytics. SNS’s pub/sub model allows broadcasting events, but without a specialized time-series database, analyzing trends, detecting anomalies, or performing predictive analytics becomes cumbersome and resource-intensive.

Option D, Redshift + Firehose, is more appropriate for batch or micro-batch analytics rather than continuous, real-time ingestion. Kinesis Data Firehose buffers data before delivery, which introduces latency, and Redshift is optimized for structured, relational, and columnar storage for analytical queries. While it excels at complex reporting and historical analytics, it cannot provide the low-latency, high-frequency ingestion and query capabilities needed for real-time IoT telemetry or sensor data pipelines. This architecture is better suited for aggregated batch processing rather than instantaneous operational analytics.

In summary, Kinesis Data Streams + AWS Lambda + Amazon Timestream provides a fully serverless, highly scalable, and fault-tolerant architecture for real-time IoT and telemetry data pipelines. It ensures durable, ordered ingestion, enables on-the-fly transformations and enrichment, and supports specialized time-series queries and analytics. Compared to SQS + RDS, SNS + DynamoDB, or Redshift + Firehose, Option A minimizes operational overhead, provides native time-series analysis, and delivers near-instant insights, making it the optimal choice for high-volume, real-time telemetry and IoT data scenarios.

In practice, KDS + Lambda + Timestream provides a robust, serverless, and scalable pipeline for ingesting and analyzing IoT telemetry in near real-time, aligning with AWS best practices for real-time IoT analytics.

Question 82:

You want to catalog new 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

The correct answer is A) AWS Glue. AWS Glue is a fully managed, serverless ETL (Extract, Transform, Load) service designed to simplify the preparation and transformation of data for analytics, machine learning, and reporting. Unlike traditional ETL solutions, AWS Glue eliminates the need to provision or manage infrastructure, automatically scales to handle varying workloads, and integrates seamlessly with other AWS services such as Amazon S3, Amazon Redshift, Amazon RDS, and Amazon Athena.

AWS Glue provides several key features that make it the preferred choice for ETL pipelines. First, its serverless architecture allows developers and data engineers to focus on data transformation logic rather than infrastructure management. Glue automatically provisions the required resources, executes ETL jobs, and scales them based on data volume, which is particularly useful for nightly ETL workloads that process JSON, CSV, or other semi-structured data formats. This eliminates the operational overhead associated with managing clusters or compute resources in services like Amazon EMR.

Second, AWS Glue includes the Glue Data Catalog, a centralized metadata repository that stores table definitions, schemas, and partitions. This makes it easier to manage metadata across datasets, ensures data consistency, and enables integration with query services like Athena or Redshift Spectrum. Glue can automatically discover and catalog data using crawlers, reducing manual schema definitions and simplifying the management of evolving datasets. This feature is particularly beneficial when dealing with JSON files or other semi-structured formats where schema evolution is common.

Third, Glue supports both code-based and visual ETL development. Developers can write transformations using Python or Scala scripts, or use Glue Studio’s visual interface to design data workflows without writing code. Glue also supports job scheduling, workflow orchestration, and dependency management, enabling automated, repeatable ETL pipelines that can run nightly, hourly, or based on event triggers.

When compared to the other options, AWS Glue stands out. Amazon EMR (option B) is a managed Hadoop and Spark platform for large-scale distributed data processing, but it requires provisioning clusters, configuring nodes, and managing scaling. While EMR is powerful for complex batch analytics, it introduces significant operational overhead and is less suited for serverless, automated ETL workflows. Amazon RDS (option C) is a managed relational database service designed for transactional workloads and does not natively support ETL processing or semi-structured data transformation. Amazon Redshift (option D) is a data warehouse optimized for analytical queries on structured datasets; while it can be integrated with ETL pipelines, it does not provide automated, serverless ETL capabilities for preparing data before loading.

In summary, AWS Glue provides a fully managed, serverless, and scalable ETL solution that automates data discovery, transformation, and cataloging. Its integration with AWS analytics services, support for semi-structured and structured data, and automated job orchestration make it the optimal choice for creating reliable, repeatable ETL pipelines with minimal operational overhead. This combination of features ensures that Glue is the preferred service for preparing data for analytics, machine learning, and reporting workflows.

In practice, Glue provides a serverless, fully automated solution for discovering, cataloging, and making S3 datasets queryable. It reduces operational overhead, ensures metadata consistency, and allows analysts and engineers to focus on analytics rather than infrastructure management.

Question 83:

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 tasks across AWS services. It supports sequential, parallel, and conditional execution, retries, and error handling. It integrates seamlessly with Glue, Lambda, EMR, and Redshift, providing visual workflow monitoring and state tracking. Step Functions allows complex ETL workflows to run reliably with minimal operational effort, supporting parallel execution for efficiency and conditional branching for dynamic processing.

Option B, Glue, is primarily an ETL engine. Glue Workflows allow chaining of ETL jobs but lack advanced conditional logic, retries, and parallel orchestration capabilities present in Step Functions.

Option C, EMR, can process distributed workloads but does not provide native orchestration, retries, or workflow visualization. External orchestration is required.

Option D, Data Pipeline, is a legacy orchestration tool that lacks modern serverless features, parallel execution, and integrated monitoring.

In practice, Step Functions provides a scalable, serverless orchestration layer for complex ETL pipelines. Organizations can define workflows with conditional logic, automatic retries, and parallel execution, ensuring efficient, reliable ETL operations.

Question 84:

You want to query raw S3 datasets using SQL without provisioning servers, paying 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 

Amazon Athena is a fully managed, serverless SQL query service that enables users to analyze data directly in Amazon S3 without the need to provision, configure, or manage any servers. Athena allows ad-hoc querying of both structured and semi-structured datasets, supporting formats such as CSV, JSON, Parquet, ORC, and Avro. Its serverless architecture makes it highly scalable, automatically handling concurrency and query execution without manual intervention. Users are charged based on the amount of data scanned per query, making Athena a cost-efficient option for datasets of varying sizes. This flexibility, combined with immediate queryability, allows analysts and data scientists to gain insights from S3 data quickly without worrying about infrastructure or long ETL pipelines.

One of Athena’s key advantages is its integration with the AWS Glue Data Catalog, which provides a centralized metadata repository. The Glue Data Catalog stores schema definitions, partitions, and table metadata, enabling Athena to immediately understand the structure of datasets stored in S3. Crawlers can automatically detect schema changes and update the catalog, ensuring that evolving datasets remain queryable without manual intervention. This integration allows users to focus on analytics rather than schema management and also facilitates consistent governance, access control, and lineage tracking across multiple datasets. Athena’s support for partitioning and compression further improves performance and reduces costs by scanning only relevant subsets of data rather than the entire dataset.

When comparing Athena to the other options, the benefits become even clearer. Option B, Amazon Redshift, is a fully managed data warehouse optimized for high-performance analytical queries over structured datasets. Redshift requires loading data into tables and provisioning clusters, which introduces both operational overhead and fixed costs. While Redshift Spectrum extends Redshift’s ability to query S3 directly, it still requires a Redshift cluster, making Athena a simpler and fully serverless alternative. Athena is particularly well-suited for ad-hoc queries, exploratory analytics, and scenarios where query patterns are unpredictable, whereas Redshift is more efficient for consistent, repetitive, large-scale analytics workloads.

Option C, Amazon EMR, is a managed big data platform that supports distributed computing frameworks such as Apache Spark, Hive, and Presto. EMR can process S3 datasets effectively using Spark SQL or Hive queries, but it requires provisioning and managing clusters, configuring nodes, and handling scaling, which adds complexity and startup latency. For lightweight or sporadic ad-hoc queries, the overhead of EMR clusters can be prohibitive, whereas Athena allows instantaneous querying without waiting for clusters to start.

Option D, AWS Glue, focuses primarily on ETL (Extract, Transform, Load) and data cataloging. While Glue is excellent for preparing data, transforming formats, or cleaning datasets, it does not provide direct SQL query capabilities on raw S3 data without creating and executing ETL jobs. This introduces delays and additional operational steps before the data becomes queryable, making Glue less suitable for immediate, ad-hoc analytics compared to Athena.

In practice, Athena excels because it combines serverless architecture, instant queryability, cost efficiency, and broad data format support. Analysts and data scientists can run queries on raw S3 datasets without waiting for data transformation, cluster provisioning, or ETL pipelines. Athena also integrates with visualization and BI tools such as Amazon QuickSight, enabling the creation of dashboards and interactive reports directly on S3 data. Its scalability ensures that multiple users or teams can query large datasets simultaneously without performance degradation.

In summary, Amazon Athena provides the fastest, most cost-efficient, and scalable solution for querying S3 datasets. It supports both structured and semi-structured formats, leverages the Glue Data Catalog for schema management, and eliminates infrastructure overhead. Compared to Redshift, EMR, and Glue, Athena stands out for ad-hoc analytics, serverless querying, and immediate access to raw S3 data, making it the ideal choice for exploratory analysis, dashboarding, and lightweight analytics workloads.

Question 85:

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

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 data retention, tiered storage, and compression, separates hot and cold storage, and supports time-series functions such as aggregations, smoothing, and trend analysis. It scales automatically to ingest millions of events per second, providing real-time analytics with minimal operational overhead.

Option B, DynamoDB, is a high-throughput key-value store. It lacks native time-series querying functions. Trend analysis would require additional design patterns, secondary indexes, or ETL, increasing complexity.

Option C, Redshift, is a structured data warehouse optimized for batch analytics. Ingesting high-volume IoT data requires ETL pipelines, and querying trends is slower due to batch-oriented architecture.

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

Amazon Timestream is a purpose-built, serverless time-series database designed specifically for IoT telemetry, operational monitoring, and real-time analytics workloads. Unlike general-purpose databases, Timestream is optimized for handling large volumes of time-stamped data, such as sensor readings, device logs, or application metrics, while automatically managing storage, scaling, and performance. This allows organizations to focus on extracting insights from their data rather than managing infrastructure, provisioning servers, or tuning database performance.

One of Timestream’s key strengths is its serverless architecture. Users do not need to provision compute or storage resources in advance. The service automatically scales to accommodate high ingestion rates from millions of IoT devices, ensuring durability and availability. Data is ingested continuously through APIs or integrations with services like Amazon Kinesis Data Streams or AWS IoT Core. This scalability allows organizations to handle spikes in telemetry data without manual intervention, which is essential for IoT environments where data flow can be highly variable.

Timestream also provides automatic data tiering, separating data into hot and cold storage. Frequently accessed recent data resides in memory-optimized hot storage for low-latency queries, while older data is moved to cost-efficient magnetic storage. This tiered storage model ensures that organizations can retain long-term historical data at a lower cost while still maintaining fast query performance for recent events. As a result, time-series analytics become both cost-effective and high-performing, even for very large datasets spanning months or years.

For analytics, Timestream supports built-in time-series functions, such as aggregations over time windows, interpolation for missing values, smoothing, and anomaly detection. These functions are critical for analyzing IoT telemetry, which often involves identifying trends, calculating averages, or detecting unusual patterns in sensor readings. Queries are executed using a familiar SQL-like syntax, making it easy for developers, analysts, and data scientists to explore and analyze time-series data without complex transformations or custom code.

Timestream also integrates seamlessly with visualization and monitoring tools like Amazon QuickSight and Grafana. This allows organizations to build interactive dashboards, monitor device performance in real time, and share insights across teams. Combined with services like AWS Lambda or Kinesis Data Analytics, Timestream can form part of an end-to-end serverless analytics pipeline, processing raw telemetry data in real time, storing it efficiently, and providing near-instant visualization.

When compared to alternative architectures, Timestream is highly advantageous for IoT workloads. Traditional relational databases, such as Amazon RDS, struggle with high-frequency writes and large-scale time-series queries. NoSQL databases like DynamoDB can store time-stamped data, but performing aggregations, trend analysis, or interpolations requires complex secondary indexes and custom application logic. Even batch-oriented solutions like Redshift or EMR can be overkill for streaming IoT telemetry, introducing unnecessary latency and operational overhead. Timestream, by contrast, is purpose-built for these workloads and provides optimized storage, query functions, and serverless scalability out of the box.

In practice, this makes Timestream the recommended choice for organizations managing IoT devices or telemetry streams. It allows teams to analyze real-time trends, detect anomalies, forecast device behavior, and integrate seamlessly with monitoring dashboards—all without worrying about provisioning, scaling, or managing infrastructure. Its serverless, scalable, and cost-efficient design ensures that organizations can focus on generating insights and improving operational efficiency, rather than maintaining database clusters or ETL pipelines.

In summary, Amazon Timestream provides a robust, fully managed platform for IoT and time-series workloads, combining serverless scalability, built-in time-series analytics, automated storage management, and seamless visualization integration. It is ideal for real-time monitoring, trend analysis, and anomaly detection in IoT applications, enabling organizations to derive actionable insights from telemetry data efficiently and cost-effectively.

Question 86:

You want to ingest high-volume clickstream data, perform real-time transformations, 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 most suitable for real-time clickstream analytics. KDS provides high-throughput ingestion with durable, ordered delivery, allowing multiple consumers to process data simultaneously. KDA enables real-time transformations, aggregations, and filtering using SQL or Apache Flink applications in a serverless, scalable environment. OpenSearch allows low-latency querying, search, and visualization via Kibana dashboards. This architecture supports adaptive scaling, high availability, and fault tolerance without manual provisioning.

Option B, SQS + RDS, is unsuitable for real-time analytics. SQS is asynchronous, and RDS is optimized for transactional workloads. Implementing streaming would require polling and batch processing, introducing latency and operational complexity.

Option C, SNS + Redshift, supports event-driven ingestion but is more batch-oriented. Redshift is a data warehouse, and immediate dashboarding is limited due to micro-batch loading and latency.

Option D, EMR + S3, is optimized for batch processing, not sub-second real-time analytics. EMR requires cluster management and scaling, which increases operational overhead.

In practice, KDS + KDA + OpenSearch allows organizations to process millions of clickstream events per second with real-time insights, operational monitoring, and dashboarding. It aligns with AWS best practices for serverless, real-time streaming analytics.

Question 87:

You need 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 cataloging service. Glue crawlers scan S3 datasets, detect schema changes, and populate the Glue Data Catalog, making data immediately queryable through Athena or Redshift Spectrum. Glue supports structured, semi-structured, and nested formats such as CSV, JSON, Parquet, and ORC. It simplifies schema management, ensures metadata consistency, and reduces operational effort for analytics teams.

Option B, EMR, can process S3 datasets using Spark or Hive but does not provide automated cataloging. Manual schema management is required, increasing operational complexity.

Option C, RDS, is designed for transactional workloads. It cannot automatically detect or catalog new datasets.

Option D, Redshift, can query external S3 data via Spectrum but requires manual schema updates if new datasets are added. Without Glue integration, automation is limited.

In practice, Glue provides a serverless, automated, and scalable solution for cataloging S3 datasets, enabling analysts to query data immediately while reducing maintenance overhead.

Question 88:

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

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 tool that coordinates multiple workflows with sequential, parallel, and conditional execution, integrated retries, and error handling. It integrates with Glue, Lambda, EMR, and Redshift and provides visual workflow monitoring and state tracking. Step Functions allows complex ETL pipelines to execute reliably and efficiently, supporting parallel tasks for performance optimization.

Option B, Glue, is a managed ETL service but has limited orchestration capabilities. Glue Workflows can chain jobs but do not provide advanced conditional branching or robust parallel execution.

Option C, EMR, executes distributed data processing jobs but lacks orchestration, retries, and workflow visualization. External orchestration is required.

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

In practice, Step Functions enables organizations to build scalable, resilient, and maintainable ETL pipelines, reducing operational overhead while supporting complex conditional and parallel workflows.

Question 89:

You want to query raw S3 datasets using SQL without provisioning infrastructure, 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 formats (CSV, Parquet) and semi-structured formats (JSON, ORC, Avro). Integration with the Glue Data Catalog allows automatic schema discovery. Athena is pay-per-query, meaning cost efficiency, and serverless architecture eliminates the need for cluster management.

Option B, Redshift, is a data warehouse that requires cluster provisioning and ETL pipelines to load S3 data. While Redshift Spectrum allows external queries, it introduces complexity and operational overhead.

Option C, EMR, can query S3 via Spark SQL or Hive, but cluster management and startup latency reduce efficiency for ad-hoc analysis.

Option D, Glue, is primarily ETL and cataloging; it does not support direct SQL queries without creating an ETL job or loading data elsewhere.

In practice, Athena provides instant, serverless, and cost-efficient querying of S3 datasets for analytics, dashboards, and ad-hoc exploration.

Question 90:

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

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 handles data retention, tiered storage, and compression, supports time-series query functions such as aggregations and smoothing, and scales automatically to ingest millions of events per second. This enables real-time trend analysis with minimal operational overhead.

Option B, DynamoDB, is a high-throughput key-value store but lacks native time-series query functions, requiring complex design and additional ETL for trend analysis.

Option C, Redshift, is optimized for batch analytics. Continuous ingestion and querying of high-volume IoT data require ETL pipelines and cluster management, increasing complexity and cost.

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

In practice, Timestream provides a serverless, scalable, and cost-efficient platform for IoT analytics, allowing organizations to analyze real-time trends, detect anomalies, and integrate with visualization tools like QuickSight or Grafana without managing infrastructure.

Question 91:

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 financial data ingestion and analytics. KDS provides durable, ordered, and high-throughput ingestion, allowing multiple consumers to process streams simultaneously. Lambda performs serverless, real-time transformations, enrichment, and anomaly detection using custom logic or libraries. OpenSearch enables low-latency querying, search, aggregation, and visualization through Kibana dashboards. This architecture is fully serverless, scalable, and fault-tolerant, allowing organizations to detect fraud or anomalies in real-time with minimal operational overhead.

Option B, SQS + RDS, is asynchronous and transactional. SQS queues messages, and RDS is designed for transactional workloads. This architecture introduces latency, cannot process events in real-time efficiently, and is unsuitable for high-volume streaming anomaly detection.

Option C, SNS + Redshift, is suitable for event-driven batch ingestion but not real-time processing. Redshift is a data warehouse optimized for batch analytics, and immediate anomaly detection dashboards are difficult due to micro-batch loading latency.

Option D, EMR + S3, is a batch processing architecture. EMR requires cluster management and scaling, and S3 is an object store with high latency for frequent updates, making this option unsuitable for real-time alerts.

In practice, KDS + Lambda + OpenSearch provides a serverless, near-real-time analytics pipeline for high-frequency financial data. Organizations can process millions of transactions per second, detect anomalies immediately, and visualize trends or trigger alerts efficiently. This architecture is aligned with AWS best practices for real-time streaming analytics and operational monitoring.

Question 92:

You want 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 cataloging service. Glue crawlers automatically scan S3 datasets, detect schema changes, and populate the Glue Data Catalog, enabling immediate queryability via Athena or Redshift Spectrum. Glue supports structured and semi-structured formats (CSV, JSON, Parquet, ORC) and allows automated ETL transformations to prepare datasets for analytics.

Option B, EMR, can process datasets using Spark or Hive but does not automatically catalog new datasets. Manual metadata management or integration with Glue is required, increasing operational effort.

Option C, RDS, is designed for transactional workloads. It cannot automatically catalog datasets in a data lake.

Option D, Redshift, can query external S3 datasets using Redshift Spectrum but cannot detect new datasets automatically. Without Glue integration, schema updates are manual and operationally intensive.

In practice, Glue ensures serverless, automated cataloging, reduces manual intervention, maintains metadata consistency, and allows analysts to query new datasets immediately. Glue simplifies data lake management and enables scalable, automated analytics workflows.

Question 93:

You want to orchestrate multiple ETL workflows with conditional logic, retries, and parallel execution. 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 for coordinating workflows with sequential, parallel, and conditional execution, integrated retries, and error handling. It integrates with AWS services like Glue, Lambda, EMR, and Redshift, and provides visual monitoring and state tracking. Step Functions allows complex ETL pipelines to run reliably, supporting parallel tasks for improved throughput and conditional branching for dynamic decision-making.

Option B, Glue, is a managed ETL service but cannot provide advanced orchestration features like robust conditional execution, parallelism, and state management.

Option C, EMR, is a distributed processing platform that lacks native orchestration, retries, or workflow visualization. Workflow logic must be implemented externally, which increases operational complexity.

Option D, Data Pipeline, is a legacy orchestration service with limited features, not serverless, and lacks modern parallel execution or monitoring capabilities.

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 with minimal operational overhead

Question 94:

You want to query raw S3 datasets using 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 (CSV, Parquet) and semi-structured (JSON, ORC, Avro) formats. Athena integrates with the Glue Data Catalog, enabling automatic schema discovery and immediate query capability. Athena charges per query based on data scanned, making it cost-efficient and eliminating the need for cluster provisioning or management.

Option B, Redshift, is a data warehouse requiring 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, interactive querying.

Option C, EMR, is suitable for large-scale batch analytics. It requires cluster management, scaling, and startup time, which reduces efficiency for ad-hoc queries.

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

In practice, Athena provides a serverless, scalable, and cost-effective solution for querying S3 data, ideal for dashboards, reporting, and ad-hoc analysis with minimal operational overhead.

Question 95:

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 and telemetry workloads. It automatically manages data retention, tiered storage, and compression, separating hot and cold storage for cost efficiency. Timestream supports time-series queries, such as aggregations, interpolation, and smoothing, allowing fast trend analysis. It scales automatically to ingest millions of events per second, enabling real-time analytics with minimal operational effort.

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 trend analysis for high-frequency IoT data require ETL pipelines and cluster management, introducing latency and operational overhead.

Option D, RDS, is designed for transactional workloads. It cannot efficiently handle high-frequency time-series data or perform complex trend analysis.

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

Question 96:

You want to ingest high-velocity IoT data, transform it in real-time, and store it for time-series analytics with minimal operational overhead. Which 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 (KDS) + Lambda + Timestream, is the ideal architecture for high-frequency IoT ingestion and analytics. KDS ingests streaming data in real-time with durability and ordering guarantees, allowing multiple consumers to process concurrently. Lambda functions provide serverless, real-time transformation, filtering, and enrichment, eliminating the need for server management. Amazon Timestream, a serverless time-series database, is optimized for storing telemetry data with automatic tiered storage, compression, and retention policies. Timestream supports time-series queries such as aggregations, smoothing, and trend analysis, enabling near-instant analytics with minimal operational overhead.

Option B, SQS + RDS, is not suitable because SQS is asynchronous, and RDS is optimized for transactional workloads, not high-throughput, real-time time-series ingestion. Implementing this architecture would require additional polling and batch processes, introducing latency and complexity.

Option C, SNS + DynamoDB, supports event-driven messaging but lacks native time-series query and analytics capabilities, making trend detection complex. DynamoDB can store large volumes of data but requires additional design for aggregation and querying over time-series data.

Option D, Redshift + Kinesis Data Firehose, is better suited for batch-oriented analytics. Firehose buffers and delivers data in micro-batches, introducing latency. Redshift is optimized for structured analytics rather than continuous real-time time-series ingestion.

In practice, KDS + Lambda + Timestream provides a fully serverless, scalable, and fault-tolerant architecture for ingesting and analyzing IoT data streams. Organizations can process millions of events per second, perform real-time trend analysis, and integrate with dashboards and monitoring tools efficiently. This approach aligns with AWS best practices for real-time streaming analytics and IoT pipelines.

Question 97:

You need to automatically discover 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 catalog service that automatically discovers datasets in S3 using crawlers. Glue crawlers infer schema and populate the Glue Data Catalog, making data immediately queryable via Athena or Redshift Spectrum. Glue supports structured and semi-structured formats such as CSV, JSON, Parquet, and ORC, and enables automated ETL transformations to prepare datasets for analytics.

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

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

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

In practice, Glue ensures serverless, automated cataloging of S3 datasets. It reduces manual effort, ensures metadata consistency, and allows immediate queryability. This is critical for organizations managing dynamic data lakes where new datasets are continuously added.

Question 98:

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 tool for 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, and provides visual workflow monitoring and state tracking, allowing teams to manage complex ETL pipelines efficiently. Parallel execution improves throughput, and conditional branching supports dynamic workflow logic.

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 optimized for distributed data processing but lacks orchestration features. Workflow management must be implemented externally, 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, enabling scalable, reliable, and maintainable workflows.

Question 99:

You want to query raw S3 datasets using SQL without provisioning infrastructure 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 objects directly. It supports structured formats (CSV, Parquet) and semi-structured formats (JSON, ORC, Avro). Integration with the Glue Data Catalog allows automatic schema discovery, making datasets queryable immediately. Athena charges per query based on data scanned, providing a cost-efficient, serverless solution without the need to manage infrastructure.

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

Option C, EMR, can query S3 via Spark SQL or Hive but requires cluster provisioning, scaling, and startup, which adds latency and operational overhead.

Option D, Glue, is primarily ETL and cataloging; it 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 ad-hoc analytics, dashboards, and reporting, eliminating infrastructure management and simplifying S3 data querying.

Question 100:

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

Option B, DynamoDB, is a high-throughput key-value store but lacks native time-series querying. Trend analysis requires additional design and ETL, increasing complexity.

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 designed for transactional workloads. It 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 analyze trends, detect anomalies, and integrate with visualization tools like QuickSight or Grafana without managing infrastructure. Timestream is the recommended choice for serverless time-series analytics workloads.