Amazon AWS Certified Data Engineer – Associate DEA-C01 Exam Dumps and Practice Test Questions Set 3 Q41-60

Visit here for our full Amazon AWS Certified Data Engineer – Associate DEA-C01 exam dumps and practice test questions.

Question 41:

You want to analyze streaming application metrics in real-time and trigger alerts if thresholds are breached. Which AWS service combination is best?

A) Amazon CloudWatch + Amazon SNS
B) Amazon S3 + Amazon Athena
C) Amazon Redshift + AWS Glue
D) Amazon EMR + Amazon SQS

Answer: A) Amazon CloudWatch + Amazon SNS

Explanation

Option A, CloudWatch + SNS, is the best solution for real-time monitoring and alerting. CloudWatch collects metrics, logs, and events from AWS services and custom applications. You can define alarms for thresholds on metrics like CPU utilization, request latency, or custom application metrics. When an alarm is triggered, SNS can notify administrators via email, SMS, or invoke Lambda functions for automated remediation. This combination ensures instantaneous alerts, supports horizontal scalability, and minimizes operational management.

Option B, S3 + Athena, is batch-oriented. While Athena allows queries over large datasets in S3, it cannot provide real-time monitoring or immediate alerting. Using this combination introduces latency and is unsuitable for operational alerting.

Option C, Redshift + Glue, is primarily for analytics on structured datasets. Redshift queries are batch-based, and Glue performs ETL. Neither service is optimized for real-time alerting or monitoring.

Option D, EMR + SQS, can process streaming data but does not natively provide real-time metric evaluation or alerting. Implementing alerts would require custom orchestration, increasing complexity and operational overhead.

Thus, CloudWatch + SNS is a serverless, scalable, and reliable solution for monitoring streaming application metrics in real-time, supporting both automated and manual responses to threshold breaches.

Question 42:

You want to catalog and discover new datasets in your S3-based data lake automatically, making them queryable via Athena and Redshift Spectrum. Which AWS service should you use?

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

Answer: A) AWS Glue

Explanation

Option A, AWS Glue, provides a serverless data catalog and automatic schema discovery. Glue crawlers scan data in S3, infer schema, and populate the Glue Data Catalog, making datasets immediately queryable with Athena or Redshift Spectrum. Glue supports structured, semi-structured, and nested formats (CSV, JSON, Parquet, ORC). It also supports ETL transformations using Python or Scala.

AWS Glue is a fully managed, serverless ETL (Extract, Transform, Load) service that simplifies the process of preparing and transforming data for analytics and machine learning. Among the options provided, AWS Glue is the most suitable solution for scenarios requiring automated, scalable, and serverless data transformation workflows. Glue is specifically designed to handle data integration at scale, supporting a wide variety of structured, semi-structured, and unstructured data sources. It abstracts much of the complexity involved in setting up ETL pipelines by automatically generating code, managing the underlying infrastructure, and providing built-in schedulers for workflow automation.

One of the key features that makes AWS Glue stand out is its serverless architecture. Unlike traditional ETL solutions, there is no need to provision or manage servers, clusters, or compute resources. Glue automatically scales its processing capacity based on the volume of data, ensuring cost efficiency and optimal performance. This scalability is crucial for organizations that process large datasets, such as nightly JSON logs, IoT telemetry, or transactional records. Users only pay for the resources consumed during ETL execution, which makes Glue a cost-effective solution compared to fixed infrastructure approaches.

AWS Glue also provides a centralized Data Catalog, which acts as a metadata repository for all data sources, including those in Amazon S3, RDS, Redshift, and other AWS services. This centralized catalog simplifies data governance, lineage tracking, and schema management. It enables users to discover and understand the structure of datasets, whether they are structured tables or semi-structured JSON files. Glue’s schema inference capabilities automatically detect data formats and changes, reducing manual effort and minimizing errors during transformations. This is particularly valuable when dealing with dynamic or evolving datasets.

The integration capabilities of AWS Glue further reinforce its suitability. Glue can seamlessly connect with Amazon Redshift, Amazon S3, Amazon RDS, and other AWS analytics services such as Athena and QuickSight. For instance, after transforming JSON files or relational data, Glue can load the processed output directly into Redshift for analytics or into S3 for long-term storage and further querying with Athena. This end-to-end integration ensures that data pipelines are streamlined and automated, reducing operational overhead and accelerating insights.

Comparing Glue with the other options clarifies its unique advantages. Amazon EMR (option B) is a managed Hadoop and Spark platform designed for big data processing and analytics. While EMR is powerful for large-scale batch processing and complex transformations, it requires cluster provisioning, configuration, and management, which increases operational complexity. EMR is ideal for custom big data frameworks but may be overkill for standard ETL workflows that require serverless automation and easy integration with AWS analytics services.

Amazon RDS (option C) is a managed relational database service. While RDS provides highly available and scalable relational storage, it is not designed for ETL or data transformation workflows. RDS is optimized for transactional workloads, not large-scale batch or stream processing. Similarly, Amazon Redshift (option D) is a data warehousing solution that excels at complex analytical queries over structured datasets. However, Redshift is not a general-purpose ETL tool—it requires data to be preprocessed and formatted appropriately before loading, making it less suitable for automated JSON transformations or unstructured data ingestion.

In summary, AWS Glue combines serverless architecture, automated schema detection, centralized metadata management, and seamless integration with other AWS analytics services, making it the preferred solution for ETL workflows. Unlike EMR, RDS, or Redshift, Glue eliminates the need for infrastructure management, supports diverse data formats, and allows organizations to automate nightly ETL jobs efficiently. It enables businesses to transform raw data into clean, analyzable datasets quickly, accelerating analytics, reporting, and machine learning initiatives while maintaining cost efficiency and operational simplicity. For organizations seeking a scalable, automated, and fully managed ETL service, AWS Glue is the optimal choice.

Option B, EMR, is a cluster-based data processing platform. While EMR can process large-scale datasets, it does not automatically catalog metadata. Managing schemas and enabling Athena or Redshift Spectrum access requires additional Glue integration.

Option C, RDS, is a relational database for OLTP workloads. It does not provide data lake cataloging or automatic schema discovery, making it unsuitable for S3 data lakes.

Option D, Redshift, is a data warehouse optimized for structured data analytics. Redshift Spectrum allows querying S3, but without Glue, it cannot automatically discover new datasets. Manual metadata management is required.

Thus, AWS Glue is the best choice for automated cataloging, schema discovery, and integration with query services in a serverless, scalable manner.

Question 43:

You want to move large volumes of streaming data from Kinesis to S3 with automatic compression and formatting, minimizing infrastructure management. Which service should you use?

A) Amazon Kinesis Data Firehose
B) Amazon Kinesis Data Streams
C) Amazon SQS
D) Amazon SNS

Answer: A) Amazon Kinesis Data Firehose

Explanation

The correct answer is A) Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is a fully managed service designed specifically for reliably loading streaming data into data lakes, data stores, and analytics services, making it ideal for near real-time ingestion pipelines. Unlike Kinesis Data Streams, which provides raw streaming data that requires custom application logic to process and deliver to destinations, Firehose abstracts much of that complexity by automatically capturing, transforming, and delivering streaming data to destinations such as Amazon S3, Amazon Redshift, Amazon Elasticsearch Service (OpenSearch), or third-party services.

Firehose is fully serverless, meaning there is no infrastructure to provision or manage. It scales automatically to handle varying throughput, ensuring that high-volume streams are processed efficiently without manual intervention. It also provides built-in data transformation capabilities through AWS Lambda, allowing users to clean, enrich, or reformat data before delivery. For example, JSON logs from applications can be flattened, filtered, or converted into Parquet or ORC formats before being stored in S3 for analytics. This streamlines the ETL process, reduces operational overhead, and ensures that downstream analytics tools receive ready-to-use, well-structured data.

Firehose also supports buffering, batching, compression, and encryption of data in transit, providing both performance optimization and security. Buffering allows Firehose to accumulate records for a short period or until a specific size is reached before delivering them, which improves throughput and cost efficiency. Integration with AWS Key Management Service (KMS) ensures encrypted data delivery, meeting compliance and security requirements.

The other options do not provide the same level of managed, near real-time delivery. Amazon Kinesis Data Streams (option B) requires building custom consumer applications to read, process, and push data to downstream services, making it more flexible but also more operationally intensive. Amazon SQS (option C) is a message queuing service designed for decoupling components of distributed applications; it is reliable but not optimized for streaming analytics. Amazon SNS (option D) is a pub/sub messaging service that broadcasts messages to multiple subscribers, but it is not designed to batch, transform, or deliver high-throughput streams to analytics destinations efficiently.

In summary, Amazon Kinesis Data Firehose is the best choice for scenarios where streaming data needs to be ingested, optionally transformed, and delivered reliably to analytics or storage services in near real time. Its serverless architecture, automatic scaling, built-in transformations, and seamless integration with AWS analytics services make it the most suitable solution among the options provided.

Option A, Kinesis Data Firehose, is a fully managed streaming ingestion service that delivers data to S3, Redshift, Elasticsearch, or Splunk. Firehose handles buffering, batching, compression, format conversion (e.g., JSON to Parquet), and automatic retry on failure. It is serverless and scales automatically with incoming traffic, eliminating infrastructure management.

Option B, Kinesis Data Streams, provides raw streaming ingestion but requires additional processing for delivery, transformation, and compression. You would need Lambda or custom consumers to handle formatting, increasing complexity.

Option C, SQS, is a message queue. While it can store messages, it lacks streaming transformations, batching, and delivery mechanisms to S3 for analytics workloads.

Option D, SNS, is a pub/sub messaging service. SNS is excellent for fan-out notifications but does not provide streaming delivery, compression, or transformations.

Thus, Kinesis Data Firehose is the simplest, serverless, and cost-effective solution for ingesting streaming data into S3 with automatic compression and transformation.

Question 44:

You need to orchestrate multiple ETL jobs, including conditional logic, retries, and parallel execution, across multiple AWS services. 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. You can define complex workflows with sequential, parallel, or conditional execution. Step Functions supports retries, error handling, and branching, making it ideal for managing multiple ETL jobs across Glue, Lambda, and EMR. It provides visual workflow monitoring, reducing operational complexity.

Option B, Glue, performs ETL tasks, but its workflow capabilities are limited. While Glue Workflows allow job chaining, complex conditional logic or parallel execution is better handled by Step Functions.

Option C, EMR, is a distributed processing platform but does not natively provide orchestration or retries. Workflow management would require external tools or scripts.

Option D, Data Pipeline, is a legacy orchestration tool. It is not fully serverless, requires management, and lacks modern features such as visual workflow design, parallel execution, and integration with serverless services.

Thus, Step Functions provides robust, serverless orchestration for ETL workflows with retries, parallelism, and conditional logic, making it the modern best practice.

Question 45:

You want to query both structured and semi-structured data in S3 without loading it into Redshift. Which service is most appropriate?

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

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). Athena integrates with the Glue Data Catalog for metadata management and allows ad-hoc queries without provisioning servers. Partitioning and columnar storage reduce query costs and improve performance.

Amazon Athena is a serverless, interactive query service that allows users to analyze data directly in Amazon S3 using standard SQL. Unlike traditional databases or data warehouses, Athena does not require provisioning or managing servers, making it a fully managed, cost-effective solution for ad hoc querying and analytics. Its serverless nature means that users can run queries on massive datasets stored in S3 without the overhead of infrastructure management, paying only for the amount of data scanned. This makes Athena particularly well-suited for scenarios involving unstructured or semi-structured data, such as JSON, CSV, Parquet, ORC, or Avro files.

One of the major advantages of Athena is its tight integration with the AWS ecosystem. Athena can query data stored in S3 directly, and it integrates seamlessly with AWS Glue Data Catalog, which provides centralized metadata management. This enables users to define and maintain schemas, track data lineage, and apply consistent governance policies across datasets. Athena also supports partitioning and compression, which optimize query performance and reduce costs by scanning only the relevant portions of data. Additionally, Athena works well with business intelligence tools such as Amazon QuickSight, allowing organizations to generate visualizations and dashboards from S3 data without needing a traditional data warehouse.

Athena is ideal for ad hoc, on-demand queries and analytics. For instance, it can be used to analyze web server logs, IoT telemetry, application events, or any large dataset stored in S3. Since Athena is serverless, there is no need to manage cluster scaling, provisioning, or maintenance, which significantly reduces operational complexity compared to other options. Users can simply write SQL queries and get results in seconds or minutes, depending on data size and query complexity.

Comparing Athena to the other options highlights why it is the most suitable choice. Amazon RDS (option B) is a managed relational database service designed for transactional workloads. While RDS supports structured data and SQL queries, it requires database schema design, provisioning, and ongoing management. It is optimized for online transaction processing (OLTP) rather than large-scale, ad hoc analytics on semi-structured or unstructured datasets. Running analytics directly in RDS on massive datasets can be expensive and inefficient, as it is not designed for serverless, on-demand querying of files in S3.

Amazon Redshift (option C) is a fully managed data warehouse optimized for complex analytical queries on structured data. Redshift provides high performance for large-scale analytics but requires loading and transforming data into its columnar storage format. This introduces additional steps and infrastructure overhead compared to Athena, which can query raw data in S3 directly. Redshift is more suitable for consistent, repeatable, high-performance analytics workloads rather than ad hoc querying on heterogeneous datasets.

Amazon DynamoDB (option D) is a fully managed NoSQL database designed for low-latency key-value or document-based workloads. DynamoDB excels at high-throughput transactional workloads but does not support SQL-style ad hoc queries or large-scale analytics directly. While it is highly performant for operational data, it is not a suitable solution for analyzing large volumes of log files, semi-structured datasets, or performing exploratory analytics.

In summary, Amazon Athena provides a serverless, cost-effective, and highly flexible solution for interactive querying of data stored in S3. Its ability to handle structured, semi-structured, and unstructured data without infrastructure management, combined with integration with AWS Glue Data Catalog and analytics tools, makes it ideal for ad hoc analytics and reporting. Compared to RDS, Redshift, and DynamoDB, Athena stands out for on-demand querying, zero server management, and seamless compatibility with data lakes, making it the optimal choice for querying S3-based datasets efficiently and effectively.

Option B, RDS, is a transactional database for OLTP workloads. It cannot query raw S3 data directly, requiring ingestion into relational tables, increasing latency and operational effort.

Option C, Redshift, requires loading data into the warehouse. Redshift Spectrum can query S3, but Athena is simpler, fully serverless, and cost-efficient for ad-hoc exploration.

Option D, DynamoDB, is a NoSQL database optimized for key-value or document workloads. It does not support SQL-based ad-hoc queries or analytics on large semi-structured datasets.

Thus, Athena is the serverless, scalable, and cost-effective solution for querying S3 data without moving it, making it ideal for modern data lake analytics.

Question 46:

You want to stream IoT sensor data to AWS, perform real-time transformations, and store it for low-latency querying. Which architecture is most suitable?

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

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

Explanation

Option A, Kinesis Data Streams (KDS) + Lambda + OpenSearch, provides a serverless, real-time streaming analytics pipeline. KDS handles high-velocity ingestion from thousands of IoT devices with durable, ordered delivery. Lambda performs transformations, filtering, or enrichment in real time, without the need to manage servers. OpenSearch allows low-latency search, aggregation, and visualization using Kibana dashboards. This architecture supports instant insights, scalability, and minimal operational management.

Option B, SQS + RDS, is unsuitable for real-time analytics. SQS is a queueing service and does not support stream processing. RDS is optimized for transactional workloads and is not designed for high-volume, low-latency ingestion and querying.

Amazon Kinesis Data Streams provides a highly scalable and durable platform to ingest and process real-time streaming data from hundreds of thousands of sources, such as application logs, IoT devices, or clickstream events. Data streams are divided into shards that allow parallel processing, enabling high throughput and low latency ingestion. Kinesis Data Streams ensures that data is reliably captured and retained for a configurable period, allowing multiple consumers to process the same stream independently.

AWS Lambda acts as the real-time processing layer. Lambda can automatically trigger functions in response to new records in Kinesis Data Streams. This serverless approach eliminates the need to provision or manage compute infrastructure. Using Lambda, data can be transformed, filtered, enriched, or aggregated in near real time before being delivered to a downstream service. For example, JSON log entries can be parsed, sensitive fields masked, or metrics extracted on the fly.

Amazon OpenSearch Service (formerly Elasticsearch Service) serves as the search and analytics layer. Lambda can push processed data into OpenSearch, where it is indexed and made available for low-latency searches, aggregations, and visualizations. OpenSearch supports dashboards through Kibana or OpenSearch Dashboards, allowing organizations to gain actionable insights, monitor operational metrics, detect anomalies, and visualize trends in real time. This architecture ensures that raw streaming data becomes queryable analytics data almost immediately.

Comparing the other options: B) Amazon SQS + Amazon RDS is suitable for decoupling application components and storing transactional data but does not support near real-time analytics or indexing for search. C) Amazon SNS + DynamoDB is a pub/sub system combined with a NoSQL database, which is excellent for notifications and key-value storage but lacks the ability to efficiently process and search high-throughput streaming data. D) Amazon Redshift + Kinesis Data Firehose can handle large-scale batch ingestion and analytics, but Redshift is optimized for structured batch queries rather than real-time, near-instant search and analytics.

In summary, Amazon Kinesis Data Streams + AWS Lambda + Amazon OpenSearch Service provides a fully managed, scalable, and serverless architecture for real-time data ingestion, processing, and analytics. It supports high-throughput data streams, low-latency transformations, and powerful search and visualization capabilities, making it the best choice for real-time monitoring, logging, and analytics pipelines.

Option C, SNS + DynamoDB, allows event-driven ingestion but does not support real-time analytics and time-series queries efficiently. DynamoDB is optimized for key-value operations, not for large-scale streaming analytics.

Option D, Redshift + Kinesis Data Firehose, is better suited for batch or micro-batch processing. Firehose buffers data and delivers it to Redshift periodically, introducing latency, which is not ideal for low-latency IoT analytics.

Thus, KDS + Lambda + OpenSearch is the best practice architecture for real-time IoT data ingestion, transformation, and queryable storage with minimal operational overhead.

Question 47:

You need to automate nightly ETL jobs that extract S3 data, transform it, and load it into Redshift with retries. Which service is most suitable?

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

Answer: A) AWS Glue

Explanation

Option A, AWS Glue, is a serverless ETL service that automates data extraction, transformation, and loading. Glue crawlers can scan S3 data, infer schemas, and populate the Glue Data Catalog. Glue ETL jobs written in Python or Scala can perform complex transformations and load data into Redshift on a schedule. Glue supports job retries, dependency management, and monitoring, making it ideal for automated ETL pipelines without infrastructure management.

Option B, EMR, is a distributed cluster platform for large-scale processing. While EMR can perform ETL, it requires cluster management, scaling, and configuration, increasing operational complexity for nightly jobs.

Option C, Step Functions, orchestrates workflows but does not perform ETL directly. It can coordinate Glue or Lambda jobs but requires Glue for the actual transformations.

Option D, Athena, allows ad-hoc queries on S3 but cannot schedule ETL jobs or load data into Redshift. It is not an ETL engine.

Thus, AWS Glue provides a fully managed, serverless ETL solution with retries, scheduling, and Redshift integration, minimizing operational effort and aligning with AWS best practices.

Question 48:

You want to query large structured and semi-structured S3 datasets without moving data or provisioning infrastructure. Which service is most appropriate?

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

Answer: A) Amazon Athena

Explanation

Option A, Athena, is a serverless SQL query service that directly queries S3 objects. Athena supports structured formats (CSV, Parquet) and semi-structured formats (JSON, ORC, Avro). Integration with the Glue Data Catalog enables metadata management, and partitioning or columnar formats reduce costs and improve performance. Athena charges only for data scanned, making it cost-efficient for ad-hoc queries without infrastructure management.

Option B, Redshift, requires loading data into tables and provisioning clusters. While Redshift Spectrum allows querying S3, Athena provides a fully serverless, simpler approach for ad-hoc queries.

Option C, EMR, allows querying S3 using Spark or Hive, but cluster management and startup latency make it unsuitable for ad-hoc, serverless queries.

Option D, Glue, is primarily for ETL and data cataloging. While it can prepare data, it does not provide ad-hoc, serverless SQL query capabilities like Athena.

Thus, Athena provides the simplest, scalable, and cost-effective solution for querying S3 datasets without moving data or provisioning servers.

Question 49:

You want to orchestrate multiple ETL jobs with conditional logic, 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, provides serverless workflow orchestration. You can define workflows with sequential, parallel, or conditional execution, and Step Functions supports retries, error handling, and branching. It integrates with Glue, Lambda, and EMR, allowing orchestration of multiple ETL tasks without manual intervention. Visual workflow monitoring and state tracking reduce operational complexity.

Option B, Glue, performs ETL but has limited workflow orchestration capabilities. Glue Workflows allow chaining but lack advanced conditional logic or parallel execution compared to Step Functions.

Option C, EMR, is a distributed processing engine. While it can execute jobs, EMR does not handle orchestration, retries, or workflow visualization.

Option D, Data Pipeline, is a legacy orchestration tool that is not fully serverless and lacks modern workflow features. Step Functions is the recommended approach for serverless orchestration with retries and parallelism.

Thus, Step Functions is the best choice for reliable, serverless orchestration of complex ETL workflows.

Question 50:

You need a centralized logging system across multiple AWS accounts, with near real-time search and analytics. Which architecture is most suitable?

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

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

Explanation

Option A provides a centralized, near real-time logging solution. CloudWatch Logs collects logs from multiple accounts. Kinesis Firehose ingests logs, buffers them, and delivers them to S3 for durable storage and OpenSearch for search, aggregation, and visualization. Kibana dashboards allow near real-time querying. This architecture supports high-throughput ingestion, low-latency analytics, and serverless scaling.

Option B, CloudTrail → S3 + Athena, is suitable for audit and historical queries. Athena queries are batch-oriented and do not support real-time log analytics or dashboards.

Option C, SQS → RDS, is inefficient. SQS is a message queue, and RDS is not designed for high-volume log ingestion or near real-time queries.

Option D, SNS → Redshift, supports messaging and batch analytics. Redshift is optimized for structured data but not real-time log search.

Thus, CloudWatch + Firehose + S3 + OpenSearch provides the best practice architecture for centralized, searchable, near real-time logging across multiple AWS accounts.

Question 51:

You need to ingest and process high-volume clickstream data in real-time, perform aggregations, and store results for dashboards with minimal infrastructure management. Which architecture is most suitable?

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 a serverless, fully managed pipeline for real-time analytics. KDS ingests high-velocity clickstream data, scaling horizontally with the number of shards. KDA allows real-time processing, aggregations, filtering, and transformations using SQL or Apache Flink. Processed data is sent to OpenSearch, enabling near real-time dashboards with Kibana. This architecture is serverless, fault-tolerant, and low-latency, ideal for operational insights and monitoring.

Option B, SQS + RDS, is not suitable for real-time analytics. SQS provides message queuing, but it lacks stream processing capabilities, and RDS is optimized for transactional workloads, not high-throughput real-time aggregation or dashboarding.

Option C, SNS + Redshift, supports event-driven ingestion, but Redshift is designed for batch or micro-batch analytics, introducing latency. SNS delivers messages but does not process streaming data.

Option D, EMR + S3, is suitable for large-scale batch processing, but cluster provisioning and job execution introduce latency, making it less suitable for sub-second or real-time dashboarding.

Thus, KDS + KDA + OpenSearch provides the best architecture for real-time clickstream analytics, low operational overhead, and immediate insights.

Question 52:

You want to store IoT time-series data and query trends over time efficiently with minimal management. 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 purpose-built, serverless time-series database. It automatically manages data lifecycle, tiered storage (hot and cold), and compression. It supports time-series queries, aggregations, and functions such as interpolation and smoothing, which are essential for IoT trend analysis. Timestream can ingest millions of events per second and scale automatically, eliminating operational overhead.

Option B, DynamoDB, provides high-throughput key-value storage but lacks native time-series query capabilities. Querying trends efficiently would require additional table design patterns or batch processing, adding complexity.

Option C, Redshift, is designed for structured analytics and data warehousing. While it can store historical data, continuous ingestion at IoT scale requires micro-batch ETL and cluster management. It is less efficient for real-time or trend analysis of time-series data.

Option D, RDS, is suitable for transactional workloads but cannot efficiently handle large-scale time-series ingestion or analytics. Complex queries on historical IoT data may lead to performance bottlenecks.

Thus, Timestream provides a serverless, scalable, and cost-efficient solution for storing and analyzing IoT time-series data, supporting both real-time and historical analytics.

Question 53:

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

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

Answer: A) AWS Glue

Explanation

Option A, AWS Glue, is a serverless ETL service designed to automate extraction, transformation, and loading of data. Glue crawlers can automatically discover schema in S3 and maintain the Glue Data Catalog. ETL jobs written in Python or Scala can transform data and load it into Redshift. Glue supports scheduling, retries, job monitoring, and dependency management, making it ideal for automated workflows.

Option B, EMR, is a distributed cluster for big data processing. While it can perform ETL, it requires manual cluster provisioning, scaling, and management, increasing operational overhead for automated workflows.

Option C, Step Functions, orchestrates workflows but does not perform ETL directly. It can trigger Glue jobs or Lambda functions, but Glue or another service is required for actual data transformations.

Option D, Athena, is designed for ad-hoc querying of S3 data. It cannot handle automated ETL, scheduling, or Redshift loading natively.

Thus, AWS Glue is the best practice solution for automated, serverless ETL pipelines with scheduling, retries, and Redshift integration.

Question 54:

You want to query large datasets in S3 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 allows you to query S3 objects directly. It supports structured and semi-structured formats (CSV, JSON, Parquet, ORC, Avro). Athena integrates with the Glue Data Catalog for metadata management. Using partitioned and columnar datasets reduces query cost and improves performance. Athena’s pay-per-query model means users pay only for the data scanned.

Option B, Redshift, requires loading data into tables and provisioning clusters. While it supports high-performance queries, it introduces operational overhead and is less cost-efficient for ad-hoc exploration on S3.

Option C, EMR, allows SQL querying using Spark or Hive but requires cluster provisioning, configuration, and management. Startup latency and maintenance make it unsuitable for ad-hoc, serverless queries.

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

Thus, Athena provides a serverless, scalable, and cost-effective solution for querying large S3 datasets without moving data or provisioning infrastructure.

Question 55:

You want to orchestrate multiple ETL jobs with conditional logic, retries, and parallel execution. Which 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 service. It allows workflows with sequential, parallel, or conditional execution. Step Functions supports retries, error handling, and branching, integrating seamlessly with Glue, Lambda, and EMR. The visual workflow interface improves monitoring, observability, and reliability of complex ETL pipelines.

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

Option C, EMR, can execute big data jobs but does not provide orchestration, retries, or conditional logic. Custom scripting is required for workflow management, increasing complexity.

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

Thus, Step Functions is the best practice solution for orchestrating complex ETL workflows with retries, conditional logic, and parallel execution.

Question 56:

You want to ingest streaming IoT data, transform it in real-time, and make it searchable and queryable immediately. Which architecture is most suitable?

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

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

Explanation

Option A, Kinesis Data Streams (KDS) + Lambda + OpenSearch, provides a serverless, scalable real-time ingestion and analytics pipeline. KDS ingests high-volume streaming data reliably and in order. Lambda performs transformations, filtering, and enrichment without provisioning infrastructure. OpenSearch stores the transformed data, enabling low-latency querying and dashboards via Kibana. This architecture supports instant insights, scales automatically with traffic, and requires minimal operational management.

Option B, SQS + RDS, is unsuitable for streaming analytics. SQS is a message queue, not a streaming platform, and RDS is optimized for transactional workloads rather than real-time ingestion or analytics.

Option C, SNS + DynamoDB, supports event-driven ingestion but does not provide real-time query or aggregation capabilities. DynamoDB is suitable for key-value operations, not large-scale time-series or streaming analytics.

Option D, Redshift + Kinesis Data Firehose, is better suited for batch or micro-batch processing. Firehose buffers data before delivery, introducing latency, making it less suitable for immediate querying.

Thus, KDS + Lambda + OpenSearch is the best practice architecture for real-time IoT ingestion, transformation, and search with minimal operational overhead.

Question 57:

You need to catalog and discover new datasets in an S3-based data lake automatically for query access via Athena and Redshift Spectrum. Which service should you use?

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

Answer: A) AWS Glue

Explanation

Option A, AWS Glue, provides a serverless Data Catalog that automatically discovers schema in S3 using crawlers. Glue stores metadata in the Glue Data Catalog, enabling Athena and Redshift Spectrum to query datasets without manual schema management. Glue supports structured, semi-structured, and nested formats (CSV, JSON, Parquet, ORC) and allows transformations with Python or Scala.

Option B, EMR, can process large datasets but does not provide automated cataloging. Metadata management requires additional integration with Glue or manual effort.

Option C, RDS, is a transactional relational database, not suitable for data lake cataloging or query discovery.

Option D, Redshift, is a data warehouse. While Redshift Spectrum can query S3, it cannot automatically discover new datasets without Glue. Manual metadata management is required.

Thus, AWS Glue is the best solution for automatic cataloging, discovery, and integration with serverless query services.

Question 58:

You want to ingest streaming logs from multiple AWS accounts and provide near real-time search and analytics. Which architecture is most appropriate?

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

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

Explanation

Option A, CloudWatch Logs → Kinesis Firehose → S3 + OpenSearch, is a centralized, scalable, and near real-time logging solution. CloudWatch Logs collects logs from multiple accounts. Kinesis Firehose ingests logs, buffers, and delivers them to S3 for durable storage and OpenSearch for search and aggregation. Kibana dashboards provide low-latency analytics, enabling operational monitoring and troubleshooting. This architecture is serverless, fault-tolerant, and high-throughput, making it ideal for multi-account log aggregation.

Option B, CloudTrail → S3 + Athena, is batch-oriented, suitable for audit or historical analysis, but does not provide real-time analytics or dashboarding.

Option C, SQS → RDS, is inefficient. SQS queues messages but lacks stream processing, and RDS cannot scale for high-volume, low-latency analytics.

Option D, SNS → Redshift, supports event-driven workflows and batch analytics. Redshift is not optimized for real-time log search, requiring additional ETL and batch loading.

Thus, CloudWatch + Firehose + S3 + OpenSearch provides a best practice solution for centralized, near real-time logging and analytics.

Question 59:

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

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 directly. It supports structured formats (CSV, Parquet) and semi-structured formats (JSON, ORC, Avro). Integration with Glue Data Catalog allows automatic schema management. Athena supports partitioning and columnar formats to reduce query costs and improve performance. Being serverless and pay-per-query, users only pay for data scanned.

Option B, Redshift, requires loading data into tables and provisioning clusters, introducing operational overhead. Redshift Spectrum allows querying S3, but Athena is simpler and fully serverless.

Option C, EMR, allows querying via Spark or Hive but requires cluster provisioning and management. Startup latency makes it less suitable for ad-hoc, serverless SQL queries.

Option D, Glue, is primarily for ETL and cataloging. While Glue can prepare data, it does not provide direct, serverless ad-hoc SQL queries.

Thus, Athena provides the simplest, most scalable, and cost-effective solution for querying S3 datasets without infrastructure management.

Question 60:

You want to orchestrate multiple ETL jobs with conditional logic, 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, Step Functions, is a serverless orchestration service. It allows workflows with sequential, parallel, or conditional execution, supports retries and error handling, and integrates with Glue, Lambda, EMR, and Redshift. Step Functions provides visual monitoring, state tracking, and centralized management for complex workflows, reducing operational overhead and improving reliability.

Option B, Glue, performs ETL but has limited orchestration capabilities. Glue Workflows allow simple job chaining but lack advanced conditional logic or parallel execution.

Option C, EMR, can execute distributed jobs but does not provide orchestration, retries, or visual workflow management. Workflow orchestration must be handled externally.

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

Thus, AWS Step Functions is the best practice choice for orchestrating complex ETL workflows with conditional logic, retries, and parallel execution.