Amazon AWS Certified Data Engineer – Associate DEA-C01 Exam Dumps and Practice Test Questions Set 2 Q21-40

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Question 21:

You are designing a real-time clickstream analytics pipeline. You want to capture streaming data, enrich it, and store the results for low-latency querying. Which AWS service combination is most suitable?

A) Amazon Kinesis Data Streams and Amazon Redshift
B) Amazon Kinesis Data Streams and Amazon Elasticsearch Service
C) Amazon SQS and Amazon Athena
D) Amazon SNS and Amazon RDS

Answer: B) Amazon Kinesis Data Streams and Amazon Elasticsearch Service

Explanation 

Option B, Kinesis Data Streams (KDS) with Amazon Elasticsearch Service (Amazon OpenSearch Service), is ideal for real-time analytics and low-latency querying. KDS provides durable, ordered ingestion of streaming data from thousands of producers. It supports horizontal scaling via shards, allowing high throughput. Data can be processed in near real-time using Kinesis Data Analytics or Lambda to enrich, filter, or transform the data before indexing it into Elasticsearch/OpenSearch.

Elasticsearch/OpenSearch is optimized for search, analytics, and aggregations, making it ideal for dashboarding, filtering, and near real-time querying. Kibana, integrated with OpenSearch, provides visualization capabilities for streaming data. Together, this combination supports sub-second latency analytics on high-volume streaming datasets.

Option A, Kinesis Data Streams to Redshift, is less suitable for real-time analytics. While Redshift can store structured data and provide analytical queries, it is optimized for batch ingestion, and continuous ingestion from KDS requires a micro-batch ETL step, introducing latency.

Amazon Kinesis Data Streams and Amazon Elasticsearch Service form a powerful combination for real-time data ingestion, processing, and analytics, which is why option B is the correct choice among the four options provided. Amazon Kinesis Data Streams is a fully managed, scalable service designed to handle large streams of real-time data. It allows organizations to continuously capture gigabytes of data per second from hundreds of thousands of sources, such as website clickstreams, financial transactions, social media feeds, or IoT devices. The service is highly durable and fault-tolerant, storing multiple copies of data across availability zones to ensure reliability. It also supports real-time processing through integration with AWS Lambda, Amazon Kinesis Data Analytics, and custom applications, allowing immediate analysis or transformation of incoming data.

On the other hand, Amazon Elasticsearch Service, now known as Amazon OpenSearch Service, is a managed service that makes it easy to deploy, secure, and operate Elasticsearch clusters at scale. Elasticsearch is widely used for search, log analytics, and real-time application monitoring. When combined with Kinesis Data Streams, Elasticsearch can index incoming data almost immediately, enabling near real-time search and visualization through tools like Kibana. This combination is ideal for scenarios such as monitoring application logs, analyzing clickstream data, tracking social media sentiment, or detecting anomalies in operational data. By streaming data directly from Kinesis into Elasticsearch, businesses can achieve minimal latency between data capture and actionable insights.

Looking at the other options clarifies why they are less suitable. Option A, Amazon Kinesis Data Streams and Amazon Redshift, pairs a real-time streaming service with a data warehousing solution. While this is effective for batch analytics and complex queries, Redshift is not optimized for near-real-time search or log analysis. It is designed for structured, relational datasets and large-scale analytical queries rather than rapid, unstructured search operations. Option C, Amazon SQS and Amazon Athena, pairs a message queuing service with a serverless query engine. SQS is excellent for decoupling application components and ensuring reliable message delivery, while Athena allows ad hoc SQL queries on S3 data. However, this combination does not provide a seamless pipeline for ingesting, indexing, and analyzing streaming data in real time. Option D, Amazon SNS and Amazon RDS, combines a pub/sub messaging service with a relational database. While suitable for notifications and transactional data storage, it cannot handle high-throughput streaming data or provide the rapid search and analytics capabilities that Elasticsearch offers.

In summary, the synergy between Amazon Kinesis Data Streams and Amazon Elasticsearch Service addresses the challenges of real-time data ingestion, indexing, and analysis. Kinesis ensures reliable, high-throughput streaming of data from diverse sources, while Elasticsearch provides scalable, low-latency indexing and search capabilities. This makes option B the most appropriate choice for applications requiring immediate insights, continuous monitoring, and real-time analytics. By leveraging this combination, organizations can transform raw streaming data into actionable intelligence almost instantaneously, supporting better decision-making, operational efficiency, and enhanced user experiences.

Option C, SQS to Athena, is unsuitable because SQS is a message queue, not a stream processing tool, and Athena is batch-oriented, querying data in S3. This combination cannot support near-real-time analytics.

Option D, SNS to RDS, is also inadequate. SNS is a pub/sub messaging service, and RDS is an OLTP database. While it can store transactional records, this setup does not support low-latency analytics or large-scale streaming ingestion.

Thus, KDS + Elasticsearch/OpenSearch is the best combination for a scalable, real-time, low-latency analytics pipeline. Firehose, Redshift, and SQS-based solutions are better suited for batch or micro-batch processing but cannot meet real-time dashboarding requirements.

Question 22:

You want to store raw IoT telemetry data for historical analysis, with the ability to query large datasets using SQL. Which AWS services are best suited?

A) Amazon S3 and Amazon Athena
B) Amazon DynamoDB and AWS Lambda
C) Amazon Redshift and Amazon Kinesis Data Firehose
D) Amazon RDS and Amazon SNS

Answer: A) Amazon S3 and Amazon Athena

Explanation 

Option A, S3 + Athena, is ideal for storing large amounts of raw IoT telemetry data. S3 provides highly durable, virtually unlimited object storage, while Athena allows serverless, ad-hoc SQL queries directly on S3 objects. Data can be stored in formats like JSON, Parquet, or ORC, enabling partitioning and columnar storage to optimize query performance and reduce costs.

Option B, DynamoDB + Lambda, is more suited for real-time processing or key-value lookups rather than historical analytics. While DynamoDB can ingest high-velocity data and Lambda can process events, DynamoDB is not cost-effective or optimized for ad-hoc SQL queries on massive historical datasets.

Option C, Redshift + Kinesis Data Firehose, is suitable for analytics, but Redshift requires loading structured data and provisioning compute clusters. While Kinesis can ingest streaming IoT data, Redshift is not cost-efficient for storing raw, semi-structured historical telemetry, making S3 more appropriate as a data lake.

Option D, RDS + SNS, is inappropriate. RDS is designed for transactional workloads, not large-scale historical storage, and SNS is a pub/sub service, which does not store data. This combination cannot efficiently support ad-hoc queries on large IoT datasets.

Thus, S3 + Athena provides a serverless, scalable, and cost-effective architecture for storing and querying raw IoT data, making it ideal for analytics, dashboards, and ML workflows.

Question 23:

You need to enforce MFA for external users accessing corporate applications, but allow seamless access for users on corporate devices. Which solution is most appropriate?

A) Azure Conditional Access policy requiring MFA
B) Security Defaults
C) Pass-through Authentication
D) Azure AD B2B collaboration

Answer: A) Azure Conditional Access policy requiring MFA

Explanation 

Option A, Conditional Access (CA) policies, allow adaptive authentication based on real-time conditions such as network location, device compliance, and risk assessment. Administrators can enforce MFA for external access while allowing seamless sign-ins from corporate-managed devices. CA supports granular targeting, including groups, users, apps, and sign-in risk levels. Integration with Identity Protection allows MFA enforcement based on suspicious activity, impossible travel, or compromised credentials.

The correct answer is A) Azure Conditional Access policy requiring MFA. Azure Conditional Access is a critical security feature in Microsoft Entra ID (formerly Azure AD) that allows organizations to enforce access controls based on specific conditions. Among these controls, requiring Multi-Factor Authentication (MFA) is one of the most effective ways to protect accounts from unauthorized access, particularly in scenarios involving sensitive data or privileged access. By configuring a Conditional Access policy that mandates MFA, administrators can ensure that users provide an additional authentication factor—such as a phone notification, SMS code, or authenticator app verification—before gaining access to applications or resources. This significantly reduces the risk of account compromise, even if credentials are stolen, because possession of the password alone is not sufficient.

Security Defaults (option B) is a baseline security feature provided by Microsoft, which enforces basic protections such as requiring all users to register for MFA. While Security Defaults provide a foundational level of security, they do not offer the granularity of Conditional Access policies. Conditional Access allows administrators to define detailed rules based on user groups, device compliance, application sensitivity, sign-in risk, or network location. This flexibility ensures that MFA can be enforced in high-risk situations without unnecessarily burdening users under low-risk conditions.

Pass-through Authentication (option C) is an authentication method that allows on-premises Active Directory passwords to be validated directly without storing credentials in the cloud. While this supports seamless single sign-on (SSO) and password management, it does not inherently enforce MFA or adaptive access controls. Therefore, pass-through authentication alone does not provide the protection required to mitigate compromised credentials in high-risk scenarios.

Azure AD B2B collaboration (option D) is a feature that enables organizations to securely share applications and resources with external partners while maintaining control over access. While it is valuable for external collaboration, it is not specifically designed to enforce MFA or conditional access policies for internal user protection.

In summary, implementing an Azure Conditional Access policy requiring MFA provides a targeted, flexible, and robust approach to securing access. Unlike Security Defaults, Pass-through Authentication, or B2B collaboration, Conditional Access policies allow administrators to apply MFA selectively based on risk signals, user roles, or device compliance, offering a stronger layer of security while minimizing user disruption. This makes option A the most effective solution for ensuring that accounts are protected by additional authentication factors and that organizational resources remain secure.

Option B, Security Defaults, enforces MFA globally for all users, without distinction between internal and external access. This approach may reduce user friction but cannot differentiate based on network or device, making it less flexible than CA.

Option C, Pass-through Authentication, validates credentials but does not provide adaptive or conditional MFA. It cannot enforce location- or device-based authentication policies.

Option D, Azure AD B2B collaboration, is intended for guest user management and external sharing. While it allows federation with external organizations, it does not provide conditional MFA enforcement for internal users.

Thus, a Conditional Access policy requiring MFA for external access ensures security by reducing exposure to external threats while maintaining seamless productivity for trusted corporate devices. Other solutions either lack granularity or do not enforce conditional MFA.

Question 24:

You are designing a centralized logging solution for multiple AWS accounts. Logs must be aggregated, searchable, and queryable in near real-time. Which architecture is most suitable?

A) CloudWatch Logs to Kinesis Firehose to S3 and Elasticsearch
B) CloudTrail to S3 and Athena
C) SQS to RDS
D) SNS to Redshift

Answer: A) CloudWatch Logs to Kinesis Firehose to S3 and Elasticsearch

Explanation 

Option A provides a scalable, real-time centralized logging solution. CloudWatch Logs collects logs from multiple accounts. Kinesis Data Firehose ingests these logs and delivers them to S3 for durable storage and Elasticsearch/OpenSearch for search and visualization. Kibana dashboards allow near real-time querying, filtering, and aggregation. This architecture supports high throughput, low latency, and cost-effective long-term storage.

Option B, CloudTrail to S3 and Athena, is suitable for audit logs and historical querying, but Athena is batch-oriented and not designed for near real-time search or dashboards.

Option C, SQS to RDS, is not scalable for centralized logging. SQS can ingest messages, but RDS cannot handle large-scale log ingestion or ad-hoc searches efficiently.

Option D, SNS to Redshift, is also inappropriate. SNS is a messaging service, and Redshift is a warehouse optimized for structured analytics, not high-throughput log ingestion or near real-time querying.

Amazon CloudWatch, Amazon Kinesis Data Firehose, Amazon S3, and Amazon Elasticsearch Service (OpenSearch) together form a best-practice logging architecture that is both centralized and scalable, enabling real-time querying and long-term storage. CloudWatch serves as the primary monitoring and logging service, capturing logs and metrics from AWS resources, applications, and on-premises systems. It ensures that operational data is collected continuously and reliably, providing the foundation for observability and proactive monitoring.

Kinesis Data Firehose acts as the ingestion and delivery mechanism, streaming the collected logs from CloudWatch to the target destinations. It abstracts the complexity of managing data pipelines, automatically scaling to handle high throughput and providing buffering, batching, and retry mechanisms to ensure reliable delivery. Firehose’s seamless integration with S3 and Elasticsearch/OpenSearch enables organizations to balance durability and real-time analysis effectively.

Amazon S3 serves as the durable storage layer, archiving logs for long-term retention and compliance purposes. By storing data in S3, organizations gain cost-effective scalability and can maintain an immutable history of log data that can be queried later using services such as Amazon Athena or Redshift Spectrum. This separation of storage and analytics ensures that real-time processing does not compromise durability or increase costs unnecessarily.

Elasticsearch/OpenSearch provides the analytics and search layer, enabling near real-time querying, visualization, and analysis of log data. Logs streamed through Firehose can be indexed in OpenSearch, making it possible to search, filter, and create dashboards for operational insights, anomaly detection, and performance monitoring. Kibana or OpenSearch Dashboards further enhance visualization, helping teams respond quickly to operational issues and gain actionable intelligence from their log data.

In combination, CloudWatch + Firehose + S3 + Elasticsearch/OpenSearch delivers a robust logging architecture that is both resilient and performant. CloudWatch ensures centralized log collection, Firehose manages scalable streaming and delivery, S3 provides durable storage for historical analysis, and Elasticsearch/OpenSearch enables real-time analytics and visualization. This architecture supports enterprise requirements for observability, compliance, and operational efficiency, making it a best-practice solution for modern cloud-based environments.

Question 25:

Your organization wants to perform complex machine learning analytics on historical and streaming data stored in S3. Which combination of services is most appropriate?

A) Amazon S3, AWS Glue, Amazon Athena, Amazon SageMaker
B) Amazon Redshift, Kinesis Data Streams, EMR
C) Amazon RDS, SQS, and DynamoDB
D) Amazon SNS, Redshift, and EMR

Answer: A) Amazon S3, AWS Glue, Amazon Athena, Amazon SageMaker

Explanation 

Option A provides a modern, serverless, and scalable architecture for ML analytics. S3 acts as the central data lake for raw and processed data. AWS Glue catalogs data, infers schema, and performs ETL transformations. Athena allows ad-hoc SQL queries on structured or semi-structured datasets, enabling exploration and feature extraction. SageMaker uses this curated data for machine learning model training, evaluation, and deployment, supporting both batch and streaming analytics.

Option B, Redshift + Kinesis + EMR, can handle analytics but is less serverless, requires cluster management, and is more operationally complex. Real-time and historical data would require additional ETL steps before ML processing.

Option C, RDS + SQS + DynamoDB, is designed for transactional or key-value workloads, not large-scale analytics or ML. It cannot efficiently store and process large historical datasets for feature extraction or modeling.

Option D, SNS + Redshift + EMR, is oriented toward messaging and batch processing but lacks integrated ML capabilities. Orchestrating ML workflows would require significant additional infrastructure.

Thus, S3 + Glue + Athena + SageMaker provides a fully managed, scalable, and flexible solution for data lake-based analytics, ML model development, and integration with streaming and historical datasets. It is cost-effective, serverless, and aligned with AWS best practices for advanced analytics.

Question 26:

Your organization wants to process streaming log data from multiple applications, perform transformations, and store it in a queryable format in S3 with minimal operational overhead. Which solution is most appropriate?

A) Amazon Kinesis Data Firehose with AWS Lambda to S3
B) Amazon Kinesis Data Streams directly to Redshift
C) Amazon SQS to Amazon RDS
D) Amazon SNS to DynamoDB

Answer: A) Amazon Kinesis Data Firehose with AWS Lambda to S3

Explanation 

Option A, Kinesis Data Firehose + Lambda + S3, is a serverless streaming ingestion and transformation pipeline. Firehose ingests high-volume data streams and can buffer, compress, and batch records before delivery to S3. Lambda enables real-time transformations, such as parsing JSON, converting formats, or filtering records. S3 serves as durable storage, queryable with Athena or Redshift Spectrum. This combination provides minimal operational overhead, automatic scaling, and reliability.

Option B, Kinesis Data Streams to Redshift, supports ingestion but requires ETL or batch loading into Redshift. Redshift is optimized for structured data, not raw streams, and continuously ingesting would require additional management or micro-batch processing, increasing complexity.

Option C, SQS to RDS, is unsuitable for streaming. SQS handles message queuing but lacks stream processing, and RDS is transactional, not optimized for high-throughput log analytics or long-term storage.

Option D, SNS to DynamoDB, allows event-driven ingestion but does not provide stream transformations, durable storage for queries, or serverless analytics capabilities. DynamoDB is better for key-value workloads rather than large-scale analytics.

Thus, Firehose + Lambda + S3 is the best solution for real-time ingestion, transformation, and queryable storage, minimizing operational management while supporting analytics.

Question 27:

You are tasked with designing a data lake for structured and semi-structured data, where analysts need ad-hoc queries without provisioning servers. Which architecture is most suitable?

A) Amazon S3, AWS Glue, Amazon Athena
B) Amazon RDS and Amazon Redshift
C) Amazon DynamoDB and Kinesis Data Streams
D) Amazon SNS, SQS, and RDS

Answer: A) Amazon S3, AWS Glue, Amazon Athena

Explanation 

Option A combines S3 as a data lake, Glue for ETL and cataloging, and Athena for serverless SQL queries. S3 provides unlimited, durable storage for structured and semi-structured data formats (JSON, Parquet, ORC). Glue discovers and catalogs datasets, maintaining metadata for analysts. Athena queries S3 directly without servers, enabling cost-efficient, ad-hoc analytics. Partitioning and columnar formats reduce query costs and improve performance.

Option B, RDS + Redshift, is more traditional. RDS is for transactional workloads; Redshift provides analytics but requires data loading, provisioning clusters, and managing storage. It is not serverless and less cost-efficient for ad-hoc querying of semi-structured or raw data.

Option C, DynamoDB + Kinesis Data Streams, is suitable for real-time ingestion but not for ad-hoc SQL queries on large historical datasets. DynamoDB lacks advanced analytical functions for joins, aggregations, and exploratory analysis.

Option D, SNS + SQS + RDS, focuses on messaging and transactional processing. While RDS can store structured data, it is not optimized for large-scale analytics or ad-hoc queries, and SNS/SQS do not provide data storage or analytics capabilities.

Thus, S3 + Glue + Athena provides a modern, serverless, and scalable data lake architecture, enabling analysts to perform ad-hoc queries on structured and semi-structured datasets without infrastructure management.

Question 28:

You are building a real-time IoT analytics system where sensors generate high-velocity data that must be stored and queried for immediate insights. Which architecture is best?

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

Answer: A) Amazon Kinesis Data Streams + Amazon Timestream

Explanation 

Option A, Kinesis Data Streams + Timestream, is ideal for real-time IoT analytics. KDS ingests high-velocity sensor data reliably. Timestream, a purpose-built time-series database, automatically tiers data, compresses it, and supports time-series queries, aggregations, and window functions. This combination allows instant insights, dashboards, and historical trend analysis.

Option B, SQS + RDS, is less suitable. SQS provides message queuing but cannot process high-throughput streams in real time. RDS is an OLTP database, not optimized for time-series analytics or high-frequency sensor data, making it unsuitable for streaming workloads.

Option C, SNS + DynamoDB, supports event-driven ingestion and key-value storage. While DynamoDB can scale for high-velocity writes, it lacks native time-series functions and analytics capabilities, requiring additional processing layers for querying trends.

Option D, Kinesis Data Firehose + Redshift, can store streaming data in Redshift for analytics but involves micro-batching and loading latency, making it less suitable for real-time insights. Redshift is designed for structured, batch analytics, not streaming IoT workloads.

Thus, KDS + Timestream provides a serverless, scalable, low-latency architecture for ingesting, storing, and analyzing IoT data in real time.

Question 29:

You need to automate ETL workflows that extract data from S3, transform it, and load it into Redshift on a scheduled basis with retries. Which AWS service is most appropriate?

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

Answer: A) AWS Glue

Explanation 

Option A, AWS Glue, is a serverless ETL service that can extract data from S3, perform transformations using Spark, and load results into Redshift. Glue supports job scheduling, retries, and monitoring, and integrates with the Glue Data Catalog for schema management. Serverless scaling ensures jobs run efficiently without manual infrastructure management.

Option B, EMR, provides distributed processing with Spark or Hadoop. While EMR can perform ETL, it requires cluster provisioning, scaling, and management, increasing operational overhead for scheduled, repeatable ETL workflows.

Option C, Step Functions, orchestrates workflows but does not perform ETL by itself. Step Functions can invoke Glue, Lambda, or EMR tasks, but it is an orchestration tool, not an ETL engine.

Option D, Athena, is a query engine for ad-hoc analytics on S3. While it can run SQL transformations, it is not a full ETL solution and lacks scheduling, retry handling, and Redshift integration for structured loads.

Thus, AWS Glue provides a fully managed, serverless ETL solution, supporting scheduled, repeatable pipelines with retries, transformation, and Redshift integration, making it the best choice for ETL automation.

Question 30:

You want to query both structured and semi-structured data in a centralized S3 data lake without loading data into Redshift. Which service should you use?

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

Answer: A) Amazon Athena

Explanation 

Option A, Amazon Athena, is a serverless query service that allows SQL queries on S3 objects directly. Athena supports structured formats (CSV, Parquet) and semi-structured formats (JSON, ORC, Avro). It integrates with the AWS Glue Data Catalog to maintain metadata, allowing queries across multiple datasets without moving or transforming the data. Serverless execution eliminates infrastructure management, and costs are pay-per-query, making it ideal for ad-hoc analytics.

Option B, RDS, is a relational database for transactional workloads. It cannot query raw S3 objects directly and would require data ingestion into relational tables, adding latency and operational effort.

Option C, Redshift, requires loading data into tables, which adds time, cost, and maintenance overhead. While Redshift Spectrum can query S3, Athena is fully serverless and simpler for ad-hoc queries on S3 data.

Option D, DynamoDB, is a NoSQL database for key-value or document storage. It does not support SQL-based ad-hoc queries on semi-structured datasets, and analytics requires additional ETL or processing layers.

Thus, Athena provides a serverless, scalable, and cost-effective solution for querying both structured and semi-structured S3 data without moving it, making it ideal for modern data lake analytics.

Question 31:

You need to ingest streaming data from thousands of IoT devices into AWS and allow real-time analytics without managing servers. Which architecture is most suitable?

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

Answer: A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3

Explanation 

Option A, Kinesis Data Streams + Lambda + S3, is ideal for high-volume IoT ingestion with real-time processing. Kinesis Data Streams provides durable, ordered ingestion for streaming data, automatically scaling with incoming traffic. AWS Lambda allows serverless transformations, filtering, and enrichment of the data in real time. Amazon S3 serves as durable storage, queryable with Athena or Redshift Spectrum for analytics. This architecture is serverless, requires minimal operational overhead, and provides low-latency insights.

Option B, SQS + RDS, is unsuitable for high-volume IoT streaming. SQS is a message queue with at-least-once delivery, but it does not support stream processing or real-time analytics. RDS is optimized for transactional workloads, not massive ingestion or time-series analysis, creating potential latency and scaling issues.

Option C, SNS + DynamoDB, allows event-driven ingestion and high write throughput but lacks time-series analytics and query capabilities for large-scale streaming datasets. DynamoDB is excellent for key-value lookups, not for analytics on massive historical or real-time datasets.

Option D, Redshift + Kinesis Data Firehose, is more suited for batch or micro-batch analytics rather than real-time processing. Firehose delivers data in intervals to Redshift, introducing latency, and Redshift is not serverless.

Thus, Kinesis + Lambda + S3 provides a fully serverless, scalable, and real-time IoT ingestion pipeline, enabling analytics and dashboards with minimal operational effort.

Question 32:

You want to perform ETL on semi-structured JSON data in S3 and load it into Redshift nightly, with minimal infrastructure management. Which service is most suitable?

A) AWS Glue
B) Amazon EMR
C) AWS Data Pipeline
D) Amazon Athena

Answer: A) AWS Glue

Explanation 

Option A, AWS Glue, is a serverless ETL service that automates schema inference, transformation, and data loading. Glue crawlers can scan S3 JSON datasets, update the Glue Data Catalog, and manage metadata. Glue ETL jobs written in Python or Scala can perform complex transformations and load the transformed data into Redshift on a scheduled basis. Serverless scaling ensures jobs run efficiently without cluster management.

Option B, Amazon EMR, is powerful for large-scale distributed processing but requires cluster provisioning and maintenance, which increases operational complexity for nightly ETL tasks. EMR is best for ad-hoc heavy processing rather than automated nightly jobs.

Option C, AWS Data Pipeline, is an older orchestration tool for moving data but lacks native transformation capabilities, requires additional configuration, and is not fully serverless. Glue has effectively replaced Data Pipeline for modern ETL.

Option D, Athena, allows ad-hoc SQL queries on S3 but does not provide scheduled ETL workflows or Redshift loading. It is designed for analytics, not automated ETL pipelines.

Thus, AWS Glue provides a fully managed, serverless ETL solution, allowing transformation of semi-structured data and automated Redshift loading with minimal operational overhead.

Question 33:

You want to query large S3 datasets using SQL without provisioning servers, and pay only for the data scanned. Which service is most appropriate?

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

Answer: A) Amazon Athena

Explanation 

Option A, Athena, is a serverless query service allowing SQL queries directly on S3 datasets. Athena integrates with the Glue Data Catalog for metadata management, supports structured and semi-structured formats, and uses columnar storage and partitioning to reduce query costs. Being pay-per-query, users only pay for the data scanned.

Option B, Redshift, requires data loading into tables and provisioning clusters. While it provides high-performance analytics, it is less cost-efficient for ad-hoc queries on raw S3 data and requires ongoing cluster management.

Option C, EMR, allows querying S3 with Hive or Spark SQL but requires cluster management, startup time, and resource configuration. For ad-hoc, serverless querying, EMR introduces unnecessary operational complexity.

Option D, AWS Glue, is primarily for ETL and data cataloging, not for interactive SQL queries. While Glue can transform data into a queryable format, it does not provide direct, serverless SQL querying for ad-hoc exploration.

Athena’s serverless, cost-efficient architecture makes it the best choice for querying large datasets on S3 without infrastructure management.

Question 34:

Your team wants to enforce MFA for users accessing cloud apps from outside the corporate network, while allowing seamless access from trusted devices. Which solution is most suitable?

A) Azure Conditional Access requiring MFA
B) Security Defaults
C) Pass-through Authentication
D) Azure AD B2B collaboration

Answer: A) Azure Conditional Access requiring MFA

Explanation 

Option A, Conditional Access (CA), allows adaptive authentication based on real-time conditions like device compliance, user risk, network location, and application type. CA policies enforce MFA for external access while enabling seamless access for trusted corporate devices. Integration with Azure AD Identity Protection allows dynamic enforcement based on risk signals, impossible travel, or credential compromise.

Option B, Security Defaults, enforces MFA globally for all users, without differentiation between internal and external access, reducing flexibility.

Option C, Pass-through Authentication, validates credentials but does not support conditional MFA enforcement, making it unsuitable for this scenario.

Option D, Azure AD B2B collaboration, manages guest accounts but cannot enforce location-based MFA for internal users.

Thus, Conditional Access requiring MFA ensures security for external users while preserving frictionless access for trusted devices.

Question 35:

You are building a centralized logging system for multiple AWS accounts. Logs must be searchable and queryable in near real-time. Which architecture is most appropriate?

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

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

Explanation 

Option A provides scalable, near-real-time centralized logging. CloudWatch Logs collects logs from multiple accounts. Kinesis Data Firehose ingests and buffers the logs, delivering them to S3 for durable storage and Elasticsearch/OpenSearch for search and dashboarding. Kibana enables visualization and filtering in near real time. This architecture supports high throughput, low latency, and analytics.

Option B, CloudTrail → S3 + Athena, is more suited for audit and historical queries, not near real-time analytics. Athena queries are batch-oriented, introducing latency.

Option C, SQS → RDS, is unsuitable. SQS is a queue, and RDS cannot handle high-volume log ingestion or real-time queries efficiently.

Option D, SNS → Redshift, is focused on messaging and batch analytics. Redshift is not optimized for real-time log searching and requires structured table loading.

Thus, CloudWatch + Firehose + S3 + Elasticsearch/OpenSearch is the best practice for centralized, searchable, near real-time log analytics.

Question 36:

You want to build a real-time analytics pipeline that ingests high-volume clickstream data, processes it, and makes it available for immediate dashboarding. Which AWS architecture is best?

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

Explanation 

Option A, Kinesis Data Streams (KDS) + Kinesis Data Analytics (KDA) + OpenSearch, is a fully managed, real-time analytics solution. KDS collects high-velocity clickstream data and supports horizontal scaling through shards, ensuring reliable, ordered delivery. KDA enables stream processing, filtering, aggregation, and enrichment of data in near real-time using SQL or Apache Flink. The transformed data can be delivered to OpenSearch, allowing low-latency querying and dashboarding via Kibana. This architecture is serverless, scalable, and fault-tolerant, enabling instant insights from streaming data.

Option B, SQS + RDS, is not suitable for real-time analytics. SQS queues messages, but it does not provide stream processing capabilities, and RDS is optimized for transactional workloads, making it inefficient for high-throughput data ingestion or aggregation.

Option C, SNS + Redshift, is oriented towards event-driven ingestion and batch analytics. While Redshift supports analytics on structured datasets, it is not optimized for real-time dashboards, and SNS only delivers messages without stream processing.

Option D, EMR + S3, can process large-scale batch datasets, but it introduces latency due to cluster provisioning and batch execution. EMR is more suitable for heavy batch ETL rather than real-time, sub-second analytics.

Thus, KDS + KDA + OpenSearch provides a real-time, serverless, scalable analytics pipeline suitable for immediate dashboarding and operational monitoring.

Question 37:

You want to store large volumes of historical IoT sensor data and query trends over time, with minimal operational overhead. Which AWS service is most appropriate?

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

Answer: A) Amazon Timestream

Explanation 

Option A, Amazon Timestream, is a purpose-built time-series database. It automatically manages data lifecycle, tiered storage, and compression, allowing queries on both recent (hot) and historical (cold) data. Timestream supports time-series functions such as windowed aggregations, interpolation, and smoothing, which are essential for IoT trend analysis. Its serverless nature reduces operational overhead, and it can scale automatically to handle millions of events per second.

Option B, DynamoDB, is a NoSQL key-value database. While it can store high-frequency IoT data, it lacks native time-series functions, and querying historical trends efficiently requires complex design patterns, additional tables, or batch processing.

Option C, Redshift, is a data warehouse for structured analytics. Redshift can store historical data, but it is not optimized for high-frequency IoT ingestion or real-time trend analysis. Continuous ingestion may require micro-batches or ETL jobs, adding latency and operational effort.

Option D, RDS, is a relational database for transactional workloads. RDS cannot efficiently handle massive time-series data or provide time-series-specific queries at scale.

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

Question 38:

You need to automate ETL workflows that extract S3 data, transform it, and load it into Redshift, handling retries and job dependencies. 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 into Redshift. Glue supports job scheduling, retry policies, and monitoring. Glue crawlers can automatically infer schema from S3 and maintain a centralized Data Catalog, allowing transformations using Python or Scala scripts. Serverless scaling ensures jobs run efficiently without infrastructure management.

Option B, EMR, is suitable for distributed ETL workloads but requires manual cluster provisioning, scaling, and management, increasing operational complexity for scheduled ETL tasks. EMR is more appropriate for ad-hoc batch processing than automated, nightly ETL pipelines.

Option C, Step Functions, orchestrates workflows but does not perform ETL directly. Step Functions can invoke Glue or Lambda, but the transformation logic must reside in those services. It is primarily an orchestration tool, not an ETL engine.

Option D, Athena, supports ad-hoc SQL queries on S3 but does not provide scheduled ETL, retry mechanisms, or Redshift integration. It is designed for analytics rather than automated ETL pipelines.

Thus, AWS Glue is the best choice for serverless, automated ETL with retries, transformations, and Redshift integration, minimizing operational effort.

Question 39:

You want to query raw S3 datasets using SQL without provisioning infrastructure, 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. It allows queries directly on S3 objects, supporting structured (CSV, Parquet) and semi-structured (JSON, ORC, Avro) data. Athena integrates with the Glue Data Catalog for schema management and supports partitioning and columnar storage to reduce cost and improve performance. Being pay-per-query, users only pay for scanned data, eliminating the need to provision servers.

Option B, Redshift, requires loading data into tables and provisioning clusters. While suitable for structured analytics, it is less cost-efficient for ad-hoc queries on raw S3 data and requires ongoing management.

Option C, EMR, allows querying S3 using Spark or Hive SQL but requires cluster provisioning and management. Startup and configuration time make it less suitable for ad-hoc, serverless queries.

Option D, AWS Glue, is primarily an ETL and data catalog service. While Glue can transform and catalog data, it does not provide serverless SQL querying on raw S3 datasets.

Athena’s serverless architecture, integration with Glue, and pay-per-query model make it the best choice for ad-hoc SQL analytics on S3 without infrastructure management.

Question 40:

You want to orchestrate a workflow of multiple ETL jobs with conditional logic, retries, and parallel execution. 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 workflow orchestration service. It allows you to define complex workflows with sequential or parallel execution, conditional branching, retries, and error handling. Step Functions integrates with Glue, Lambda, EMR, and Redshift, providing a centralized workflow control plane. It reduces operational overhead and ensures reliability and observability.

Option B, Glue, performs ETL but has limited workflow orchestration. Glue Workflows allow chaining jobs, but complex logic with conditional paths, retries, and parallel execution is better handled by Step Functions.

Option C, EMR, is a distributed processing platform. While it can run jobs, EMR does not provide orchestration, retries, or conditional logic. Workflows must be manually orchestrated using scripts or external tools.

Option D, Data Pipeline, is older, not fully serverless, and requires infrastructure management. While it can orchestrate tasks, it lacks the modern features, visual workflow design, and serverless scalability of Step Functions.

Thus, AWS Step Functions provides a reliable, scalable, and serverless solution for orchestrating complex workflows with retries, branching, and parallel execution. It is considered best practice for modern ETL orchestration on AWS.