8 of the 81 questions in this set, with the correct answer marked and every option explained.
1. A machine learning team has several large CSV datasets in Amazon S3. Historically, models built with the Amazon SageMaker Linear Learner algorithm have taken hours to train on similar-sized datasets. The team’s leaders need to accelerate the training process. What can a machine learning specialist do to address this concern?
✗Use Amazon Machine Learning to train the models.
Amazon Machine Learning was a simplified, now-retired AWS service for building predictive models from data already in S3, Redshift or RDS; it never addressed training speed and offered none of SageMaker's high-throughput data-loading modes.
✗Use Amazon Kinesis to stream the data to Amazon SageMaker.
Kinesis moves streaming data between producers and consumers in real time; the datasets here are static CSVs already sitting in S3, so there is no stream to build, and Kinesis does nothing to speed up how SageMaker reads training data.
✗Use AWS Glue to transform the CSV dataset to the JSON format.
Glue can reformat the files, but JSON is not a faster format for Linear Learner to ingest than CSV — the fix for slow training is changing how SageMaker reads the data (Pipe mode), not what format it's stored in.
✓Use Amazon SageMaker Pipe mode.
Pipe mode streams training data directly from S3 to the algorithm container instead of first copying the full dataset onto the training instance's disk, which cuts the startup and I/O time that was making training slow.
2. A data engineer needs to set up a batch data ingestion pipeline that ingests data from an on-premises database into Amazon Redshift. Which service should the engineer use to accomplish this task?
✗Amazon Kinesis Data Streams
A low-level streaming service for continuous, real-time data producers and consumers — well suited to live event data, not a one-time batch load from an existing on-premises database.
✓AWS Database Migration Service (DMS)
DMS is purpose-built to replicate data from an external, on-premises database into an AWS target like Redshift, handling schema conversion and change capture along the way.
✗Amazon S3
Durable, scalable object storage — not a relational or NoSQL database, and on its own it has no compute engine to process real-time streams or run complex ETL transformations.
✗AWS Glue
A serverless ETL and data-cataloging service for batch and event-driven data transformation — it doesn't provide real-time low-latency stream compute on its own, train machine learning models, enforce per-row access control, or monitor a running job's health.
3. A data engineer needs to choose a storage solution for a highly dynamic dataset with frequent read and write operations. Which service is the most appropriate?
✓Amazon DynamoDB
DynamoDB is a managed NoSQL database designed for exactly this profile: very high request rates with single-digit-millisecond latency on both reads and writes.
✗Amazon Redshift
A petabyte-scale SQL data warehouse for analytical queries — not built for low-latency transactional access, general object storage, machine learning model training or deployment, or workflow/job orchestration on its own.
✗Amazon RDS
A managed relational database for structured, transactional (OLTP) workloads — not built for petabyte-scale analytics, key-value access at extreme scale, general object storage, time-series data, or scheduled infrastructure backups on its own.
✗Amazon S3
Durable, scalable object storage — not a relational or NoSQL database, and on its own it has no compute engine to process real-time streams or run complex ETL transformations.
4. A data engineer needs to automate the deployment of a data processing pipeline. Which AWS service is suitable for this task?
✗Amazon Redshift
A petabyte-scale SQL data warehouse for analytical queries — not built for low-latency transactional access, general object storage, machine learning model training or deployment, or workflow/job orchestration on its own.
✗Amazon EMR
A managed Hadoop/Spark cluster service for large-scale batch data processing — not a deployment-automation tool, a metadata catalog, a serverless query engine, or a lightweight real-time stream-analytics service.
✗Amazon Athena
A serverless SQL query engine for data already in S3 — built for ad hoc querying, not for training machine learning models, automating deployments, enforcing fine-grained access control, cataloging metadata, or processing real-time streams on its own.
✓AWS CodePipeline
CodePipeline automates the build-test-deploy stages of a pipeline's code and configuration, which is what 'automate the deployment' calls for.
5. Which AWS service can be used to manage encryption keys for data stored in AWS services?
✗Amazon GuardDuty
Continuously analyzes account activity for malicious or unauthorized behavior — a threat-detection service, not an encryption-key manager, sensitive-data classifier, web-application firewall, or configuration-compliance tracker.
✗AWS Secrets Manager
Stores and rotates secrets such as database credentials and API keys — a credential-management tool, not a data-masking, encryption-key-management, data-discovery, or web-application-firewall service.
✓AWS Key Management Service (KMS)
KMS is AWS's dedicated service for creating, storing and controlling access to the cryptographic keys used to encrypt data across other AWS services.
✗AWS Shield
A DDoS-protection service for network- and application-layer attacks — a different security control from encryption-key management, account-activity threat detection, or sensitive-data discovery.
6. Which AWS service can be used to transform streaming data in real-time before storing it in a data lake?
✗Amazon Redshift
A petabyte-scale SQL data warehouse for analytical queries — not built for low-latency transactional access, general object storage, machine learning model training or deployment, or workflow/job orchestration on its own.
✗AWS Lambda
Runs custom code in response to events — a useful building block inside a pipeline, but on its own it isn't a managed ETL/orchestration service, a SQL-based stream-analytics engine, or a big-data cluster framework.
✓Amazon Kinesis Data Firehose
Firehose is built specifically to buffer, optionally transform (via a Lambda function) and deliver streaming records into a destination like S3 or a data lake, with no infrastructure to manage.
✗AWS Glue
A serverless ETL and data-cataloging service for batch and event-driven data transformation — it doesn't provide real-time low-latency stream compute on its own, train machine learning models, enforce per-row access control, or monitor a running job's health.
7. Which AWS service provides a data catalog that helps discover and manage metadata for data stored in AWS?
✗Amazon EMR
A managed Hadoop/Spark cluster service for large-scale batch data processing — not a deployment-automation tool, a metadata catalog, a serverless query engine, or a lightweight real-time stream-analytics service.
✓AWS Glue
The Glue Data Catalog is AWS's central metadata repository, and Glue crawlers populate it automatically by scanning data sources — the standard way to make data discoverable and queryable.
✗Amazon Athena
A serverless SQL query engine for data already in S3 — built for ad hoc querying, not for training machine learning models, automating deployments, enforcing fine-grained access control, cataloging metadata, or processing real-time streams on its own.
✗Amazon Redshift
A petabyte-scale SQL data warehouse for analytical queries — not built for low-latency transactional access, general object storage, machine learning model training or deployment, or workflow/job orchestration on its own.
8. Which AWS service provides real-time monitoring and logging capabilities for applications running in AWS?
✗AWS CloudTrail
Logs the API calls made across an AWS account to build an audit trail for security and compliance reviews — a logging and auditing tool, not a data-processing, storage, monitoring-dashboard, or access-control service.
✓Amazon CloudWatch
CloudWatch collects and displays the metrics, logs and alarms that show how applications and resources are behaving in real time.
✗AWS Trusted Advisor
Surfaces account-wide best-practice recommendations on cost, security and fault tolerance — it doesn't track configuration-change history or log account API calls the way Config and CloudTrail do.
✗AWS Config
Continuously records AWS resource configurations and evaluates them against rules to flag compliance drift — a configuration-tracking tool, not a data-processing, encryption, or general application-monitoring service.
73 more questions in the app
Practise the full 81-question set with a timer, scoring and progress tracking.
