MLS-C01 Data Engineering Practice Test 9 — 15 Questions

Question 1

Which THREE factors should be considered when choosing between Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose for a real-time data ingestion pipeline? (Choose 3.)

Accepted Answer

Need for custom data processing using AWS Lambda. Kinesis Data Streams supports custom processing via AWS Lambda consumers (using the Kinesis Client Library or direct integration), enabling real-time transformations, filtering, or enrichment. Kinesis Data Firehose does not natively support custom Lambda processing for transformation; it only allows optional Lambda functions for data format conversion or transformation before delivery, but not for arbitrary real-time processing logic.

Answer

Ability to compress data before delivery

Answer

Ability to encrypt data at rest

Question 2

Which TWO AWS services can be used to schedule and orchestrate a data pipeline that includes multiple steps such as data extraction, transformation, and loading? (Choose 2.)

Accepted Answer

Amazon Managed Workflows for Apache Airflow (MWAA). Amazon Managed Workflows for Apache Airflow (MWAA) and AWS Step Functions are both designed for orchestrating multi-step workflows, including data pipelines with extraction, transformation, and loading (ETL) steps. MWAA provides managed Apache Airflow, allowing you to define complex workflows as Directed Acyclic Graphs (DAGs) with scheduling, dependency management, and monitoring. Step Functions offers state machines with visual workflow design, conditional logic, error handling, and integration with over 200 AWS services. In contrast, AWS Lambda is ideal for serverless function execution but not for orchestrating multi-step processes with dependencies; AWS Glue is primarily an ETL service with basic job triggers and crawlers, lacking native DAG-based orchestration with conditions; Amazon CloudWatch Events (now Amazon EventBridge) is for event-driven scheduling and does not support complex workflow orchestration with multiple steps and dependencies.

Answer

AWS Lambda

Answer

AWS Glue

Answer

Amazon CloudWatch Events

Question 3

A data engineer needs to analyze large CSV files stored in Amazon S3 using SQL queries. The data is not frequently accessed, and cost is a primary concern. Which AWS service should be used to query the data directly in S3 without moving it?

Accepted Answer

Amazon Athena. Amazon Athena is a serverless interactive query service that allows you to analyze data directly in Amazon S3 using standard SQL, without needing to load or transform the data. It is ideal for this use case because it charges only for the data scanned per query, making it cost-effective for infrequently accessed large datasets. Athena integrates with AWS Glue Data Catalog for schema management and supports common formats like CSV, JSON, Parquet, and ORC.

Answer

Amazon EMR

Answer

Amazon Redshift Spectrum

Answer

AWS Glue

Question 4

A company is using Amazon Kinesis Data Streams to ingest real-time clickstream data. The data must be transformed before being stored in Amazon S3. The transformations include enrichment with reference data from Amazon DynamoDB. Which AWS service should be used to perform the transformation with minimal operational overhead?

Accepted Answer

Amazon Kinesis Data Analytics for Apache Flink. Amazon Kinesis Data Analytics for Apache Flink (Option C) is the correct choice because it provides a fully managed, stateful stream processing engine that can read directly from Kinesis Data Streams, enrich records with reference data from DynamoDB via Flink's Async I/O or JDBC connectors, and write the transformed data to S3—all without provisioning or managing any infrastructure. This minimizes operational overhead compared to self-managed solutions like EMR or Lambda-based architectures that require custom checkpointing and scaling logic.

Answer

Amazon Kinesis Data Firehose with data transformation

Answer

AWS Lambda functions invoked by Kinesis Data Streams

Answer

Amazon EMR with Apache Spark Streaming

Question 5

A data engineering team is designing a data lake on Amazon S3. Raw data is ingested in JSON format and must be partitioned by year, month, and day. The team expects high query performance for recent data but infrequent queries for older data. The data is immutable. Which storage tier configuration minimizes costs while meeting performance requirements?

Accepted Answer

Use S3 Intelligent-Tiering for the entire data lake. S3 Intelligent-Tiering automatically moves objects between access tiers (frequent, infrequent, and archive instant access) based on changing access patterns, without performance impact or lifecycle management overhead. This matches the workload: recent data is queried frequently (automatic frequent tier), older data is queried rarely (automatic infrequent/archive instant tiers), and data is immutable, so no write/delete penalties apply. It minimizes cost by charging only for the tiers actually used, while maintaining millisecond latency for all tiers.

Answer

Store all data in S3 Standard, then move to S3 Glacier after 30 days using a lifecycle policy

Answer

Store recent partitions in S3 Standard, older partitions in S3 One Zone-IA

Answer

Keep all data in S3 Standard because query performance is critical

Question 6

A data scientist needs to run a one-time SQL query on a large dataset in Amazon S3. The dataset is stored in Parquet format and is about 500 GB. The query requires complex aggregations and joins. Which AWS service should be used to minimize cost and setup time?

Accepted Answer

Amazon Athena. Amazon Athena is the correct choice because it is a serverless query service that allows you to run SQL directly on data stored in S3 without provisioning any infrastructure. For a one-time query on 500 GB of Parquet data, Athena minimizes cost (pay-per-query, no idle cluster costs) and setup time (no cluster creation or data loading). Its ability to handle complex aggregations and joins on columnar formats like Parquet makes it ideal for this ad-hoc use case.

Answer

Amazon Redshift

Answer

Amazon RDS for MySQL

Answer

Amazon EMR with Spark SQL

Question 7

A company is building a data lake on Amazon S3. Raw data is ingested from multiple sources in different formats (CSV, JSON, Parquet). The data must be cataloged and made queryable using Amazon Athena. The data schema may evolve over time. Which approach minimizes manual effort and supports schema evolution?

Accepted Answer

Use AWS Glue Crawlers to automatically create and update the Glue Data Catalog. AWS Glue Crawlers automatically infer schema from data in S3, create and update the Glue Data Catalog tables, and handle schema evolution by detecting changes such as new columns or partitions. This minimizes manual effort because the crawler runs on a schedule or trigger, and the catalog is natively integrated with Athena for querying without any additional setup.

Answer

Use Athena only, without a catalog, by directly querying files

Answer

Use Amazon EMR to process data and write to a Hive metastore

Answer

Manually create tables in Athena using DDL statements

Question 8

An e-commerce company uses Amazon Kinesis Data Firehose to deliver clickstream data to Amazon S3. The data arrives at unpredictable rates, with occasional bursts. The company needs to ensure data is delivered within 60 seconds of ingestion, and the data must be partitioned by year/month/day/hour. Which configuration meets these requirements?

Accepted Answer

Set the buffer interval to 60 seconds and enable dynamic partitioning. Setting the buffer interval to 60 seconds ensures data is flushed to Amazon S3 within that time window, meeting the 60-second delivery requirement. Enabling dynamic partitioning allows Firehose to automatically partition data by year/month/day/hour based on the data's timestamp, without needing custom code or additional services.

Answer

Set the buffer size to 1 MB and disable dynamic partitioning

Answer

Use a Lambda function to process data and write to S3 with partitioning

Answer

Use AWS Glue streaming ETL to read from Firehose and write to S3

Question 9

Which TWO AWS services can be used to transform data in transit before storing it in Amazon S3? (Choose TWO.)

Accepted Answer

AWS Glue. AWS Glue is correct because it provides a serverless data integration service that can transform data in transit using its built-in transformation jobs (e.g., PySpark scripts) before writing the results to Amazon S3. This allows you to clean, enrich, or reshape streaming or batch data as it moves through the pipeline.

Answer

Amazon Redshift Spectrum

Answer

AWS Data Pipeline

Answer

Amazon Athena

Question 10

A company is designing a data pipeline to analyze customer behavior. The pipeline must handle real-time streaming data and batch data. The data must be stored in a data lake on Amazon S3 and also made available for interactive queries. Which THREE services should be combined to build this pipeline? (Choose THREE.)

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is correct because it is the primary AWS service for ingesting and processing real-time streaming data at scale. It can capture and store streaming data from sources like clickstreams or IoT devices, making it available for downstream consumers such as AWS Glue or Amazon Athena for analysis.

Answer

Amazon Redshift

Answer

Amazon DynamoDB Streams

Question 11

A data engineering team is migrating on-premises Hadoop workloads to AWS. The workloads include batch processing using Apache Spark and interactive SQL queries. The data is stored in HDFS. Which TWO AWS services should be used to replace HDFS and provide a scalable, durable storage layer? (Choose TWO.)

Accepted Answer

Amazon EMR with EMRFS. Amazon S3 provides a highly durable (99.999999999% durability), scalable, and cost-effective object storage layer that replaces HDFS for Hadoop workloads. Amazon EMR with EMRFS allows Spark and Hive to read and write data directly from S3, treating it as a native filesystem with features like consistent view and read-after-write consistency, making it the ideal compute layer for batch processing and interactive SQL queries.

Answer

Amazon EBS

Answer

Amazon FSx for Lustre

Answer

Amazon RDS

Question 12

A data engineering team needs to ingest streaming data from thousands of IoT devices into a data lake on Amazon S3 for near-real-time analytics. The data must be partitioned by device ID and timestamp, and the team must minimize data loss during ingestion failures. Which solution is MOST appropriate?

Accepted Answer

Use Amazon Kinesis Data Firehose to write directly to S3 with dynamic partitioning.. Amazon Kinesis Data Firehose with dynamic partitioning is the most appropriate solution because it natively supports partitioning incoming data by device ID and timestamp before writing to S3, and it provides built-in data buffering and retry logic to minimize data loss during ingestion failures. Unlike a Lambda-based approach, Firehose handles large-scale streaming ingestion without requiring custom code for partitioning or error handling, making it ideal for near-real-time analytics on IoT data.

Answer

Use Amazon Kinesis Data Streams with a Lambda function that writes to S3.

Answer

Use Amazon S3 Transfer Acceleration with direct uploads from devices.

Answer

Use AWS Lambda to receive data via API Gateway and write to S3.

Question 13

A data scientist needs to query a 2 TB dataset stored in Amazon S3 using Amazon Athena. The data is in CSV format and is used for exploratory analysis. Queries are currently slow and expensive. Which action will improve query performance and reduce cost?

Accepted Answer

Convert the data to Parquet format and partition by a commonly filtered column.. Converting CSV data to Parquet (a columnar storage format) significantly reduces the amount of data scanned by Athena, as only the columns needed for the query are read. Partitioning by a commonly filtered column (e.g., date or region) further limits the data scanned to only relevant partitions, directly reducing both query cost (Athena charges per TB scanned) and query execution time.

Answer

Convert the data to JSON format to improve compression.

Answer

Increase the number of workers in the Athena query engine.

Answer

Create a composite index on the data using Athena's index feature.

Question 14

A company uses AWS Glue ETL jobs to process data from an Amazon RDS for MySQL database into Amazon S3. The job runs daily and takes 6 hours to complete. The team wants to reduce runtime and cost. The source table has 50 million rows and is updated continuously. Which combination of changes would be MOST effective?

Accepted Answer

Use JDBC connections with pushdown predicates and increase the number of DPUs.. Using JDBC pushdown predicates filters data at the source database, reducing the volume of data transferred over the network and processed by Glue. Increasing the number of DPUs (data processing units) adds parallelism, which directly reduces runtime. Together, these changes minimize both execution time and cost by optimizing data movement and compute resources.

Answer

Use a single worker with a larger instance type.

Answer

Increase the number of DPUs and enable job bookmarking.

Answer

Change the job trigger from time-based to event-based.

Question 15

A data pipeline uses Amazon Kinesis Data Streams to ingest clickstream data. The data is consumed by an AWS Lambda function that transforms and writes to Amazon DynamoDB. The Lambda function is throttled during traffic spikes, causing data to be reprocessed. Which solution should the team implement to handle the throttling without losing data?

Accepted Answer

Increase the number of shards in the Kinesis stream and configure a dead-letter queue (DLQ) for the Lambda function.. Increasing the number of shards in the Kinesis stream raises the throughput capacity, reducing the likelihood of Lambda throttling. Configuring a dead-letter queue (DLQ) for the Lambda function captures any records that fail processing after exhausting retries, preventing data loss. This combination addresses both the throttling cause and provides a safety net for unprocessed records.

Answer

Use Amazon SQS as an intermediate buffer between Kinesis and Lambda.

Answer

Enable DynamoDB auto scaling to handle writes.

Answer

Reduce the batch size in the Lambda event source mapping.