MLS-C01 Data Engineering Practice Test 4 — 15 Questions

Question 1

Which TWO AWS services can be used to move data from an on-premises database to Amazon S3 on a recurring schedule without writing custom code? (Choose 2.)

Accepted Answer

AWS Glue. AWS Glue is correct because it provides a fully managed ETL service that can run crawlers and jobs on a recurring schedule to extract data from on-premises databases (via JDBC connections) and write it to Amazon S3 without requiring any custom code. AWS DMS is correct because it supports continuous replication or scheduled tasks to migrate data from on-premises databases to S3 as a target, using built-in transformation capabilities and no custom scripting.

Answer

AWS Snowball Edge

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Amazon Athena

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Amazon Kinesis Data Firehose

Question 2

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. Option C is correct because Kinesis Data Streams supports custom consumers (including AWS Lambda) that can read and process records with arbitrary logic, whereas Firehose only performs managed transformations via Lambda for record-level conversion. Option D is correct because Data Streams retains records for a configurable period (24 hours by default, extendable up to 365 days), while Firehose does not retain data—it delivers it to the destination and discards it. Option E is correct because Data Streams offers near-real-time (sub-second to low-seconds) delivery to consumers, whereas Firehose buffers data (60 seconds by default, or based on size) before delivering to S3, so latency requirements drive the choice. Options A and B are not distinguishing factors: both services support data compression before delivery (Firehose natively, Streams via consumers) and both support encryption at rest using AWS KMS, so they do not help decide between the two.

Answer

Ability to compress data before delivery

Answer

Ability to encrypt data at rest

Question 3

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) (C) is correct because it is a fully managed service for Apache Airflow, which is purpose-built to author, schedule, and monitor multi-step data pipelines (DAGs) with dependencies between extraction, transformation, and loading tasks. AWS Step Functions (D) is also correct because it is a serverless orchestration service that models workflows as state machines, allowing you to sequence and coordinate multiple steps such as Lambda-based extraction, Glue transformations, and load operations with retries and error handling. AWS Lambda (A) only runs individual functions and has no native multi-step scheduling or orchestration capability, so it is not a scheduling/orchestration service on its own. AWS Glue (B) is a managed ETL service that can run jobs and has triggers, but it is primarily for data cataloging and transformation rather than general-purpose orchestration of arbitrary multi-step pipelines. Amazon CloudWatch Events (E), now Amazon EventBridge, is an event bus for routing events and can trigger scheduled rules, but it does not orchestrate or track the state of a multi-step pipeline.

Answer

AWS Lambda

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AWS Glue

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Amazon CloudWatch Events

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 company is using AWS Glue to run ETL jobs that transform data from Amazon S3 to Amazon Redshift. The jobs are currently failing due to insufficient memory. The data volume varies, with occasional spikes. Which solution should be used to handle the variable memory requirements efficiently?

Accepted Answer

Increase the number of DPUs (Data Processing Units) for all jobs. To handle variable memory requirements efficiently, increasing the number of DPUs for all jobs ensures sufficient memory during spikes. AWS Glue Flex execution uses spare capacity and can be preempted, so it does not guarantee resource availability and is not suitable for handling memory insufficiencies. Option C provides a direct way to allocate more resources, preventing job failures due to insufficient memory.

Answer

Migrate the ETL jobs to Amazon EMR with Apache Spark

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Use AWS Glue Flex execution to allocate resources dynamically

Answer

Split the jobs into smaller steps and run them sequentially

Question 7

A data pipeline uses Amazon Kinesis Data Streams to ingest event data. The data is consumed by an AWS Lambda function, which writes to Amazon DynamoDB. The Lambda function is experiencing throttling errors, and the DynamoDB write capacity is underutilized. The events must be processed in order per shard. Which solution most effectively addresses the throttling?

Accepted Answer

Increase the Lambda function's reserved concurrency. The Lambda function is throttling because the number of concurrent invocations from the Kinesis stream exceeds the function's concurrency limit (default 1000 per region). Increasing the number of shards would increase the concurrency, worsening the throttling. The correct approach is to increase the Lambda function's reserved concurrency, which raises the number of allowed concurrent executions, directly addressing the throttling while preserving per-shard ordering.

Answer

Use an SQS FIFO queue between Kinesis and Lambda to buffer

Answer

Increase the write capacity units of the DynamoDB table

Answer

Increase the number of shards in the Kinesis Data Stream

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

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 (option B) is the correct replacement for HDFS because it provides a highly durable (11 nines), scalable, and cost-effective object storage layer that decouples storage from compute, which is the standard AWS pattern for migrating Hadoop/HDFS data. Amazon EMR with EMRFS (option A) is also correct because EMRFS is the EMR file system implementation that lets Apache Spark, Hive, and other Hadoop-compatible frameworks read and write data directly in Amazon S3 using the s3:// or s3a:// scheme, effectively replacing HDFS as the storage layer for batch and interactive SQL workloads. Together, S3 as the durable storage backend and EMR/EMRFS as the compute-and-access layer satisfy the requirement for scalable, durable storage that supports Spark batch processing and interactive SQL. Amazon EBS (option C) is block storage attached to a single EC2 instance and is not a shared, scalable HDFS replacement. Amazon FSx for Lustre (option D) is a high-performance parallel file system often used as a temporary scratch layer linked to S3, but it is not the durable primary storage replacement for HDFS. Amazon RDS (option E) is a managed relational database service and is unrelated to Hadoop/HDFS storage.

Answer

Amazon EBS

Answer

Amazon FSx for Lustre

Answer

Amazon RDS

Question 10

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 11

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.

Question 12

A financial services company needs to build a data lake on Amazon S3 that meets regulatory requirements for data retention and encryption. Data must be encrypted at rest and in transit, and access must be audited. The data lake will be queried by Amazon Athena and Amazon Redshift Spectrum. Which combination of actions should be taken?

Accepted Answer

Enable S3 default encryption with SSE-KMS and enable AWS CloudTrail for S3 data events.. Regulatory requirements for encryption at rest and audited access are best met by enabling S3 default encryption with SSE-KMS, which provides customer-managed key control and auditability via CloudTrail, and enabling CloudTrail data events for S3, which logs object-level access. This combination satisfies encryption and auditing mandates for Athena and Redshift Spectrum queries.

Answer

Use IAM policies to control access and enable S3 server access logging.

Answer

Use SSL/TLS for all connections and enable S3 versioning.

Answer

Enable S3 default encryption with SSE-S3 and use S3 access logs.

Question 13

A data engineering team is building a pipeline to process terabytes of log data daily using Amazon EMR with Spark. The data arrives in hourly batches and must be processed within 4 hours. The team needs to minimize cost. Which cluster configuration is MOST cost-effective?

Accepted Answer

Use a transient cluster with a mix of on-demand and spot instances, terminated after the job completes.. A transient cluster with a mix of on-demand and spot instances minimizes cost for batch workloads that have a defined lifecycle. Spot instances offer significant discounts (up to 90%) for fault-tolerant Spark jobs, and terminating the cluster after processing eliminates idle compute charges. This approach aligns with the 4-hour processing window and hourly batch arrival, as EMR can provision and tear down clusters quickly.

Answer

Use a single large instance with multiple cores to avoid data shuffling.

Answer

Use a long-running cluster of on-demand instances to avoid startup time.

Answer

Use Amazon EMR Serverless to automatically scale.

Question 14

A company runs a real-time fraud detection pipeline using Amazon Kinesis Data Analytics. The pipeline reads from a Kinesis data stream, performs sliding window aggregations, and writes results to a DynamoDB table. The application is experiencing high latency during peak hours. Which action would MOST effectively reduce latency?

Accepted Answer

Increase the number of shards in the Kinesis data stream.. Increasing the number of shards in the Kinesis data stream directly increases the ingestion capacity and parallelism of the stream, allowing the Kinesis Data Analytics application to consume and process records faster. This addresses the root cause of high latency during peak hours by scaling the data source throughput, which is the bottleneck in a streaming pipeline.

Answer

Enable DynamoDB auto scaling to handle write spikes.

Answer

Decrease the parallelism level in the Kinesis Data Analytics application.

Answer

Increase the sliding window size to reduce computational frequency.

Question 15

A data engineering team needs to schedule a nightly ETL job that extracts data from an Amazon RDS for PostgreSQL instance, transforms it using Spark, and loads it into Amazon S3. The team wants to use AWS Glue for this task. Which components are required? (Select TWO.)

Accepted Answer

An AWS Glue ETL job with a Spark script.. Option A is correct because AWS Glue ETL jobs run Spark scripts (Python/Scala) on serverless Spark infrastructure, which is exactly what is needed to perform the transform step of the ETL pipeline. Option C is correct because AWS Glue needs a connection resource to reach the RDS for PostgreSQL instance inside a VPC, supplying JDBC URL, credentials, subnet, and security group details so the job can extract data. Option B is not required because a crawler is only needed to discover schemas and populate the Data Catalog; the team can define the source and target schemas directly in the job script or via explicit catalog tables. Option D is not required because a development endpoint is only for interactive development and debugging of Glue scripts, not for running the scheduled nightly job. Option E is not required because a Glue notebook is a development/exploration tool and is not part of the production ETL execution path.

Answer

An AWS Glue crawler to populate the Data Catalog.

Answer

An AWS Glue development endpoint.

Answer

An AWS Glue notebook for data exploration.