MLS-C01 Data Engineering Practice Test 19 — 15 Questions

Question 1

A data engineer needs to extract data from an Amazon RDS for MySQL database into Amazon S3 for further processing. The data volume is 2 TB and the job must run daily within a 1-hour window. Which AWS service is most suitable for this task?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

AWS Glue. AWS Glue is the most suitable service because it provides a fully managed ETL (Extract, Transform, Load) capability that can efficiently extract 2 TB of data from Amazon RDS for MySQL and write it to Amazon S3. Glue can leverage JDBC connections to the RDS instance, scale horizontally with its dynamic worker allocation, and complete the job within a 1-hour window by using appropriate worker types (e.g., G.2X or G.8X) and partitioning strategies. Additionally, Glue integrates natively with the AWS Glue Data Catalog and can handle incremental or full-load extraction with minimal overhead.

Answer

Amazon Kinesis Data Firehose

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AWS Database Migration Service (DMS)

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Amazon Athena

Question 2

A company is building a data lake on Amazon S3. Data arrives from multiple sources in different formats (CSV, JSON, Parquet). The engineering team wants to query this data using Amazon Athena with minimal transformation. Which approach minimizes query cost and improves performance?

Accepted Answer

Use AWS Glue to convert all data to Parquet format, partition by date, and store in a separate S3 bucket. Converting data to Parquet format (a columnar storage format) significantly reduces the amount of data scanned by Athena, which directly lowers query cost (Athena charges per TB scanned). Partitioning by date further limits scanned data by pruning irrelevant partitions. AWS Glue provides a serverless ETL service to perform this conversion efficiently, and storing the output in a separate S3 bucket avoids polluting the raw data lake.

Answer

Use Amazon Redshift Spectrum to query the data directly without transformation

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Use Amazon EMR to convert data to CSV format and repartition

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Store data as-is in S3 and create external tables in Athena for each format

Question 3

A data pipeline uses AWS Lambda to process small files (10-50 MB) from an S3 bucket and write results to DynamoDB. The Lambda function times out after 15 seconds for larger files. The team wants to handle files up to 100 MB without changing the Lambda code. Which solution is MOST cost-effective?

Accepted Answer

Increase the Lambda function timeout to 5 minutes. Increasing the Lambda function timeout to 5 minutes directly addresses the 15-second timeout issue for larger files (up to 100 MB) without requiring any code changes. This is the most cost-effective solution as it avoids additional infrastructure costs (e.g., Glue, ECS, SQS) and leverages Lambda's existing pay-per-execution pricing model, which remains economical for occasional longer-running invocations.

Answer

Use AWS Glue Python shell job to replace Lambda

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Use Amazon ECS with AWS Fargate to run the processing task

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Configure an SQS queue to buffer the S3 events and batch them

Question 4

A data scientist needs to run a one-time query on 10 TB of data stored in S3 using Amazon Athena. The query scans 5 TB and returns a small result set. Which approach minimizes cost?

Accepted Answer

Query the data directly in Athena without any preprocessing. Athena charges based on the amount of data scanned per query. Since this is a one-time query on 10 TB of data that scans only 5 TB, querying directly in Athena without preprocessing is the most cost-effective approach because you pay only for the 5 TB scanned, with no additional costs for data conversion, storage, or cluster provisioning.

Answer

Create an S3 Select query to filter data before Athena

Answer

Use Amazon Redshift Spectrum to query the data

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Use AWS Glue to convert the data to Parquet format and repartition by date

Question 5

A company uses Amazon Kinesis Data Firehose to ingest streaming data and deliver it to an S3 bucket. The data is in JSON format with a timestamp field. The data science team wants to query the data using Athena with partitioning by year/month/day. How should the S3 data be organized?

Accepted Answer

Configure Firehose to use dynamic partitioning with custom prefix. Kinesis Firehose can partition data using custom prefixes like 'year=!{timestamp:yyyy}/month=!{timestamp:MM}/day=!{timestamp:dd}/'. This creates Hive-style partitions that Athena can automatically discover.

Answer

Store data in a single prefix and use Athena's 'partition projection' feature

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Use AWS Glue crawler to partition the data after delivery

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Use Amazon EMR to partition the data after delivery

Question 6

An organization is migrating its on-premises Hadoop cluster to AWS. The cluster runs Spark jobs that process 50 TB of data daily. The data is stored in HDFS with 3x replication. Which storage option on AWS provides the best price-performance for this workload?

Accepted Answer

Use Amazon EMR with S3 as the data store via EMRFS. Amazon EMR with S3 as the data store via EMRFS provides the best price-performance for this workload because it eliminates the need for 3x replication (S3 is inherently durable and replicated across multiple AZs), reduces storage costs, and allows compute and storage to scale independently. EMRFS enables Spark jobs to read/write directly to S3 with consistency guarantees, matching the throughput requirements of 50 TB daily processing without the overhead of managing HDFS on EBS volumes.

Answer

Use AWS Glue to run Spark jobs with data stored in S3

Answer

Use Amazon Redshift Spectrum to query the data directly in S3

Answer

Use Amazon EMR with HDFS on EBS volumes

Question 7

A company needs to ingest real-time clickstream data from thousands of web servers into AWS for near-real-time analytics. The data volume varies and can spike during promotions. Which service should be used to capture and buffer the data before processing?

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is the correct choice because it is designed for real-time data ingestion and buffering of large streams of data, such as clickstream events from thousands of web servers. It provides durable, low-latency storage (up to 365 days retention) and supports multiple consumers for near-real-time analytics, making it ideal for handling variable and spiky data volumes during promotions.

Answer

Amazon SQS

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Amazon Kinesis Data Firehose

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Amazon MQ

Question 8

A data engineer uses AWS Glue to run ETL jobs that transform data from JSON to Parquet. The job runs successfully but takes 30 minutes longer than expected. CloudWatch metrics show high memory utilization and disk spills. What is the most likely cause?

Accepted Answer

The data is skewed and not evenly distributed across partitions. High memory utilization and disk spills in AWS Glue indicate that the data is not evenly distributed across partitions, causing some executors to handle a disproportionate amount of data. This data skew leads to excessive spilling to disk as memory is exhausted, which significantly slows down the job. Option D directly addresses this root cause, as skewed data prevents efficient parallel processing.

Answer

The number of DPUs is too low

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The sink bucket has insufficient I/O throughput

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The source data format is too large

Question 9

A company stores sensitive customer data in an S3 bucket. The security team requires that all data be encrypted at rest with a key that is automatically rotated every year. Which solution meets these requirements with the least operational overhead?

Accepted Answer

Use SSE-S3 (Amazon S3-managed keys). SSE-S3 uses Amazon S3-managed keys (AES-256) that are automatically rotated annually by AWS, meeting the encryption-at-rest and automatic rotation requirements with zero operational overhead. This is the simplest option because no key management or rotation configuration is needed from the customer.

Answer

Use SSE-KMS with a customer-managed key and automatic rotation

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Use SSE-C (customer-provided keys)

Answer

Use SSE-KMS with a customer-managed key and manual rotation

Question 10

Which TWO options are valid ways to reduce the amount of data scanned by Amazon Athena queries, thereby reducing cost?

Accepted Answer

Use columnar storage formats like Parquet or ORC. A is correct because columnar storage formats like Parquet and ORC store data in a compressed, column-oriented layout. When Athena queries only a subset of columns, it can skip reading the entire row, drastically reducing the amount of data scanned from disk. This directly lowers the cost, as Athena charges based on the volume of data read per query.

Answer

Use LIMIT clause in SQL queries

Answer

Convert data to CSV format

Answer

Create materialized views in Athena

Question 11

Which THREE AWS services can be used together to build a serverless data pipeline that ingests streaming data, transforms it, and loads it into Amazon Redshift for analysis?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose (C) is the correct service because it is designed to reliably capture, transform, and load streaming data into Amazon Redshift with near-real-time latency. It can invoke AWS Lambda for on-the-fly data transformation (e.g., converting JSON to Parquet) and directly stream the processed records into Redshift via the Redshift COPY command, making it the central orchestration component for a serverless pipeline.

Answer

Amazon EMR

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Amazon SQS

Question 12

Which TWO options are best practices for managing access to data stored in Amazon S3 for a data lake?

Accepted Answer

Use IAM policies to control user and role permissions. C is correct because IAM policies are the primary mechanism for controlling access to AWS services, including S3, for users and roles within an AWS account. They allow you to define fine-grained permissions based on identity, which is a best practice for managing access to a data lake. This aligns with the principle of least privilege and centralized access control.

Answer

Use S3 access control lists (ACLs) for granular permissions

Answer

Enable default encryption with SSE-S3

Answer

Generate pre-signed URLs for all data access

Question 13

A data engineer is investigating why an Athena query against the my-data-lake bucket is slow. The query filters on year, month, and day. The exhibit shows the metadata of one Parquet file. What is the MOST likely cause of the slow query?

Accepted Answer

The file is too large, causing Athena to process it in a single task. The Parquet file is 1 GB in size, which is too large for efficient processing in Athena. Athena splits data into tasks for parallel execution, but a single large file cannot be split, causing the query to run slowly. Partitioning on year, month, and day is already applied and is not the issue. The other options are incorrect: version ID null is irrelevant, the query does use partition columns, and standard storage is faster than Glacier.

Answer

The version ID is null, causing data inconsistency

Answer

The partition columns are not being used in the query

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The storage class is STANDARD, which is slower than GLACIER

Question 14

The Glue job my-glue-job fails after a few successful runs. The error log shows 'Job run exceeds max concurrent runs limit'. The CloudFormation template is shown in the exhibit. What change should be made to allow multiple runs to execute concurrently?

Accepted Answer

Set the MaxConcurrentRuns property to 3. The 'MaxConcurrentRuns' is set to 1, which prevents parallel executions. Setting it to a higher value (e.g., 3) allows concurrent runs. MaxRetries is for retry count, not concurrency. Role and TempDir are not relevant.

Answer

Change the IAM role to one with more permissions

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Increase the MaxRetries property to 3

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Remove the --job-bookmark-option argument

Question 15

A Glue job fails with an AccessDenied error when trying to write to the S3 bucket my-data-lake. The IAM policy attached to the job role is shown in the exhibit. What is the MOST likely reason for the failure?

Accepted Answer

The job role does not have permissions to decrypt the KMS key used for server-side encryption. The policy allows s3:PutObject on the bucket, so write access seems granted. However, if the bucket is encrypted with SSE-KMS, the job also needs kms:Decrypt and kms:GenerateDataKey permissions. The policy does not include KMS actions. The bucket policy might also deny, but the most common issue is KMS encryption.

Answer

The s3:ListBucket action is missing on the bucket level

Answer

The s3:PutObject action is not sufficient; the job needs s3:PutObjectAcl

Answer

The resource ARN for s3:PutObject should include a specific prefix