MLS-C01 Data Engineering Practice Test 25 — 15 Questions

Question 1

Refer to the exhibit. A data engineer has deployed this CloudFormation template. The Glue job 'my-etl-job' reads from the S3 bucket 'my-data-lake-bucket' and writes transformed data to another bucket. After 30 days, the data engineer notices that the Glue job fails with 'Input data not found' errors. What is the most likely cause?

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

Accepted Answer

The lifecycle configuration deletes objects from the bucket after 30 days, removing the input data.. The lifecycle configuration on the S3 bucket 'my-data-lake-bucket' is set to delete objects after 30 days. Since the Glue job 'my-etl-job' reads input data from this bucket, once the 30-day period elapses, the input data is removed, causing the 'Input data not found' error. This matches the symptom of the job failing after exactly 30 days.

Answer

The temporary directory 'my-temp-dir' is being cleaned up by the lifecycle configuration.

Answer

The script location 's3://my-scripts/etl.py' is being deleted by the lifecycle rule.

Answer

The job bookmark option 'job-bookmark-enable' is causing the job to skip newly arriving data.

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

Answer

Use Amazon EMR to convert data to CSV format and repartition

Answer

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

Question 3

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

Answer

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

Question 4

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 Data Firehose dynamic partitioning evaluates a JSON key (such as the timestamp field) and writes records into Hive-style prefixes like year=2024/month=01/day=15/, which Athena can query with partition pruning. This avoids post-processing and delivers partitioned data directly to S3. It is the native, serverless way to achieve the requirement.

Answer

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

Answer

Use AWS Glue crawler to partition the data after delivery

Answer

Use Amazon EMR to partition the data after delivery

Question 5

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

Answer

Amazon Kinesis Data Firehose

Answer

Amazon MQ

Question 6

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

Answer

The sink bucket has insufficient I/O throughput

Answer

The source data format is too large

Question 7

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. Option A is correct because Athena charges based on the amount of data scanned, and columnar formats such as Parquet or ORC store data by column so that queries reading only a subset of columns scan far less data than row-based formats like CSV or JSON. Option E is correct because partitioning the data by a frequently filtered column (for example, partitioning by date or region) lets Athena use partition pruning to skip entire partitions that do not match the query's WHERE predicate, drastically reducing bytes scanned. Option B is not valid because a LIMIT clause only restricts the number of rows returned after scanning has already occurred; Athena still scans the full underlying data unless the query itself is constrained by partitions or column projection. Option C is not valid because converting data to CSV is a row-based, non-columnar format that typically increases the data scanned compared to Parquet or ORC. Option D is not valid because Athena does not natively support materialized views as a cost-reduction mechanism; while you can create views, they are logical and do not precompute or store results to reduce scanned data.

Answer

Use LIMIT clause in SQL queries

Answer

Convert data to CSV format

Answer

Create materialized views in Athena

Question 8

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 correct because it is a fully managed, serverless service that can ingest streaming data and deliver it directly into Amazon Redshift (via S3 staging and the Redshift COPY command), making it ideal for the load stage of the pipeline. Amazon Kinesis Data Streams (D) is correct because it provides the serverless, scalable ingestion layer for capturing and buffering streaming data before it is consumed or transformed downstream. AWS Lambda (E) is correct because it is a serverless compute service that can be triggered by Kinesis to transform records in flight before they are loaded into Redshift, satisfying the transform requirement without managing servers. Amazon EMR (A) is not correct here because it is a managed big-data cluster service (EC2-based) rather than a serverless component, and it is not needed for this streaming ingest-transform-load pattern. Amazon SQS (B) is not correct because it is a message queue for decoupling applications, not a streaming data ingestion or delivery service for Redshift pipelines.

Answer

Amazon EMR

Answer

Amazon SQS

Question 9

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. Option C is correct because IAM policies are the primary, centrally managed mechanism for granting users and roles fine-grained permissions to S3 data lake resources, allowing least-privilege access tied to identities. Option D is correct because S3 bucket policies are resource-based policies that can explicitly grant or deny cross-account access, which is essential when external accounts or partners need controlled access to the data lake. Option A is not a best practice because S3 ACLs are legacy, coarse-grained, and AWS recommends disabling them in favor of IAM and bucket policies. Option B is not a best practice for access management because SSE-S3 default encryption protects data at rest but does not control who can access it. Option E is not a best practice for general access management because pre-signed URLs grant temporary access to specific objects and are unsuitable as a primary, scalable authorization strategy.

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 10

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 error 'Job run exceeds max concurrent runs limit' indicates that the AWS Glue job is configured to allow fewer concurrent runs than are being attempted. The MaxConcurrentRuns property in the job definition controls how many runs of the same job can execute simultaneously. Setting it to 3 would allow up to three concurrent runs, resolving the error.

Answer

Change the IAM role to one with more permissions

Answer

Increase the MaxRetries property to 3

Answer

Remove the --job-bookmark-option argument

Question 11

A data engineer needs to automate the transformation of CSV files to Parquet format as soon as they are uploaded to an S3 bucket. The transformed files should be stored in another S3 bucket. Which solution is the most cost-effective and requires the least maintenance?

Accepted Answer

Configure an S3 event notification to invoke a Lambda function.. AWS Lambda, triggered by S3 event notifications, provides a serverless, event-driven architecture that automatically converts CSV to Parquet upon file upload. This approach is cost-effective because you pay only for compute time during execution, and it requires minimal maintenance as AWS manages the infrastructure, scaling, and fault tolerance.

Answer

Configure an S3 event notification to invoke an AWS Glue job.

Answer

Run an Amazon EMR cluster continuously to watch for new files.

Answer

Set up an EC2 instance with a cron job to poll the S3 bucket.

Question 12

Which TWO data formats are columnar and optimized for analytics queries in Amazon S3?

Accepted Answer

ORC. ORC (B) is a columnar storage format that stores data by column rather than row, enabling predicate pushdown, column pruning, and compression that make analytics queries in Amazon S3 (e.g., via Athena, EMR, or Redshift Spectrum) far more efficient. Parquet (E) is likewise a columnar format with schema evolution, efficient encoding, and compression, and is natively supported by AWS analytics services for high-performance S3 queries. CSV (A) is a row-based plain-text format with no columnar layout or built-in compression, so it is not optimized for analytics. JSON (C) is a row-oriented, self-describing text format that is flexible for ingestion but inefficient for large-scale analytical scans. Avro (D) is a row-based format designed for serialization and streaming/write-heavy workloads, not columnar analytics.

Answer

CSV

Answer

JSON

Answer

Avro

Question 13

Which THREE considerations are important when designing a data lake on Amazon S3?

Accepted Answer

Setting up S3 Lifecycle policies to transition data to colder storage. Option A is correct because S3 Lifecycle policies let you automatically transition objects to colder storage classes such as S3 Standard-IA, S3 Glacier Instant Retrieval, or S3 Glacier Deep Archive, which is essential for controlling cost as a data lake grows. Option C is correct because partitioning data by date (for example, s3://bucket/year=2024/month=05/day=01/) lets query engines like Amazon Athena, Redshift Spectrum, or AWS Glue prune irrelevant partitions, dramatically reducing data scanned and improving query performance. Option E is correct because encrypting data at rest with AWS KMS (SSE-KMS) provides centralized key management, auditability via CloudTrail, and compliance for sensitive data stored in the lake. Option B is incorrect because Provisioned IOPS is an EBS volume feature; S3 does not offer provisioned IOPS and scales throughput automatically. Option D is incorrect because S3 is inherently replicated across multiple Availability Zones within a Region, so restricting data to a single AZ is neither possible nor desirable for durability.

Answer

Using Provisioned IOPS for S3

Answer

Using a single Availability Zone for data storage

Question 14

A data scientist is training a deep learning model on a GPU instance. The training data is stored in S3 and is 50 GB. To reduce I/O bottlenecks, which storage option should be used to cache the data locally on the instance?

Accepted Answer

Mount an Amazon FSx for Lustre file system linked to the S3 bucket. Amazon FSx for Lustre is a high-performance file system optimized for compute-intensive workloads like deep learning. It can be linked to an S3 bucket, allowing the data to be lazily loaded from S3 and cached on the file system, providing low-latency access to the training data. This reduces I/O bottlenecks by providing high throughput and low latency.

Answer

Attach an Amazon EFS file system to the instance and copy data from S3

Answer

Provision an Amazon EBS io2 volume and copy data from S3 using AWS DataSync

Answer

Use instance store volumes to cache the data from S3

Question 15

A company is using Amazon Kinesis Data Firehose to deliver streaming data to an S3 bucket. The data is JSON and must be transformed into Parquet format before delivery. Which approach should the data engineer use?

Accepted Answer

Configure Kinesis Data Firehose to convert the record format to Parquet using a schema from AWS Glue Data Catalog. Kinesis Data Firehose natively supports record format conversion from JSON to Parquet (and ORC) using a schema stored in the AWS Glue Data Catalog. You enable this in the Firehose stream configuration, and Firehose handles the conversion and delivery to S3 without custom code. This is the lowest-effort, fully managed approach.

Answer

Send the data to Amazon Kinesis Data Analytics to convert to Parquet

Answer

Use an AWS Lambda function to transform JSON to Parquet and write to S3

Answer

Use an AWS Glue ETL job to read from Firehose and write Parquet to S3