MLS-C01 Data Engineering Practice Test 23 — 15 Questions

Question 1

A data engineering team needs to orchestrate a complex workflow that involves multiple AWS Glue jobs, Lambda functions, and S3 operations. The workflow must run on a schedule and allow monitoring of each step. Which AWS service should they use?

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

Accepted Answer

AWS Step Functions. AWS Step Functions is a serverless orchestration service that lets you coordinate multiple AWS services, including AWS Glue jobs, Lambda functions, and S3 operations, into a state machine workflow. It provides built-in scheduling via Amazon EventBridge (formerly CloudWatch Events) and offers visual monitoring, logging, and error handling for each step, making it the ideal choice for complex, multi-step workflows that require observability.

Answer

Amazon Simple Workflow Service (SWF)

Answer

AWS Data Pipeline

Answer

Amazon CloudWatch Events

Question 2

A company wants to centralize logging from multiple AWS accounts and on-premises servers. The logs must be stored cost-effectively and be searchable. Which TWO services should be used? (Choose TWO.)

Accepted Answer

Amazon Athena. Amazon S3 (option E) is correct because it provides durable, low-cost object storage ideal for centralizing log data from multiple AWS accounts and on-premises servers, and it serves as the underlying data store that Athena queries in place. Amazon Athena (option C) is correct because it is a serverless query service that runs standard SQL directly against data in S3, making the centralized logs searchable without loading them into a database or managing infrastructure. Together, S3 plus Athena deliver the required cost-effective storage and searchability. Amazon CloudWatch Logs (A) is designed for log ingestion, monitoring, and metric filters rather than cheap long-term centralized storage and ad hoc SQL search across accounts. Amazon Redshift (B) is a data warehouse that requires loading data and is more expensive than querying S3 directly. Amazon Kinesis Data Streams (D) is for real-time streaming ingestion, not cost-effective storage or search.

Answer

Amazon CloudWatch Logs

Answer

Amazon Redshift

Answer

Amazon Kinesis Data Streams

Question 3

A data engineer runs the above CLI command and sees that the bucket contains many small Parquet files (1 MB each) under the prefix. When querying this data with Athena, the query performance is poor and costs are high. Which approach would MOST improve performance and reduce cost?

Accepted Answer

Consolidate the small files into fewer, larger Parquet files. C is correct because consolidating many small Parquet files into fewer, larger files (e.g., 128–256 MB each) reduces the overhead of Amazon Athena's file listing and metadata operations, and improves compression and predicate pushdown efficiency. Parquet is a columnar format optimized for analytics, so keeping it while reducing file count directly addresses the root cause of poor performance and high cost.

Answer

Convert the files to JSON format

Answer

Convert the files to CSV format

Answer

Add more partitions by including hour in the prefix

Question 4

A data engineer is designing a data lake on Amazon S3. The data is collected from IoT devices and is highly variable in volume. The engineer needs to ensure that the data is ingested reliably and can be processed in near real-time. Which AWS service should be used to ingest the data into the data lake?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is the correct choice because it is a fully managed service designed to reliably load streaming data into data lakes on Amazon S3 with near-real-time latency (typically 60 seconds). It automatically handles scaling to accommodate highly variable IoT data volumes, provides built-in data transformation and compression, and requires no manual shard management or consumer code, making it ideal for ingestion into S3-based data lakes.

Answer

AWS Glue

Answer

Amazon Kinesis Data Streams

Answer

Amazon Simple Queue Service (SQS)

Question 5

A data engineer needs to transform a large dataset stored in Amazon S3 using Apache Spark. The engineer wants to minimize costs and avoid managing infrastructure. Which AWS service should be used?

Accepted Answer

AWS Glue. AWS Glue is the optimal choice because it provides a serverless Apache Spark environment, fully managed, that allows the engineer to run Spark transformations without provisioning or managing clusters. This meets both requirements: using Apache Spark as specified, and minimizing costs and infrastructure management through its pay‑as‑you‑go, serverless model. Amazon Athena is a SQL query service and does not execute Apache Spark code. Amazon EMR provides Spark but typically requires cluster management (unless using EMR Serverless, which is not as straightforward as Glue for serverless Spark). Amazon SageMaker is focused on machine learning, not general ETL transformations.

Answer

Amazon Athena

Answer

Amazon SageMaker

Answer

Amazon EMR

Question 6

A machine learning team is preparing a large dataset for training. The dataset consists of 10,000 CSV files, each about 100 MB, stored in Amazon S3. The team wants to transform the data using AWS Glue ETL jobs. The transformation involves filtering rows, adding new columns, and joining with a small reference table (100 KB). The team is concerned about job performance and cost. They currently have a Glue job with 10 DPU (Data Processing Units) and it takes about 2 hours to complete. The team wants to reduce the runtime and cost. Which approach should they take?

Accepted Answer

Convert the CSV files to Parquet format and partition the data by a column.. Converting the CSV files to Parquet format and partitioning the data by a column significantly reduces the amount of data scanned and processed by AWS Glue. Parquet is a columnar storage format that allows Glue to read only the necessary columns, and partitioning enables predicate pushdown to skip irrelevant partitions. This directly reduces I/O and compute requirements, leading to faster job runtime and lower cost without increasing DPU count.

Answer

Use Amazon Athena to transform the data.

Answer

Increase the number of DPUs to 100.

Answer

Use Amazon EMR with Spot Instances instead of AWS Glue.

Question 7

A data engineer needs to continuously ingest streaming data from thousands of IoT devices and store the raw data in Amazon S3 for archival processing. The data volume varies significantly throughout the day, and the solution must be serverless, scalable, and cost-effective. Which AWS service should be used to capture and buffer the streaming data before writing to S3?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is the correct choice because it is a fully managed, serverless service designed to reliably capture, buffer, and automatically load streaming data into Amazon S3 without requiring any custom code or infrastructure management. It handles variable data volumes by scaling automatically and provides built-in buffering (up to 128 MB or 900 seconds) before writing to S3, making it cost-effective for archival storage.

Answer

Amazon Kinesis Data Streams

Answer

AWS Glue

Answer

Amazon Simple Queue Service (SQS)

Question 8

A company is running a machine learning training job on Amazon SageMaker that reads training data from an S3 bucket. The job fails intermittently with an S3 throttling error. The data is partitioned across thousands of small files (average 100 KB). Which strategy is MOST effective to resolve the throttling issue?

Accepted Answer

Combine the small files into larger files (e.g., 100 MB) using a preprocessing step. S3 throttling errors (HTTP 503) occur when many small files cause a high request rate per prefix. By combining thousands of 100 KB files into fewer 100 MB files, you drastically reduce the number of GET requests, staying within S3's 5,500 GET requests per second per prefix limit. This preprocessing step directly addresses the root cause of the throttling without changing the training infrastructure.

Answer

Use Amazon Athena to query the data and output results to a new S3 location

Answer

Enable S3 Transfer Acceleration on the bucket

Answer

Increase the number of SageMaker training instances to distribute the load

Question 9

A data scientist wants to explore a large dataset stored in Amazon S3 using SQL queries without moving the data. The dataset is in CSV format and is updated daily with new partitions. Which AWS service should be used to directly query the data in S3?

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 supports CSV format and can automatically discover new partitions when used with Hive-style partitioning and the MSCK REPAIR TABLE command or by enabling partition projection. This makes it the ideal choice for directly querying a large, daily-updated CSV dataset stored in S3.

Answer

Amazon Redshift Spectrum

Answer

Amazon EMR

Answer

AWS Glue

Question 10

A company is building a data pipeline that ingests data from multiple sources into a centralized data lake on Amazon S3. The data must be transformed before it is available for analysis. The pipeline should be event-driven, automatically triggering transformation jobs when new data arrives. Which combination of AWS services should be used?

Accepted Answer

Amazon S3 event notifications to invoke AWS Lambda, which triggers an AWS Glue job. Amazon S3 event notifications can be configured to invoke an AWS Lambda function when new objects are created in an S3 bucket. The Lambda function can then trigger an AWS Glue job to perform the necessary data transformations. This creates an event-driven, serverless pipeline that automatically processes data as it arrives, meeting the requirements for a centralized data lake on S3.

Answer

Amazon Kinesis Data Analytics for transformation

Answer

Amazon EMR with automatic scaling

Answer

AWS Step Functions to orchestrate the pipeline

Question 11

A company wants to perform real-time analytics on streaming data from clickstreams. The data needs to be ingested, processed, and made available for querying within seconds. Which AWS service should be used for the processing step?

Accepted Answer

Amazon Kinesis Data Analytics. Amazon Kinesis Data Analytics is the correct choice because it enables real-time processing and analysis of streaming data using SQL or Apache Flink. It can ingest data from Kinesis Data Streams or Kinesis Data Firehose, process it with sub-second latency, and output results to destinations like Kinesis Data Streams or Firehose for further querying, meeting the requirement for analytics within seconds.

Answer

AWS Glue

Answer

Amazon Redshift

Answer

Amazon Athena

Question 12

A company is using AWS Glue to catalog metadata from various data sources. The crawler is configured to run daily. However, the catalog is not reflecting new partitions added to an S3 bucket during the day. What is the MOST likely cause?

Accepted Answer

The crawler is not scheduled frequently enough to capture changes. The crawler is configured to run daily, but new partitions are being added to the S3 bucket throughout the day. Since the crawler only runs once per day, it will not detect and catalog those new partitions until its next scheduled run. To capture changes more frequently, the crawler schedule should be increased or an event-driven trigger (e.g., using Amazon S3 Events and AWS Lambda) should be implemented.

Answer

The S3 bucket has insufficient permissions for the Glue crawler

Answer

The table schema has changed and the crawler does not update it

Answer

The data format is not supported by AWS Glue

Question 13

A data engineer is designing a data pipeline that ingests data from a relational database into a data lake on Amazon S3. The data must be incrementally loaded daily. Which TWO AWS services can be used together to achieve this?

Accepted Answer

AWS Glue. AWS Glue [CORRECT] is the right choice because it provides serverless ETL with crawlers that populate the AWS Glue Data Catalog and jobs that can read only new/changed rows (for example via JDBC bookmarks) and write partitioned Parquet files to Amazon S3, which directly supports incremental daily loads into a data lake. AWS Database Migration Service (DMS) [CORRECT] is also correct because it can perform ongoing (CDC) or scheduled full-load-plus-incremental replication from a relational database to Amazon S3, capturing changes so only new or modified records are delivered daily. Together, DMS can handle change data capture from the source database while Glue transforms and catalogs the landed data in S3. Amazon Kinesis Data Streams is a streaming service for real-time event ingestion, not a relational-database incremental extract tool, so it does not fit this daily batch scenario. Amazon Athena is a serverless query engine over S3 and does not perform ingestion or incremental extraction. Amazon Redshift is a data warehouse, not a mechanism for incrementally loading a relational database into an S3 data lake.

Answer

Amazon Kinesis Data Streams

Answer

Amazon Athena

Answer

Amazon Redshift

Question 14

A company is using AWS Glue ETL jobs to transform data. The jobs are failing due to insufficient memory. The data processing involves complex joins and aggregations. Which THREE actions can improve job performance and reduce memory usage?

Accepted Answer

Filter and project data early in the transformation to reduce data volume. Option A is correct because filtering rows and projecting only the needed columns early in the Glue ETL script reduces the volume of data read, shuffled, and held in memory during complex joins and aggregations, directly lowering memory pressure. Option C is correct because repartitioning and bucketing organize data by join keys, which minimizes shuffle size and skew, reducing the memory required for shuffle operations in Spark-based Glue jobs. Option D is correct because increasing the number of DPUs (workers) adds more executors and memory capacity, allowing the job to distribute partitions across more nodes and avoid out-of-memory failures on complex transformations. Option B is incorrect because decreasing DPUs reduces available memory and parallelism, worsening the insufficient-memory condition. Option E is incorrect because a single-node cluster concentrates all processing and shuffle on one node, eliminating distributed parallelism and increasing the risk of memory exhaustion.

Answer

Decrease the number of DPUs allocated to the job

Answer

Use a single node cluster to avoid shuffle overhead

Question 15

A data engineer is designing a data pipeline that transforms raw JSON files (each 50-200 KB) in Amazon S3 into Parquet format using AWS Glue. The pipeline must minimize data processing costs and handle a high volume of small files (millions per day). The engineer configures a Glue ETL job with Spark, but the job is slow and expensive due to overhead of reading many small files. Which optimization should the engineer implement to reduce cost and improve performance?

Accepted Answer

Use S3 object grouping or batch operations to combine small files before Glue processing.. The primary performance bottleneck with many small files in S3 is the overhead of listing, opening, and reading each file individually in Spark. By grouping or batching small files into larger objects (e.g., using S3 Batch Operations or a pre-processing step), you reduce the number of input splits and task launches, which dramatically lowers the cost and runtime of the Glue ETL job. This directly addresses the root cause of the inefficiency rather than merely scaling resources.

Answer

Increase the worker type to G.2X for more memory per worker.

Answer

Increase the number of DPUs allocated to the Glue job.

Answer

Change the output format from Parquet to CSV to reduce compression overhead.