MLS-C01 Data Engineering Practice Test 6 — 15 Questions

Question 1

A data pipeline uses AWS Glue to transform data from Amazon RDS to Amazon S3. The team wants to ensure that only new or updated records are processed in each run, minimizing cost and time. Which AWS Glue feature should be used?

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

Accepted Answer

Enable Glue Job Bookmarks.. Glue Job Bookmarks track processed data and persist state between job runs, enabling incremental processing of new or updated records from a source like Amazon RDS. This minimizes cost and time by avoiding full table scans and reprocessing unchanged data.

Answer

Use Glue triggers to run the job on a schedule.

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Use Glue partition pruning to filter data.

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Use Glue crawlers to detect new data.

Question 2

A company is using Amazon SageMaker to train machine learning models. The training data is stored in Amazon S3, but the data includes personally identifiable information (PII) that must be anonymized before training. What is the most efficient way to anonymize the data?

Accepted Answer

Use an AWS Glue ETL job to read from S3, apply anonymization, and write to another S3 bucket.. AWS Glue ETL jobs are purpose-built for serverless data transformation at scale, making them the most efficient choice for anonymizing PII in S3 before training. Glue can read directly from S3, apply built-in or custom anonymization transforms (e.g., masking, hashing) using PySpark or Scala, and write the cleaned data to a separate S3 bucket without provisioning any infrastructure. This approach decouples the data preparation from SageMaker, avoids unnecessary compute costs during training, and scales automatically with data volume.

Answer

Use Amazon Athena to query the data and apply anonymization functions.

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Use Amazon Redshift Spectrum to query and anonymize data in S3.

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Use a SageMaker Processing job to read from S3 and apply anonymization.

Question 3

A data engineer needs to move 50 TB of historical data from an on-premises Hadoop cluster to Amazon S3. The network bandwidth is limited to 100 Mbps. Which AWS service should be used to transfer the data most efficiently?

Accepted Answer

AWS Snowball Edge device to physically ship the data.. Given 50 TB of data and a 100 Mbps network link, the theoretical minimum transfer time over the network is over 46 days (50 TB * 8 / 100 Mbps ≈ 4,000,000 seconds ≈ 46.3 days), not accounting for protocol overhead, retransmissions, or contention. AWS Snowball Edge is a physical appliance that bypasses the network bottleneck entirely, allowing you to copy data locally and ship it to AWS, making it the most efficient option for this volume over a constrained link.

Answer

Amazon S3 Transfer Acceleration to speed up the transfer.

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AWS Direct Connect to establish a dedicated network connection.

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AWS Site-to-Site VPN to connect and copy data.

Question 4

A team is building a data lake on Amazon S3 and using AWS Glue to catalog data. They notice that Glue crawlers are taking too long to update the catalog for a large dataset with millions of small files. Which approach will MOST improve crawler performance?

Accepted Answer

Consolidate the small files into larger files (e.g., 100 MB each).. AWS Glue crawlers incur significant overhead when processing millions of small files because each file requires a separate read, schema inference, and metadata write operation. Consolidating small files into larger files (e.g., 100 MB each) reduces the total number of objects that the crawler must scan, dramatically decreasing the time spent on file-level operations and improving overall throughput.

Answer

Increase the frequency of the crawler runs.

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Partition the data by date in S3.

Answer

Use a custom classifier to parse the data.

Question 5

A company uses Amazon Kinesis Data Firehose to deliver streaming data to Amazon S3. The data is in JSON format, and the company wants to convert it to Parquet for efficient querying. Which configuration should be used?

Accepted Answer

Enable data transformation in Firehose using an AWS Lambda function to convert JSON to Parquet, and set the output format to Parquet.. Amazon Kinesis Data Firehose supports data transformation via AWS Lambda, allowing you to convert incoming JSON records to Parquet format before delivery to S3. By enabling a Lambda function to perform the conversion and setting the output format to Parquet, Firehose handles the transformation in-stream, ensuring the data lands in S3 already in the optimized columnar format for efficient querying with services like Amazon Athena or Amazon Redshift Spectrum.

Answer

Use an AWS Glue job to convert the JSON files in S3 to Parquet after delivery.

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Use Amazon Kinesis Data Analytics to convert the stream to Parquet before sending to Firehose.

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Configure Firehose to deliver data directly to Amazon Redshift, which automatically converts to Parquet.

Question 6

A data engineer needs to run a one-time ETL job to transform 500 GB of data from Amazon RDS to Amazon S3. The job should be cost-effective and require minimal infrastructure management. Which AWS service should be used?

Accepted Answer

AWS Glue. AWS Glue is the correct choice because it is a fully managed, serverless ETL service designed for one-time or scheduled data transformation jobs. It automatically provisions and scales the underlying Spark environment, requires no infrastructure management, and charges only for the resources consumed during job execution, making it highly cost-effective for a 500 GB ETL workload from RDS to S3.

Answer

Amazon EMR

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Amazon Athena

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AWS Data Pipeline

Question 7

A company uses Amazon DynamoDB as the primary data store for a real-time application. The data science team wants to analyze the data using Amazon Athena. What is the most efficient way to make the DynamoDB data available for Athena queries?

Accepted Answer

Use DynamoDB Streams to invoke an AWS Lambda function that writes data to Amazon S3 in Parquet format. Then query the data in S3 using Athena.. DynamoDB Streams captures real-time changes, and an AWS Lambda function can efficiently write these changes to Amazon S3 in Parquet format, which is optimized for columnar storage and Athena queries. This approach minimizes the overhead of scheduled batch jobs and provides near-real-time data availability for analytics.

Answer

Use AWS Glue to extract data from DynamoDB and load into S3 on a schedule.

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Use Amazon Redshift Spectrum to query DynamoDB directly.

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Use Amazon EMR to read directly from DynamoDB and run Hive queries.

Question 8

A company is designing a data pipeline that ingests streaming data from social media feeds. The data must be processed in real-time to detect trending topics, and results must be stored in Amazon DynamoDB for low-latency access. Which services should the company use? (Choose TWO.)

Accepted Answer

Amazon Kinesis Data Analytics. Amazon Kinesis Data Analytics (D) is correct because it provides real-time SQL-based processing of streaming data, enabling the detection of trending topics from social media feeds without requiring custom code. It directly analyzes data from Kinesis Data Streams and can output results to DynamoDB via a Lambda function or Firehose, meeting the low-latency storage requirement.

Answer

Amazon Kinesis Data Firehose

Answer

AWS Lambda

Answer

Amazon Simple Queue Service (SQS)

Question 9

A data engineer needs to transform and move 2 TB of data from an Amazon RDS for PostgreSQL instance to Amazon S3 daily. The transformation includes filtering, joining with data in S3, and aggregating. Which AWS services can be used together to accomplish this with minimal operational overhead? (Choose THREE.)

Accepted Answer

Amazon S3. Amazon S3 is correct because it serves as the target storage location for the transformed data. The daily 2 TB output from the ETL pipeline must be stored durably and cost-effectively, and S3 provides the ideal object storage layer for this purpose, especially when combined with AWS Glue for the transformation logic.

Answer

Amazon EMR

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Amazon Redshift

Question 10

A company wants to build a data lake on Amazon S3. The data lake should support both batch and real-time data ingestion. Which AWS services should be used for data ingestion? (Choose TWO.)

Accepted Answer

AWS Glue. AWS Glue is correct because it provides a managed ETL service that can handle batch data ingestion into a data lake on Amazon S3. It can be scheduled for periodic batch loads or triggered by events, making it suitable for batch ingestion workflows. Amazon Kinesis Data Firehose is correct because it is a fully managed service for loading streaming data into S3 in near real-time, supporting real-time ingestion with automatic buffering and compression.

Answer

Amazon Redshift

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Amazon Athena

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Amazon SQS

Question 11

A data engineer wants to stream clickstream data from a web application to Amazon S3 for near-real-time analytics. Which AWS service should be used to ingest and buffer the data before landing in S3?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is the correct choice because it is a fully managed service designed to ingest streaming data, buffer it, and reliably deliver it to destinations like Amazon S3 with near-real-time latency (typically 60 seconds). It handles automatic scaling, data transformation, and compression, making it ideal for clickstream data landing directly into S3 for analytics.

Answer

Amazon AppFlow

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Amazon Kinesis Data Streams

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AWS Glue

Question 12

A machine learning team needs to process a large dataset stored in Amazon S3 using Apache Spark. They want to minimize cost and avoid managing infrastructure. Which AWS service should they use?

Accepted Answer

AWS Glue. AWS Glue is a fully managed, serverless Spark environment that eliminates infrastructure management. It directly meets the requirement to process large datasets in S3 with Apache Spark while minimizing cost, as you only pay for resources consumed during job execution. Glue also integrates natively with S3 and the broader AWS ecosystem, making it the optimal choice for this use case.

Answer

Amazon Athena

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Amazon EMR

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Amazon SageMaker

Question 13

A company uses AWS Glue to run ETL jobs on a daily schedule. The jobs are failing intermittently with 'OutOfMemory' errors. The data volume has grown 5x over the past month. Which is the MOST cost-effective fix?

Accepted Answer

Increase the number of DPUs for the Glue job. The 'OutOfMemory' errors in AWS Glue are caused by insufficient compute resources (DPUs) to process the 5x increased data volume. Increasing the number of DPUs allocates more memory and processing capacity to the job, directly addressing the memory shortage without changing the data or architecture. This is the most cost-effective fix because it scales resources incrementally rather than switching to a more expensive service like EMR.

Answer

Increase the number of partitions in the source S3 data

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Reduce the data volume by sampling

Answer

Switch from AWS Glue to Amazon EMR

Question 14

A data scientist needs to query a dataset stored as Parquet files in Amazon S3 using standard SQL without managing any infrastructure. Which service should they use?

Accepted Answer

Amazon Athena. Amazon Athena is a serverless interactive query service that allows you to query data stored in Amazon S3 using standard SQL without any infrastructure to manage. It directly reads Parquet files from S3 and executes queries using Presto under the hood, making it the ideal choice for this use case.

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Amazon QuickSight

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AWS Glue

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Amazon Redshift

Question 15

A team wants to build a data pipeline that processes incoming JSON files from an S3 bucket and loads them into a Redshift table. The pipeline must handle schema evolution and data validation. Which combination of services would be MOST appropriate?

Accepted Answer

Amazon S3 + AWS Glue + Amazon Redshift. AWS Glue provides built-in schema discovery and evolution capabilities via its crawlers and the Data Catalog, which automatically detect and adapt to changes in JSON schemas. Combined with Glue ETL jobs for data validation and transformation, it seamlessly loads processed data into Amazon Redshift, making it the most appropriate choice for handling schema evolution and validation in this pipeline.

Answer

Amazon S3 + Amazon SQS + Amazon Redshift

Answer

Amazon S3 + AWS Data Pipeline + Amazon Redshift

Answer

Amazon S3 + AWS Lambda + Amazon Redshift