MLS-C01 Data Engineering Practice Test 20 — 15 Questions

Question 1

An S3 event notification is configured to trigger a Lambda function when new objects are created. The Lambda function processes the event JSON shown. Which field should the function use to read the new object from S3?

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

Accepted Answer

s3.bucket.arn and s3.object.key. The Lambda function needs the bucket name and object key to retrieve the new object from S3. The `s3.bucket.arn` provides the bucket identifier, and `s3.object.key` provides the object path. Together, they allow the function to call `GetObject` on the S3 API.

Answer

s3.s3SchemaVersion

Answer

awsRegion

Answer

eventName

Question 2

A company is using AWS Glue ETL jobs to process data stored in Amazon S3. The jobs currently run sequentially and take too long. The data engineer wants to reduce job duration without rewriting the code. Which action is most effective?

Accepted Answer

Increase the number of DPUs (Data Processing Units) for the job. Increasing the number of DPUs (Data Processing Units) for the AWS Glue ETL job directly allocates more distributed computing resources, enabling parallel execution of the job's stages. This reduces the overall runtime without requiring any code changes, as Glue automatically distributes the workload across the additional DPUs.

Answer

Change the underlying EC2 instance type to a compute-optimized instance

Answer

Convert the data from CSV to Parquet format

Answer

Enable job bookmarks to skip already processed data

Question 3

A company wants to analyze historical data stored in Amazon S3 using Amazon Athena. The data is in CSV format and is partitioned by date. Which action will provide the best query performance and cost optimization?

Accepted Answer

Convert the data to Parquet format and use the existing partition structure. Converting CSV to Parquet while preserving the existing date partitions combines the two biggest Athena cost/performance levers: columnar storage (Parquet) and partition pruning. Parquet lets Athena read only the columns needed and benefits from compression and predicate pushdown, while the existing year/month/day partitions let Athena skip irrelevant S3 prefixes. Together they deliver the best query performance and lowest bytes-scanned cost.

Answer

Use AWS Glue to compress the CSV files with gzip

Answer

Create an S3 event notification to trigger a Lambda function that warms up Athena

Answer

Keep CSV format but ensure partitions are in the format year=YYYY/month=MM/day=DD

Question 4

A company runs a nightly AWS Glue ETL job that processes data from an Amazon Redshift cluster and writes to Amazon S3. The job fails intermittently with 'ERROR: cannot execute INSERT in a read-only transaction'. What is the most likely cause?

Accepted Answer

The Redshift cluster is in a read-only state due to a failover or maintenance. The error 'cannot execute INSERT in a read-only transaction' is a Redshift-side error, not a client-side permission or driver issue. When a Redshift cluster is in read-only mode — which happens during a failover to a new leader node, during certain maintenance windows, or when the cluster is being restored — all write transactions are rejected with exactly this message. The Glue job's INSERT statements therefore fail until the cluster returns to a writable state.

Answer

The IAM role used by Glue does not have permissions to insert into Redshift

Answer

The JDBC driver version is incompatible with the Redshift cluster

Answer

The Glue job's connection pool is exhausted

Question 5

A company uses Amazon Redshift for data warehousing. The data engineering team notices that query performance has degraded over time. Which THREE actions should the team take to improve performance? (Choose THREE.)

Accepted Answer

Define appropriate sort keys on large tables. Sort keys in Amazon Redshift define the order in which data is stored on disk within each node. By defining appropriate sort keys on large tables, the query engine can use zone maps to skip entire blocks of data that do not match the query's filter predicates, dramatically reducing the amount of data scanned and improving query performance.

Answer

Increase the number of nodes in the Redshift cluster

Answer

Delete old data that is no longer needed

Question 6

A data engineer is building a data pipeline that ingests streaming data from IoT devices. The data must be processed in near real-time and stored in Amazon S3 for further analysis. Which AWS service should be used to capture and process the streaming data before storing it in S3?

Accepted Answer

Use Amazon Kinesis Data Streams to capture the data and Amazon Kinesis Data Firehose to deliver it to S3.. Amazon Kinesis Data Streams is designed for real-time data ingestion and can capture streaming data from IoT devices with low latency. Amazon Kinesis Data Firehose then reliably loads that streaming data into Amazon S3, handling buffering, compression, and partitioning automatically. This combination provides the near real-time processing and durable storage required for the pipeline.

Answer

Use Amazon S3 with S3 Event Notifications to trigger AWS Lambda for processing.

Answer

Use AWS Glue to perform ETL on the streaming data.

Answer

Use Amazon Simple Queue Service (SQS) to buffer the data and then process it with AWS Lambda.

Question 7

A machine learning team needs to preprocess large volumes of clickstream data stored in Amazon S3 before training a model. The preprocessing includes data cleaning, feature engineering, and normalization. The team wants to use a serverless solution that minimizes operational overhead. Which combination of services should the team use?

Accepted Answer

AWS Glue ETL jobs reading from and writing to S3.. AWS Glue ETL jobs are a serverless solution that automatically provisions and scales the underlying compute resources, making them ideal for preprocessing large volumes of clickstream data stored in S3. Glue can read directly from S3, perform data cleaning, feature engineering, and normalization using PySpark or Scala, and write the transformed data back to S3, all without managing any infrastructure. This minimizes operational overhead while handling the required preprocessing tasks at scale.

Answer

Amazon SageMaker Notebooks with custom Python scripts.

Answer

Amazon EMR with Spark clusters.

Answer

Amazon Athena with SQL queries.

Question 8

A data scientist wants to query a dataset stored in Amazon S3 using standard SQL without provisioning any servers. The dataset is in CSV format and is updated daily. Which AWS service should be used?

Accepted Answer

Amazon Athena. Amazon Athena is a serverless interactive query service that allows you to query data directly from Amazon S3 using standard SQL without provisioning any servers. It is ideal for querying CSV datasets that are updated daily because it supports schema-on-read, meaning you can define the table schema at query time without loading or transforming the data beforehand.

Answer

Amazon Redshift

Answer

Amazon RDS

Answer

Amazon DynamoDB

Question 9

A company is using Amazon S3 as a data lake. The data engineering team needs to catalog the schema of the data and make it available for querying with Amazon Athena. Which AWS Glue component should be used?

Accepted Answer

AWS Glue Crawlers. AWS Glue Crawlers automatically scan data in Amazon S3, infer the schema, and populate the AWS Glue Data Catalog with metadata tables. This makes the data immediately available for querying with Amazon Athena without manual schema definition.

Answer

AWS Glue Studio

Answer

AWS Glue ETL jobs

Answer

AWS Glue DataBrew

Question 10

A data engineer needs to transfer 50 TB of historical data from an on-premises Hadoop cluster to Amazon S3. The on-premises network has a 100 Mbps connection to AWS. The transfer must be completed within one week. Which approach should the engineer use?

Accepted Answer

Use AWS Snowball Edge device to physically transfer the data.. The on-premises network has a 100 Mbps connection, which yields a theoretical maximum transfer of about 1.08 TB per day (100 Mbps * 86400 seconds / 8 bits per byte / 1024^4 bytes per TB). To transfer 50 TB within 7 days, the required throughput is approximately 7.14 TB per day, far exceeding the available bandwidth. AWS Snowball Edge provides a physical shipping method that bypasses network constraints entirely, making it the only viable option for this volume and timeline.

Answer

Use Amazon S3 Transfer Acceleration.

Answer

Use AWS DataSync to transfer the data over the network.

Answer

Use multiple concurrent AWS CLI copy commands over VPN.

Question 11

A company is using Amazon Redshift for data warehousing. The data engineering team notices that queries are slow and the system is frequently writing to disk due to insufficient memory. Which type of workload management (WLM) configuration change would help reduce disk writes?

Accepted Answer

Increase the memory percentage allocated to the WLM queue.. When queries spill to disk in Amazon Redshift, it indicates that the memory allocated to the WLM queue is insufficient for the workload. Increasing the memory percentage for the queue allows more queries to be processed in memory, reducing the need to write intermediate results to disk and improving query performance.

Answer

Increase the number of query concurrency slots.

Answer

Enable query monitoring rules to abort queries that spill to disk.

Answer

Enable short query acceleration (SQA).

Question 12

A data engineer needs to analyze large CSV files stored in Amazon S3 using SQL queries. The data is not frequently accessed, and cost is a primary concern. Which AWS service should be used to query the data directly in S3 without moving it?

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 is ideal for this use case because it charges only for the data scanned per query, making it cost-effective for infrequently accessed large datasets. Athena integrates with AWS Glue Data Catalog for schema management and supports common formats like CSV, JSON, Parquet, and ORC.

Answer

Amazon EMR

Answer

Amazon Redshift Spectrum

Answer

AWS Glue

Question 13

A data engineer is tasked with building a system to process a continuous stream of IoT sensor data. The data must be processed in near real-time, and the results must be stored in Amazon S3 partitioned by hour. Which AWS service is the most cost-effective and simplest to implement?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is a fully managed service that ingests streaming data and automatically delivers it to destinations like Amazon S3, with built-in support for dynamic partitioning by keys such as hour. It requires no infrastructure management, scales automatically, and you pay only for the data volume processed, making it the most cost-effective and simplest solution for near real-time ingestion with hourly S3 partitioning. Firehose also handles buffering, compression, and encryption, reducing operational overhead.

Answer

Amazon Simple Queue Service (SQS) with AWS Lambda

Answer

Amazon Kinesis Data Streams with Amazon EC2 consumers

Answer

AWS Database Migration Service (DMS) for continuous replication

Question 14

A data scientist needs to run a one-time SQL query on a large dataset in Amazon S3. The dataset is stored in Parquet format and is about 500 GB. The query requires complex aggregations and joins. Which AWS service should be used to minimize cost and setup time?

Accepted Answer

Amazon Athena. Amazon Athena is the correct choice because it is a serverless query service that allows you to run SQL directly on data stored in S3 without provisioning any infrastructure. For a one-time query on 500 GB of Parquet data, Athena minimizes cost (pay-per-query, no idle cluster costs) and setup time (no cluster creation or data loading). Its ability to handle complex aggregations and joins on columnar formats like Parquet makes it ideal for this ad-hoc use case.

Answer

Amazon Redshift

Answer

Amazon RDS for MySQL

Answer

Amazon EMR with Spark SQL

Question 15

A company is building a data lake on Amazon S3. Raw data is ingested from multiple sources in different formats (CSV, JSON, Parquet). The data must be cataloged and made queryable using Amazon Athena. The data schema may evolve over time. Which approach minimizes manual effort and supports schema evolution?

Accepted Answer

Use AWS Glue Crawlers to automatically create and update the Glue Data Catalog. AWS Glue Crawlers automatically infer schema from data in S3, create and update the Glue Data Catalog tables, and handle schema evolution by detecting changes such as new columns or partitions. This minimizes manual effort because the crawler runs on a schedule or trigger, and the catalog is natively integrated with Athena for querying without any additional setup.

Answer

Use Athena only, without a catalog, by directly querying files

Answer

Use Amazon EMR to process data and write to a Hive metastore

Answer

Manually create tables in Athena using DDL statements