MLS-C01 Data Engineering Practice Test 21 — 15 Questions

Question 1

Which TWO AWS services can be used to transform data in transit before storing it in Amazon S3? (Choose TWO.)

Accepted Answer

AWS Glue. AWS Glue is correct because it provides a serverless data integration service that can transform data in transit using its built-in transformation jobs (e.g., PySpark scripts) before writing the results to Amazon S3. This allows you to clean, enrich, or reshape streaming or batch data as it moves through the pipeline.

Answer

Amazon Redshift Spectrum

Answer

AWS Data Pipeline

Answer

Amazon Athena

Question 2

A company is designing a data pipeline to analyze customer behavior. The pipeline must handle real-time streaming data and batch data. The data must be stored in a data lake on Amazon S3 and also made available for interactive queries. Which THREE services should be combined to build this pipeline? (Choose THREE.)

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is correct because it is the primary AWS service for ingesting and processing real-time streaming data at scale. It can capture and store streaming data from sources like clickstreams or IoT devices, making it available for downstream consumers such as AWS Glue or Amazon Athena for analysis.

Answer

Amazon Redshift

Answer

Amazon DynamoDB Streams

Question 3

A data engineering team needs to ingest streaming data from thousands of IoT devices into a data lake on Amazon S3 for near-real-time analytics. The data must be partitioned by device ID and timestamp, and the team must minimize data loss during ingestion failures. Which solution is MOST appropriate?

Accepted Answer

Use Amazon Kinesis Data Firehose to write directly to S3 with dynamic partitioning.. Amazon Kinesis Data Firehose with dynamic partitioning is the most appropriate solution because it natively supports partitioning incoming data by device ID and timestamp before writing to S3, and it provides built-in data buffering and retry logic to minimize data loss during ingestion failures. Unlike a Lambda-based approach, Firehose handles large-scale streaming ingestion without requiring custom code for partitioning or error handling, making it ideal for near-real-time analytics on IoT data.

Answer

Use Amazon Kinesis Data Streams with a Lambda function that writes to S3.

Answer

Use Amazon S3 Transfer Acceleration with direct uploads from devices.

Answer

Use AWS Lambda to receive data via API Gateway and write to S3.

Question 4

A data scientist needs to query a 2 TB dataset stored in Amazon S3 using Amazon Athena. The data is in CSV format and is used for exploratory analysis. Queries are currently slow and expensive. Which action will improve query performance and reduce cost?

Accepted Answer

Convert the data to Parquet format and partition by a commonly filtered column.. Converting CSV data to Parquet (a columnar storage format) significantly reduces the amount of data scanned by Athena, as only the columns needed for the query are read. Partitioning by a commonly filtered column (e.g., date or region) further limits the data scanned to only relevant partitions, directly reducing both query cost (Athena charges per TB scanned) and query execution time.

Answer

Convert the data to JSON format to improve compression.

Answer

Increase the number of workers in the Athena query engine.

Answer

Create a composite index on the data using Athena's index feature.

Question 5

A company wants to use Amazon SageMaker to train a model on a dataset stored in Amazon S3. The dataset is 100 GB and consists of millions of small JSON files. What should the data engineering team do to optimize training performance?

Accepted Answer

Combine the small JSON files into larger Parquet files using a Spark job on Amazon EMR.. Combining millions of small JSON files into larger Parquet files using a Spark job on Amazon EMR is correct because it reduces the overhead of S3 LIST and GET requests during training. Parquet's columnar format also improves compression and allows SageMaker to read only the necessary columns, significantly accelerating I/O-bound training workloads.

Answer

Copy the data to an Amazon EBS volume attached to the training instance.

Answer

Use Amazon Athena to convert the data into a single CSV file.

Answer

Use S3 Select to filter data before training.

Question 6

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 1 Gbps connection to AWS. The transfer must be completed within 10 days. What is the MOST efficient approach?

Accepted Answer

Use AWS Snowball Edge to physically ship the data.. Transferring 50 TB over a 1 Gbps connection would take approximately 5.6 days under ideal conditions (50 TB × 8 bits/byte / 1 Gbps / 86400 seconds/day ≈ 4.63 days), but real-world factors like network congestion, TCP overhead, and protocol inefficiencies typically reduce throughput to 50-70% of line rate, pushing the transfer beyond the 10-day window. AWS Snowball Edge provides a physical shipping alternative that bypasses network limitations entirely, making it the most efficient and reliable method for this volume and deadline.

Answer

Use Amazon S3 Transfer Acceleration to speed up the upload.

Answer

Use AWS DataSync over the existing network connection.

Answer

Set up a VPN connection and use multi-part upload directly to S3.

Question 7

A data engineering team is designing a data pipeline to process streaming data from social media feeds. The data must be deduplicated, enriched with customer information from a relational database, and stored in Amazon S3 in Parquet format. Which AWS services should the team use to build this pipeline? (Select TWO.)

Accepted Answer

AWS Glue. Amazon Kinesis Data Streams (E) is correct because it ingests high-throughput streaming data from social media feeds in real time, allowing custom consumers to deduplicate records before further processing. AWS Glue (A) is correct because it can run Apache Spark ETL jobs that enrich the streamed data with customer information from a relational database via JDBC and write the output to Amazon S3 in Parquet format. Together, Kinesis Data Streams handles real-time ingestion and Glue handles the transformation, enrichment, and Parquet conversion. Amazon Kinesis Data Firehose (B) is not selected because it primarily delivers streaming data to destinations without the flexible deduplication and relational enrichment logic required here. Amazon Athena (C) is a query service for data in S3, not a pipeline component for streaming ingestion or ETL. Amazon SageMaker (D) is a machine learning platform and does not provide the streaming ingestion or ETL capabilities needed for this pipeline.

Answer

Amazon Kinesis Data Firehose

Answer

Amazon Athena

Answer

Amazon SageMaker

Question 8

A company uses AWS Glue Data Catalog to manage metadata for its data lake on Amazon S3. The data lake contains terabytes of data in CSV format. The data engineering team wants to improve query performance in Amazon Athena and reduce costs. Which actions should the team take? (Select THREE.)

Accepted Answer

Compress the data using Snappy or GZIP.. Option B is correct because compressing CSV data with Snappy or GZIP reduces the number of bytes Athena must read from Amazon S3, and since Athena charges based on data scanned, this directly lowers query cost and improves performance. Option C is correct because partitioning the data by commonly filtered columns (e.g., date or region) lets Athena use partition pruning to scan only the relevant S3 prefixes instead of the entire dataset, dramatically reducing both latency and bytes scanned. Option D is correct because converting CSV to Parquet, a columnar format, allows Athena to read only the columns referenced in a query and benefits from better compression and predicate pushdown, which improves performance and reduces cost. Option A is not correct because creating views only simplifies query authoring and does not by itself reduce the amount of data scanned or improve performance. Option E is not correct because JSON is a row-based text format that is typically less efficient than Parquet for analytical queries and does not provide the columnar and compression benefits needed here.

Answer

Create views in Athena to simplify queries.

Answer

Convert the data to JSON format.

Question 9

A company captures streaming data from IoT devices using Amazon Kinesis Data Streams. The data is consumed by a custom application that processes records in near real-time. Recently, the application has been falling behind, and the stream is showing increased 'iterator age' metrics in CloudWatch. Which action is MOST likely to reduce the iterator age?

Accepted Answer

Increase the number of shards in the stream. The 'iterator age' metric in Amazon Kinesis Data Streams measures the time between the oldest unread record in a shard and the current time. An increasing iterator age indicates that consumers are reading data slower than it is being produced. Increasing the number of shards increases the stream's total read capacity, allowing the custom application to process records in parallel and reduce the backlog.

Answer

Increase the data retention period of the stream

Answer

Decrease the number of shards in the stream

Answer

Reduce the data retention period of the stream

Question 10

An ML engineer is using Amazon SageMaker to train a model on a dataset that contains personal identifiable information (PII). The data must be encrypted at rest and in transit. The company uses AWS KMS for key management. How should the engineer configure the SageMaker training job to meet these encryption requirements?

Accepted Answer

Specify a KMS key for the training job's VolumeKmsKeyId and enable inter-container traffic encryption. It addresses both encryption at rest and in transit for the SageMaker training job. Specifying a KMS key via VolumeKmsKeyId encrypts the ML storage volume (EBS) used by the training instances at rest, while enabling inter-container traffic encryption ensures data exchanged between distributed training containers is encrypted in transit using TLS. This combination meets the PII encryption requirements using AWS KMS.

Answer

Enable S3 Server-Side Encryption (SSE-S3) on the input data bucket

Answer

Use a custom Docker image with built-in encryption and disable inter-container traffic encryption for performance

Answer

Use a VPC with an S3 VPC Endpoint and enable SSL for the endpoint

Question 11

A team is building a data pipeline using Amazon Kinesis Data Firehose to deliver real-time clickstream data to an Amazon S3 bucket. The data must be partitioned by year, month, day, and hour. Which configuration should the team use to achieve this?

Accepted Answer

Enable dynamic partitioning in Firehose and configure the partition keys as YYYY/MM/dd/HH. Amazon Kinesis Data Firehose supports dynamic partitioning, which allows you to automatically partition incoming data in S3 based on keys like YYYY/MM/dd/HH. By enabling this feature and configuring the partition keys to match the desired year, month, day, and hour format, Firehose will write data directly into the corresponding S3 prefix structure without requiring additional processing.

Answer

Configure an S3 lifecycle rule to move data into partition folders after delivery

Answer

Use an AWS Lambda function to write data to S3 with the desired partition structure

Answer

Use Amazon Athena partition projection to dynamically create partitions

Question 12

A data engineer is designing a data pipeline that uses Amazon Kinesis Data Streams to ingest sensor data. The data must be processed in real-time, and the results must be stored in Amazon DynamoDB. Which TWO AWS services can be used together to achieve this? (Choose TWO.)

Accepted Answer

Amazon Kinesis Data Analytics. Amazon Kinesis Data Analytics (option B) is correct because it can consume records directly from a Kinesis Data Stream, run continuous SQL or Apache Flink queries over the streaming data in real time, and then write the transformed results to a downstream sink such as DynamoDB. AWS Lambda (option E) is correct because it can be configured as an event source mapping for a Kinesis Data Stream, invoking the function in real time as records arrive so the function can process each batch and write the results to DynamoDB. Together, Kinesis Data Analytics and Lambda provide the real-time processing and DynamoDB storage required by the pipeline. Amazon Athena (A) is an interactive query service for data in S3, not a stream processor. AWS Glue (C) is a serverless ETL service primarily used for batch and catalog operations, not real-time Kinesis stream processing. Amazon S3 (D) is object storage and does not process streaming data in real time.

Answer

Amazon Athena

Answer

AWS Glue

Answer

Amazon S3

Question 13

An AWS Glue job is failing with an error that it cannot access an S3 bucket. The IAM role attached to the Glue job is shown in the exhibit. What is the MOST likely cause of the failure?

Accepted Answer

The S3 bucket has a bucket policy that denies access to this role. Even if the IAM role has S3 permissions, an S3 bucket policy that explicitly denies access to that role will override any allow. AWS evaluates all policies (identity-based and resource-based) and a deny in any policy results in a final deny decision. The error indicates the Glue job cannot access the bucket, which is consistent with a bucket-level deny.

Answer

The role lacks S3 permissions

Answer

The role does not have permission to call S3 APIs

Answer

The trust policy does not allow Glue to assume the role

Question 14

A data engineer needs to process streaming data from an IoT fleet and store the results in Amazon S3 for analysis. The solution must be serverless and handle data that arrives at irregular intervals. Which AWS service should be used to ingest the data?

Accepted Answer

AWS IoT Core. AWS IoT Core is the correct choice because it is a fully managed, serverless service designed specifically to ingest data from IoT devices at scale, handling irregular and high-frequency message arrivals via MQTT, HTTP, or LoRaWAN protocols. It can directly route data to Amazon S3 using IoT Rules, making it ideal for this streaming IoT fleet scenario without requiring any server management.

Answer

Amazon S3

Answer

Amazon Simple Queue Service (SQS)

Answer

Amazon Kinesis Data Streams

Question 15

A machine learning team is building a real-time inference pipeline using Amazon SageMaker. The input data is located in an S3 bucket, and the team needs to transform the data before inference using a custom Python script. The transformation should run on a serverless infrastructure and must be triggered automatically when new data arrives in S3. Which combination of services should the team use?

Accepted Answer

Use AWS Lambda functions triggered by S3 events to run the transformation, then invoke a SageMaker endpoint.. AWS Lambda functions can be triggered directly by S3 events (e.g., ObjectCreated) to run a custom Python transformation script on the incoming data, and then invoke a SageMaker endpoint for real-time inference. This combination meets the serverless infrastructure requirement and provides automatic, event-driven processing without managing any servers.

Answer

Use AWS Glue jobs triggered by S3 events.

Answer

Use Amazon SageMaker Processing jobs triggered by S3 events.

Answer

Use Amazon Kinesis Data Firehose to transform data and deliver to SageMaker.