MLS-C01 Data Engineering Practice Test 2 — 15 Questions

Question 1

Match each AWS service to its primary purpose in a machine learning pipeline.

Accepted Answer

Amazon SageMaker: Fully managed service for building, training, and deploying machine learning models.. The correct matches associate each service with its primary ML pipeline role. SageMaker is for end-to-end ML, Lambda for serverless inference, S3 for data/model storage, Glue for data preparation. Distractors confuse these roles.

Answer

Amazon SageMaker: Serverless compute service for inference.

Answer

AWS Lambda: Scalable object storage for models.

Question 2

Match each data format to its typical use in AWS ML.

Accepted Answer

CSV: Tabular data for built-in algorithms. Common data formats in AWS ML: CSV for tabular data, JSON for semi-structured inference I/O, Parquet for columnar analytics, and TFRecord for TensorFlow training. Distractors confuse CSV with Parquet and JSON with binary formats.

Answer

CSV: Columnar storage for analytics

Answer

JSON: Binary format for efficient I/O

Question 3

A company uses AWS Glue ETL jobs to transform CSV data from an S3 bucket into Parquet. The jobs often fail with memory errors when processing large datasets. They want to minimize cost and improve reliability. What should they do?

Accepted Answer

Use G.1X or G.2X worker types and increase the number of DPUs per worker.. The G.1X and G.2X worker types provide more memory per worker (16 GB and 32 GB, respectively) compared to the standard G.0X worker (4 GB). By using these worker types and increasing the number of DPUs per worker, you allocate more memory to each task, reducing out-of-memory errors when processing large datasets. This approach also optimizes cost by using fewer, more powerful workers instead of many underpowered ones, improving reliability without unnecessary scaling.

Answer

Use Amazon Athena with CTAS queries to convert the data to Parquet.

Answer

Switch to S3 Batch Operations with AWS Lambda to process the files individually.

Answer

Increase the number of workers in the Glue job configuration.

Question 4

A machine learning team needs to create a training dataset by joining two large datasets (10 TB and 5 TB) stored in S3. The join key is 'user_id'. They want to minimize data movement and cost. Which approach should they use?

Accepted Answer

Use AWS Glue ETL to read both datasets, join them using Spark DataFrames, and write the result to S3.. AWS Glue ETL provides a serverless Spark environment that can read both datasets from S3, perform the join in a distributed manner, and write the result back to S3. This approach minimizes data movement because Glue reads directly from S3 and writes back to S3 without requiring intermediate storage or cluster management. It is cost-effective due to per-second billing and is optimized for large-scale ETL workloads like joining 10 TB and 5 TB datasets. Amazon Athena would scan 15 TB of data, incurring high costs and potential performance issues, while EMR and Redshift require provisioning and managing clusters, leading to higher operational overhead and costs.

Answer

Launch an Amazon EMR cluster with Spark, read data from S3, perform the join, and write results back to S3.

Answer

Use Amazon Athena to run a SQL query joining the two datasets directly on S3.

Answer

Load both datasets into Amazon Redshift using COPY commands, then perform the join in Redshift.

Question 5

A company uses an Amazon SageMaker notebook to train a model using data from an S3 bucket. The IAM role attached to the notebook has the following policy. What is the MOST specific change needed to allow the notebook to read from the bucket 'ml-data-123'?

Accepted Answer

Remove the Deny statement from the IAM policy.. The existing IAM policy includes an explicit Deny statement that blocks all s3:GetObject access to the bucket 'ml-data-123'. In IAM, an explicit Deny overrides any Allow, so even if other policies grant read access, the Deny prevents it. Removing the Deny statement is the most specific change because it eliminates the blocking condition without requiring additional permissions or resources.

Answer

Add an Allow statement for 's3:GetObject' on 'ml-data-123' to the IAM policy.

Answer

Create an S3 access point and update the IAM policy to use the access point ARN.

Answer

Add a bucket policy on 'ml-data-123' that grants access to the notebook's IAM role.

Question 6

A company uses Kinesis Data Streams to ingest real-time sensor data. The data is consumed by a Lambda function that writes to DynamoDB. During peak hours, the Lambda function throws ProvisionedThroughputExceededException. The team wants to decouple the write operation and improve resilience. What should they do?

Accepted Answer

Use Kinesis Firehose as a consumer of the stream, with a Lambda transformation to write to DynamoDB, and enable error handling.. Kinesis Firehose can consume data from a Kinesis Data Stream and invoke a Lambda function for transformation before delivering to destinations like DynamoDB. By using Firehose with error handling, the team decouples the write operation from the Lambda consumer, allowing Firehose to buffer data and retry failed writes, which improves resilience against ProvisionedThroughputExceededException without losing data.

Answer

Increase the Lambda function's reserved concurrency and provision more DynamoDB write capacity.

Answer

Place the Lambda function's output into an Amazon SQS queue, and have a second Lambda function write to DynamoDB.

Answer

Use Kinesis Data Analytics to process the stream and write results directly to DynamoDB.

Question 7

A data engineer needs to design a data pipeline that ingests CSV files from an SFTP server daily, transforms them, and loads them into Amazon Redshift. The files are typically 2-3 GB. Which combination of AWS services is MOST appropriate?

Accepted Answer

Use AWS Transfer Family to automate SFTP file retrieval to S3, then use Redshift COPY to load data.. The most appropriate because AWS Transfer Family provides a fully managed, serverless solution for automating SFTP file retrieval directly into Amazon S3. Once the CSV files are in S3, the Redshift COPY command can efficiently load the 2-3 GB files using parallel processing, which is far more performant and cost-effective than alternatives like Lambda or Glue for large file sizes.

Answer

Use AWS Glue ETL with a JDBC connection to the SFTP server to read files directly.

Answer

Use AWS Lambda to download the files from SFTP, transform them in memory, and write to Redshift using the Data API.

Answer

Use Amazon Kinesis Data Firehose with an HTTP endpoint source to receive files from SFTP.

Question 8

A team stores raw data in S3 and uses a Glue Data Catalog for metadata. They want to allow data scientists to query the data with Amazon Athena using their existing IAM roles. What is the MINIMUM set of permissions required?

Accepted Answer

Grant the IAM role permissions for Athena actions, Glue Data Catalog actions, and S3 read access.. Athena requires read access to S3 for querying data, permissions to the Glue Data Catalog for schema/metadata resolution, and Athena-specific actions to run queries. Write access to S3 is not required for querying, making B the minimum set.

Answer

Grant the IAM role permissions for Athena, Glue, and S3 (read and write).

Answer

Grant the IAM role permissions for Athena and Amazon Redshift Spectrum.

Answer

Grant the IAM role permissions for Athena and Amazon Kinesis.

Question 9

A company uses Amazon Kinesis Data Streams to ingest clickstream data. They need to archive raw data to S3 every hour and also enable real-time processing with sub-second latency. Which TWO actions should they take? (Choose two.)

Accepted Answer

Use Kinesis Data Analytics to write output to S3.. Option A is correct because Kinesis Data Analytics (managed Apache Flink) can consume the Kinesis Data Stream and write its output to Amazon S3, satisfying the hourly archival requirement while also supporting real-time stream processing. Option B is correct because an AWS Lambda function can be registered as a consumer of the Kinesis Data Stream via event source mapping, enabling real-time processing with sub-second latency as records arrive. Option C is wrong because S3 events cannot trigger a Lambda function to read from a Kinesis stream; S3 events only fire on S3 object operations, not on stream ingestion. Option D is wrong because Kinesis Data Firehose with a 3600-second buffer interval only handles the hourly S3 archival and does not provide sub-second real-time processing. Option E is wrong because the Kinesis Agent writes data into a Kinesis stream, not directly to S3, and does not address real-time processing.

Answer

Use S3 events to trigger a Lambda function that reads from the stream.

Answer

Create a Kinesis Data Firehose delivery stream with S3 as destination and set a buffer interval of 3600 seconds.

Answer

Install the Kinesis Agent on an EC2 instance to write data to S3.

Question 10

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.

Answer

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

Answer

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

Question 11

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.

Answer

Use Amazon Redshift Spectrum to query DynamoDB directly.

Answer

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

Question 12

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 Streams (E) is correct because it is the service designed to continuously capture and store streaming data from sources like social media feeds, allowing multiple consumers to read and process the data in real time with low latency. Amazon Kinesis Data Analytics (D) is correct because it runs SQL or Apache Flink applications directly on streaming data from Kinesis Data Streams, enabling real-time detection of trending topics before writing results to DynamoDB. Together, Kinesis Data Streams ingests the feed and Kinesis Data Analytics performs the real-time stream processing, which matches the requirement for real-time trend detection with low-latency DynamoDB storage. Amazon Kinesis Data Firehose (A) is not correct because it is primarily a delivery service for loading streaming data into destinations such as S3, Redshift, or Elasticsearch, and it does not perform real-time analytics on the stream. AWS Lambda (B) can process events, but by itself it is not the designated streaming ingestion and real-time analytics service for this scenario, and Amazon SQS (C) is a message queue for decoupling components, not a real-time stream analytics platform.

Answer

Amazon Kinesis Data Firehose

Answer

AWS Lambda

Answer

Amazon Simple Queue Service (SQS)

Question 13

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. AWS Glue (E) is the right choice because it is a fully managed, serverless ETL service that can read from Amazon RDS for PostgreSQL via JDBC, perform filtering, joins, and aggregations using Spark or Python shell jobs, and write results to Amazon S3 with minimal operational overhead. Amazon S3 (C) is correct because it is the required destination for the transformed 2 TB of daily data and also serves as the source for the join data, making it an essential part of the pipeline. AWS Glue Data Catalog (D) is correct because it stores the table and schema metadata for the RDS PostgreSQL source, the S3 data used in joins, and the S3 target, enabling AWS Glue jobs and crawlers to discover and query that data consistently. Amazon EMR (A) is not marked correct because, although it can perform large-scale Spark transformations, it requires more cluster provisioning and management than the serverless AWS Glue approach and therefore does not meet the minimal operational overhead requirement. Amazon Redshift (B) is not marked correct because it is a data warehouse service and would add unnecessary provisioning, loading, and administration overhead for this daily ETL scenario rather than serving as the primary transformation and movement mechanism.

Answer

Amazon EMR

Answer

Amazon Redshift

Question 14

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

Answer

Reduce the data volume by sampling

Answer

Switch from AWS Glue to Amazon EMR

Question 15

A company uses Amazon Kinesis Data Analytics for real-time anomaly detection on a stream of IoT sensor data. The application is experiencing high latency. The data volume has doubled. Which action would MOST effectively reduce latency?

Accepted Answer

Increase the Parallelism setting of the Kinesis Data Analytics application. Increasing the Parallelism setting of the Kinesis Data Analytics application directly allocates more processing resources (e.g., more Kinesis Processing Units or KPUs) to handle the doubled data volume. This allows the application to process records concurrently, reducing the per-record processing time and overall latency. Parallelism is the primary scaling mechanism for Kinesis Data Analytics to match throughput increases.

Answer

Change the record format from JSON to Avro

Answer

Decrease the retention period of the source stream

Answer

Increase the number of shards in the source Kinesis stream