MLS-C01 Data Engineering Practice Test 12 — 15 Questions

Question 1

A data scientist needs to perform exploratory data analysis on a 100 GB CSV file stored in Amazon S3. The data is not sensitive. The scientist wants to use SQL queries to filter and aggregate the data without setting up a server or moving the data. Which service should be used?

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

Accepted Answer

Amazon Athena. Amazon Athena is the correct choice because it is a serverless, interactive query service that allows you to run standard SQL directly on data stored in Amazon S3 without any infrastructure setup. For a 100 GB CSV file, Athena can handle the query workload efficiently by automatically scaling, and it charges only for the data scanned per query, making it ideal for ad-hoc exploratory analysis without moving or transforming the data.

Answer

AWS Glue

Answer

Amazon EMR

Answer

Amazon Redshift Spectrum

Question 2

A company is using Amazon SageMaker to train a model on a dataset that is updated daily. The data is stored in an S3 bucket. The training pipeline uses AWS Step Functions to orchestrate data preprocessing and model training. The preprocessing step uses a SageMaker Processing job that reads data from S3, cleans it, and writes the output back to S3. The team notices that the training step often fails due to insufficient disk space on the processing instance. Which change should the team make to resolve this issue without increasing cost?

Accepted Answer

Configure the processing job to use local instance store (SSD) for scratch space.. The issue is insufficient disk space on the processing instance. Option D resolves this by configuring the processing job to use the local instance store (SSD) for scratch space, which provides high-throughput temporary storage without incurring additional cost, as the instance store is included with the instance. This allows the preprocessing step to handle larger intermediate data without requiring a larger or more expensive instance.

Answer

Enable automatic scaling for the processing job.

Answer

Use AWS Batch instead of SageMaker Processing.

Answer

Use a larger instance type with more memory.

Question 3

A team is building a data pipeline that ingests data from an Amazon S3 bucket, transforms it using AWS Glue, and loads it into Amazon Redshift for analysis. The Glue job runs on a schedule every hour. The team has noticed that the job takes longer than expected and sometimes fails due to memory issues. The data volume is variable, with occasional spikes. Which solution should the team implement to optimize the pipeline?

Accepted Answer

Enable job bookmarks to process only new data and use a G.2X worker type for more memory.. Enabling job bookmarks allows the Glue job to process only new or changed data since the last run, reducing the data volume per execution. Using the G.2X worker type provides additional memory (e.g., 16 GB per DPU vs. 4 GB for G.1X), which helps prevent out-of-memory failures during data spikes. Together, these optimizations address both the variable data volume and memory constraints without requiring a complete pipeline redesign.

Answer

Decrease the number of workers to reduce memory contention.

Answer

Increase the schedule frequency to run the job more often with smaller data increments.

Answer

Replace AWS Glue with Amazon EMR using Spark.

Question 4

Which TWO AWS services can be used to transform data in a streaming fashion without using a persistent cluster? (Choose 2.)

Accepted Answer

AWS Lambda. (Lambda) and Option D (Kinesis Data Analytics) are serverless streaming transformation services. Option A (Glue) is serverless but not low-latency streaming. Option B (EMR) requires a persistent cluster. Option E (Data Pipeline) is for batch.

Answer

AWS Glue

Answer

Amazon EMR

Answer

AWS Data Pipeline

Question 5

Which THREE factors should a data engineer consider when choosing between Amazon S3 and Amazon Redshift for storing large datasets used for machine learning? (Choose 3.)

Accepted Answer

Query performance and latency requirements. When choosing between Amazon S3 and Amazon Redshift for ML data storage, key considerations include: (A) Query performance and latency: Redshift offers low-latency SQL querying on structured data, while S3 provides higher latency for direct access, making performance needs critical. (C) Cost of storage vs. compute: S3 decouples storage and compute, allowing independent scaling; Redshift combines them, affecting cost. (D) Data format and compression: S3 supports any format, but Redshift works best with columnar formats like Parquet. (B) Encryption at rest and (E) Data retention policies are available in both, so they are not differentiating factors.

Answer

Encryption at rest capabilities

Answer

Data retention policies

Question 6

A company is using Amazon Kinesis Data Streams with a Lambda consumer. The Lambda function writes results to an S3 bucket. The team wants to ensure that each record is processed exactly once and in order. Which TWO configurations should the team implement? (Choose 2.)

Accepted Answer

Set the parallelization factor to 1. Setting the parallelization factor to 1 ensures that each shard of the Kinesis Data Stream is processed by only one Lambda instance at a time, preserving the order of records within that shard. Option E is correct because enabling S3 bucket versioning helps track duplicates: if the same record is written multiple times, S3 versioning creates multiple versions, allowing the system to detect and handle duplicates, contributing to exactly-once processing semantics.

Answer

Set the batch size to 1

Answer

Increase the Lambda function's reserved concurrency

Answer

Configure a dead-letter queue for failed records

Question 7

Refer to the exhibit. An ML engineer applies this bucket policy to an S3 bucket. The SageMaker execution role MySageMakerRole is used to train a model. The training data is located in s3://my-bucket/data/. The SageMaker training job fails with an access error. What is the most likely cause?

Accepted Answer

The Deny statement on s3:ListBucket prevents the role from listing objects in the bucket.. The Deny statement on s3:ListBucket explicitly denies the s3:ListBucket action for the MySageMakerRole. SageMaker training jobs require the ability to list objects in the bucket to discover and read training data, even if the GetObject permission is granted. The explicit Deny overrides any Allow, causing the access error.

Answer

The policy allows GetObject only from the data/ prefix, but the training job uses a different prefix.

Answer

The role is not in the same AWS account as the bucket.

Answer

The bucket has default encryption enabled, causing a conflict.

Question 8

Refer to the exhibit. An ML engineer runs the above CLI command to inspect files in an S3 bucket. The training data consists of 200 CSV files, each 1 GB. The engineer plans to use Amazon SageMaker to train a model using this data. What should the engineer do to optimize training performance?

Accepted Answer

Convert the CSV files to Parquet format and use Pipe input mode.. Converting CSV files to Parquet format and using Pipe input mode significantly improves training performance. Parquet is a columnar storage format that reduces I/O by reading only relevant columns, and it is compressed. Pipe input mode streams data directly from S3 to the training algorithm without downloading to EBS, reducing startup time and disk usage. Option A is incorrect because simply increasing the number of instances does not address the inefficiency of reading CSV files; it may help parallelization but not per-instance throughput. Option B is incorrect because Amazon Athena is a query service, not a data transformation tool for SageMaker; converting to CSV with headers does not improve performance. Option C is incorrect because using File input mode copies all files to the training instance's EBS volume, which is slow for 200 GB of data and does not leverage streaming benefits.

Answer

Increase the number of training instances to process files in parallel.

Answer

Use Amazon Athena to transform the data into CSV format with headers.

Answer

Use the File input mode and copy all files to the training instance's EBS volume.

Question 9

A company runs a real-time recommendation system that uses Amazon SageMaker endpoints for inference. The system ingests user activity data from a mobile app via Amazon API Gateway and AWS Lambda, which writes events to an Amazon Kinesis Data Stream. A second Lambda function consumes the stream, calls a SageMaker endpoint to generate recommendations, and stores the results in Amazon DynamoDB. The system has been working well, but recently the team noticed an increase in latency from the time a user action occurs to when the recommendation is stored. The SageMaker endpoint shows increased invocation latency but no throttling. CloudWatch metrics show that the Kinesis stream's IteratorAgeMilliseconds is increasing, indicating the consumer is falling behind. The Lambda consumer's duration is within limits, but the number of invocations is lower than expected. The team suspects the issue is with the event source mapping. Which course of action should the team take to reduce the latency?

Accepted Answer

Increase the batch size in the event source mapping to process more records per invocation.. The increasing IteratorAgeMilliseconds indicates the consumer is falling behind. The Lambda consumer's duration is within limits but the number of invocations is lower than expected, suggesting that the event source mapping is not invoking the function often enough. Increasing the batch size allows each invocation to process more records per invocation, effectively increasing throughput without requiring more invocations. This directly addresses the lag. Option B (increase shards) could help if the consumer had sufficient concurrency, but the root cause is low invocations per shard, not lack of shards. Option C (decrease reserved concurrency) would worsen the problem. Option D (Firehose) does not solve the consumer lag and changes the architecture unnecessarily.

Answer

Increase the number of shards in the Kinesis data stream to increase parallelism.

Answer

Decrease the Lambda function's reserved concurrency to force it to scale down.

Answer

Replace the Lambda consumer with an Amazon Kinesis Data Firehose delivery stream.

Question 10

A data engineering team needs to process streaming data from thousands of IoT devices. The data must be ingested with low latency and processed in near real-time to detect anomalies. Which AWS service should they use for ingestion?

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is the correct choice because it is designed for real-time, low-latency ingestion of streaming data from thousands of sources, such as IoT devices. It provides a durable, scalable data stream that can be consumed by multiple applications in near real-time, making it ideal for anomaly detection use cases.

Answer

Amazon Kinesis Data Firehose

Answer

Amazon Kinesis Data Analytics

Answer

Amazon S3

Question 11

A data scientist needs to run ad-hoc SQL queries on a large dataset stored in Amazon S3 (Parquet format, 2 TB). The queries are interactive and require sub-second response times. Which service should they use?

Accepted Answer

Amazon Athena. Amazon Athena is the correct choice because it is a serverless, interactive query service designed for ad-hoc SQL queries on data stored in Amazon S3, with no infrastructure to manage. It natively supports Parquet format and can achieve sub-second response times on 2 TB datasets through columnar projection, predicate pushdown, and data partitioning, making it ideal for this use case.

Answer

Amazon Redshift Spectrum

Answer

Amazon QuickSight

Answer

Amazon EMR with Spark SQL

Question 12

A company is using Amazon Kinesis Data Firehose to load streaming data into Amazon S3. The data is in JSON format, and they want to convert it to Parquet before storage. What should they configure?

Accepted Answer

Enable data format conversion in Firehose and specify a Glue table. Amazon Kinesis Data Firehose supports built-in data format conversion from JSON to Parquet or ORC. By enabling this feature and specifying an AWS Glue table that defines the schema, Firehose automatically converts incoming JSON records to Parquet before delivering them to the S3 destination. This eliminates the need for additional compute resources or post-processing steps.

Answer

Use an AWS Lambda function to transform the data

Answer

Run an AWS Glue ETL job after data is in S3

Answer

Use Kinesis Data Analytics for Apache Flink to convert the format

Question 13

A data engineering team needs to move 10 TB of historical data from an on-premises Hadoop cluster to Amazon S3. The data is currently stored in HDFS. Which service should they use for an efficient transfer?

Accepted Answer

AWS DataSync. AWS DataSync is the correct choice because it is designed to efficiently transfer large volumes of data from on-premises storage systems, including HDFS, to AWS services like Amazon S3. It uses a purpose-built network protocol and parallel multi-threading to optimize transfer speed over the internet or AWS Direct Connect, and it can handle the 10 TB volume without requiring physical appliances or complex streaming setups.

Answer

S3 Transfer Acceleration

Answer

Amazon Kinesis Data Streams

Answer

AWS Snowball Edge

Question 14

An e-commerce company uses Amazon DynamoDB as the primary data store for user sessions. They want to run analytics on historical session data using Amazon Athena. What is the recommended approach to export DynamoDB data to S3 in a format optimized for Athena?

Accepted Answer

Use AWS Glue ETL to read from DynamoDB and write to S3 as Parquet. AWS Glue ETL can read from DynamoDB and write to S3 in Parquet format, which is optimized for Athena due to its columnar storage and compression. Option A (AWS Data Pipeline) can copy data to S3 as CSV, but CSV is less efficient for Athena and Data Pipeline is a legacy service. Option B (Amazon Kinesis Data Firehose) is designed for streaming data, not for exporting existing DynamoDB tables. Option C (DynamoDB Streams with Lambda) writes to S3 as JSON, which is less performant than Parquet for Athena queries and adds operational complexity.

Answer

Use AWS Data Pipeline to copy data to S3 as CSV

Answer

Use Amazon Kinesis Data Firehose to stream data from DynamoDB to S3

Answer

Use DynamoDB Streams with AWS Lambda to write to S3 as JSON

Question 15

A data engineer needs to schedule an AWS Glue ETL job to run every hour. Which service should they use for scheduling?

Accepted Answer

Amazon CloudWatch Events. Amazon CloudWatch Events (now part of Amazon EventBridge) can trigger an AWS Glue ETL job on a schedule using a cron or rate expression. This is the native, serverless scheduling service for running jobs at fixed intervals, such as every hour, without needing to manage any infrastructure.

Answer

AWS Lambda

Answer

Amazon Simple Queue Service (SQS)

Answer

AWS Step Functions