MLS-C01 Data Engineering Practice Test 5 — 15 Questions

Question 1

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. Option A (Glue) is the ETL service, Option C (S3) is the target, and Option D (Glue Data Catalog) manages metadata. Option B (EMR) adds overhead, Option E (Redshift) is a warehouse, not needed.

Answer

Amazon EMR

Answer

Amazon Redshift

Question 2

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. Option B (Kinesis Firehose) is for real-time ingestion, Option D (AWS Glue) is for batch ETL. Option A (Athena) is for querying, Option C (Redshift) is a warehouse, Option E (SQS) is not for ingestion into S3.

Answer

Amazon Redshift

Answer

Amazon Athena

Answer

Amazon SQS

Question 3

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 service for loading streaming data into S3 without custom code. Option A is wrong because Kinesis Data Streams requires a consumer to write to S3. Option C is wrong because AppFlow is for SaaS integrations. Option D is wrong because Glue is for ETL, not real-time streaming.

Answer

Amazon AppFlow

Answer

Amazon Kinesis Data Streams

Answer

AWS Glue

Question 4

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 serverless Spark environment that can process data in S3 without provisioning clusters. EMR requires cluster management. Athena is SQL-only. SageMaker is for training models, not general-purpose Spark.

Answer

Amazon Athena

Answer

Amazon EMR

Answer

Amazon SageMaker

Question 5

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. Increasing the number of DPUs (data processing units) in the Glue job configuration provides more memory and parallelism cost-effectively. Switching to EMR is more expensive and complex. Reducing data is not a solution. Increasing S3 partitions does not affect memory.

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 6

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 serverless and allows SQL queries directly on S3 data. Redshift requires a cluster. Glue is for ETL. QuickSight is for visualization.

Answer

Amazon QuickSight

Answer

AWS Glue

Answer

Amazon Redshift

Question 7

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 can crawl the S3 data to infer schema, perform ETL transformations, and load into Redshift. SQS is not needed. Lambda is event-driven but lacks built-in schema evolution. Data Pipeline is older and less flexible.

Answer

Amazon S3 + Amazon SQS + Amazon Redshift

Answer

Amazon S3 + AWS Data Pipeline + Amazon Redshift

Answer

Amazon S3 + AWS Lambda + Amazon Redshift

Question 8

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 (number of KPUs) in Kinesis Data Analytics allows processing more data in parallel, reducing latency. Changing record format may help but not as much as scaling. Reducing retention is not relevant. Using Lambda adds overhead.

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

Question 9

A data engineer needs to transfer 50 TB of historical data from an on-premises HDFS cluster to Amazon S3. The company has a 1 Gbps internet connection. Which service would complete the transfer in the shortest time?

Accepted Answer

AWS Snowball. AWS Snowball is a physical device that can transfer large amounts of data faster than internet due to bandwidth limitations. DataSync is for network transfers. Direct Connect helps but still limited by bandwidth. S3 Transfer Acceleration speeds up internet transfers but cannot match physical shipment for 50 TB.

Answer

Amazon S3 Transfer Acceleration

Answer

AWS Direct Connect

Answer

AWS DataSync

Question 10

A company is building a data lake on Amazon S3. They need to enforce encryption at rest for all objects. Which combination of actions will achieve this? (Assume the bucket is versioned.)

Accepted Answer

Enable S3 default encryption and set a bucket policy to deny PutObject without encryption headers. Enabling default encryption on the bucket ensures all new objects are encrypted. Adding a bucket policy that denies PutObject without encryption headers prevents unencrypted uploads. The other options do not enforce encryption for all objects.

Answer

Use AWS KMS with automatic key rotation

Answer

Enable S3 default encryption only

Answer

Enable S3 Block Public Access

Question 11

A company uses Amazon EMR with Spark to process data daily. The job reads from S3 and writes to S3. Recently, the job started failing with 'S3AccessDenied' errors. The IAM role used by EMR has not changed. What is the MOST likely cause?

Accepted Answer

The S3 bucket policy was updated to deny access to the EMR role. S3 bucket policies can be changed independently and may block access. IAM policies are not the only factor; bucket policies also apply. The role itself hasn't changed, but the bucket policy might have been updated to deny access. Other options are less likely because the role hasn't changed and the errors are access-related.

Answer

The EMR cluster's security group blocks outbound traffic

Answer

The S3 bucket was deleted and recreated

Answer

The EMR service role was rotated

Question 12

A company is designing a data pipeline to ingest data from multiple sources into an Amazon S3 data lake. The data must be encrypted at rest and in transit. Which TWO actions should be taken to meet these requirements?

Accepted Answer

Enable Server-Side Encryption on the S3 bucket. Server-Side Encryption (SSE-S3 or KMS) encrypts data at rest in S3. Using HTTPS (SSL/TLS) for all API calls encrypts data in transit. Client-side encryption is an alternative but not the standard AWS approach. VPC endpoints and CloudFront do not encrypt in transit by themselves.

Answer

Enable S3 Transfer Acceleration

Answer

Use client-side encryption before uploading

Answer

Use S3 VPC Endpoint

Question 13

A data engineering team uses AWS Glue to run ETL jobs. They notice that jobs are taking longer to complete as data volume grows. They want to optimize performance without increasing cost significantly. Which THREE strategies should they consider?

Accepted Answer

Partition the input data in S3. Partitioning the input data allows Glue to process in parallel. Using columnar formats like Parquet reduces I/O. Increasing the number of DPUs (workers) improves parallelism but increases cost; however, it can be cost-effective if job duration decreases significantly. Removing partitions would hurt performance. Using Spark directly is not necessary.

Answer

Remove partitioning from the output

Answer

Use Amazon EMR instead of Glue

Question 14

A company wants to analyze streaming data from IoT devices in near-real-time. They need to store raw data in Amazon S3 and also run SQL queries on the streaming data. Which TWO services should they use?

Accepted Answer

Amazon Kinesis Data Analytics. Amazon Kinesis Data Firehose can deliver streaming data to S3. Amazon Kinesis Data Analytics can run SQL queries on the stream. Kinesis Data Streams is for ingestion but requires custom consumers. Lambda can process streams but not SQL. Glue is for batch ETL.

Answer

AWS Glue

Answer

Amazon Kinesis Data Streams

Answer

AWS Lambda

Question 15

An IAM policy is attached to a group. A user in the group tries to read the object s3://data-lake-bucket/sensitive/file.txt from an IP address 192.168.1.1. What will happen?

Accepted Answer

The request is denied because of the Deny statement. The Deny statement explicitly denies any S3 action on the sensitive prefix when the source IP is not from 10.0.0.0/8. Since the IP 192.168.1.1 is not in that range, the Deny applies. Deny statements override Allow statements. So the user is denied access.

Answer

The request is allowed because the Allow statement grants s3:GetObject

Answer

The request is allowed because the Deny condition does not match

Answer

The request is denied because the policy has no explicit Allow for the sensitive prefix