MLS-C01 Data Engineering Practice Test 13 — 15 Questions

Question 1

A company is using AWS Glue Data Catalog as the metadata store for their data lake. They have multiple AWS accounts and want to share the catalog across accounts. Which feature should they use?

Accepted Answer

AWS Resource Access Manager (RAM). AWS Resource Access Manager (RAM) enables you to share AWS Glue Data Catalog databases and tables across multiple AWS accounts without needing to copy metadata. This allows a centralized catalog to be consumed by different accounts for querying and ETL operations, maintaining a single source of truth for the data lake.

Answer

Amazon Athena Federated Query

Answer

AWS Lake Formation

Answer

Amazon S3 Cross-Region Replication

Question 2

A data engineer is designing a data pipeline that ingests 500 GB of data daily from an on-premises Oracle database to Amazon S3. The pipeline must minimize data loss and support change data capture (CDC). Which combination of services should they use?

Accepted Answer

AWS Database Migration Service (DMS) with ongoing replication. AWS DMS with ongoing replication is the correct choice because it provides continuous change data capture (CDC) from an Oracle source database using Oracle LogMiner or binary reader technology, replicating transactions in near real-time to Amazon S3. This minimizes data loss by capturing incremental changes without requiring batch snapshots, and it supports the 500 GB daily volume efficiently with parallel tasks and task tuning.

Answer

AWS Data Pipeline with SQL query

Answer

Amazon Kinesis Data Streams with a custom Oracle CDC connector

Answer

AWS Glue ETL jobs running on a schedule

Question 3

Which TWO of the following are valid ways to reduce query costs in Amazon Athena? (Choose 2)

Accepted Answer

Partition the data in S3. (partitioning data) and Option D (using columnar formats like Parquet) are correct because both reduce the amount of data scanned by Athena queries, directly lowering costs. Option A (UNLOAD) exports results but does not reduce query costs. Option C (increasing query timeout) does not affect data scanned. Option E (encryption at rest) does not reduce costs.

Answer

Use UNLOAD to export query results to S3

Answer

Increase the query timeout limit

Answer

Enable encryption at rest on S3

Question 4

Which THREE of the following are best practices for optimizing performance of Amazon EMR clusters? (Choose 3)

Accepted Answer

Consolidate small files into larger ones before processing. Consolidating small files into larger ones before processing on Amazon EMR reduces the overhead of the Hadoop Distributed File System (HDFS) metadata operations. Each small file consumes a block of memory in the NameNode, and processing many small files leads to excessive task launches and I/O overhead, degrading performance. Using tools like `s3-dist-cp` to combine files into fewer, larger blocks improves throughput and reduces job execution time.

Answer

Use Spot Instances for task nodes

Answer

Use Spot Instances to reduce costs

Question 5

Which TWO services can be used to transform data in transit within a Kinesis Data Firehose delivery stream? (Choose 2)

Accepted Answer

AWS Lambda. AWS Lambda is correct because it can be invoked as a transformation function within a Kinesis Data Firehose delivery stream. When you enable data transformation, Firehose buffers incoming records and then calls a Lambda function you specify, passing batches of records for processing. The Lambda function can modify, enrich, filter, or reformat the data before Firehose continues delivering it to the destination.

Answer

Amazon Kinesis Data Analytics

Answer

Amazon Athena

Answer

Amazon S3

Question 6

A financial services company uses Amazon Kinesis Data Streams with 50 shards to ingest real-time stock trade data. The data is consumed by a custom Java application running on Amazon EC2 instances. Recently, the application has been experiencing high latency, and CloudWatch metrics show that the average iterator age is increasing. The application uses the Kinesis Client Library (KCL) with DynamoDB for lease tracking. The EC2 instances are in an Auto Scaling group with a minimum of 2 and maximum of 10 instances, and the current CPU utilization is below 50%. The team wants to reduce latency without increasing costs significantly. What should they do?

Accepted Answer

Increase the maximum size of the Auto Scaling group and set a scaling policy based on iterator age. Increasing the number of consumers (EC2 instances) by raising the Auto Scaling group maximum and setting a scaling policy based on iterator age allows more shards to be processed concurrently, reducing the iterator age. Option A is incorrect because the DynamoDB lease table is not the bottleneck; lease operations are lightweight and the current read capacity is sufficient. Option B is incorrect because enhanced fan-out is designed for multiple consumer applications to get dedicated throughput, but here there is a single consumer group; it would not reduce latency for the existing consumer and would add cost. Option C is incorrect because increasing shards would increase the stream's throughput capacity but also cost, whereas the current issue is consumer-side capacity, not stream capacity.

Answer

Increase the provisioned read capacity of the DynamoDB lease table

Answer

Enable enhanced fan-out on the Kinesis stream

Answer

Increase the number of shards in the Kinesis stream

Question 7

A media company ingests video metadata from multiple sources into an Amazon S3 bucket. Each metadata record is a JSON file about 2 KB. They use AWS Glue ETL jobs to process these files and load them into Amazon Redshift for analytics. The jobs currently run hourly and take about 10 minutes to process all new files. However, the company is growing and expects the number of files to increase 100x. The data engineering team wants to minimize processing time and cost. The Glue job currently reads all files from the S3 bucket using a full scan. What should they do to optimize the pipeline?

Accepted Answer

Use S3 event notifications to trigger Glue jobs only for new files. Using S3 event notifications to trigger Glue jobs only for new files eliminates the need to scan all files in the bucket, reducing processing time and cost. Option A consolidating files would reduce the number of small files but does not address the full scan issue and would still require processing all files. Option B converting to Parquet improves performance and reduces scan size, but the job still scans all files unnecessarily. Option C increasing DPUs speeds up processing but increases cost without addressing the root cause of scanning all files.

Answer

Consolidate the small JSON files into larger files using a scheduled job

Answer

Convert the data to Parquet format and partition it

Answer

Increase the number of Glue DPUs to process files faster

Question 8

A retail company uses Amazon Redshift for its data warehouse. The data engineering team runs ETL jobs that load data from multiple sources into Redshift daily. They notice that the load performance is slow and the cluster CPU utilization is high during the ETL window. The team wants to improve load performance without changing the cluster configuration. They currently load data using INSERT statements from a staging table. What should they do?

Accepted Answer

Use the COPY command to load data from S3 in parallel. The COPY command is the most efficient way to load large amounts of data into Amazon Redshift because it uses the cluster's nodes in parallel to read data from Amazon S3, maximizing throughput. Option A (VACUUM and ANALYZE) are maintenance operations that reclaim space and update statistics, but they do not improve load performance. Option C (increasing node count) contradicts the requirement to not change cluster configuration. Option D (compression encoding) can reduce storage and improve scan performance but does not significantly speed up the initial load.

Answer

Run VACUUM and ANALYZE before loading

Answer

Increase the number of nodes in the Redshift cluster

Answer

Apply compression encoding on the staging table

Question 9

A data engineering team needs to ingest streaming data from thousands of IoT devices into Amazon S3 for near-real-time analytics. The data arrives in bursts and must be processed with minimal latency. Which AWS service is most appropriate for the ingestion layer?

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is the most appropriate ingestion layer because it is designed for real-time, low-latency data ingestion from thousands of sources, such as IoT devices. It can handle bursty traffic by scaling shards dynamically and provides sub-second to second-level latency for data to be available for processing, which meets the minimal latency requirement for near-real-time analytics.

Answer

Amazon Kinesis Data Firehose

Answer

Amazon SQS

Answer

Amazon S3

Question 10

A company is building a data pipeline using AWS Glue to transform data from Amazon RDS to Amazon S3. The pipeline runs daily and processes about 500 GB of data. The team notices that the job is taking longer than expected. Which change would MOST improve the job performance?

Accepted Answer

Increase the number of DPUs for the Glue job. Increasing the number of DPUs (Data Processing Units) for the AWS Glue job directly allocates more distributed computing resources, allowing the job to process the 500 GB dataset in parallel across multiple workers. This is the most effective way to reduce runtime for a large-scale ETL job, as Glue's Spark-based execution scales horizontally with DPU count.

Answer

Disable job bookmarking

Answer

Upgrade the RDS instance to a larger class

Answer

Use smaller file sizes in S3 output

Question 11

A data engineer is designing a data lake on Amazon S3 that must support both batch and streaming analytics. The data comes in Parquet format and needs to be queryable by Amazon Athena. Which partitioning strategy will optimize query performance and reduce costs?

Accepted Answer

Partition by date and hour for time-based queries. Partitioning by date and hour is optimal for time-series data in Parquet format queried by Athena because it leverages Hive-style partitioning to prune partitions during query execution, drastically reducing the amount of data scanned. This minimizes Athena's cost (which is based on data scanned) and improves query performance by limiting I/O to only the relevant partitions. Parquet's columnar storage further reduces scan volume when queries select only specific columns, making this combination highly efficient for both batch and streaming ingestion patterns.

Answer

Store data as CSV without partitioning for simplicity

Answer

Partition by device_id for granular access

Answer

Use a single partition for all data to simplify management

Question 12

A company uses AWS Lambda to process events from Amazon S3. The Lambda function transforms the data and writes results to another S3 bucket. Recently, the function has been failing due to timeout errors when processing large files. Which solution should the data engineer implement?

Accepted Answer

Use S3 Batch Operations with a Lambda function to process objects. S3 Batch Operations is designed to handle large-scale object processing by invoking a Lambda function asynchronously for each object, bypassing the synchronous invocation limits of S3 event notifications. This allows processing of large files without hitting Lambda's 15-minute timeout or memory constraints, as each object is processed independently and the operation can scale to billions of objects.

Answer

Increase the Lambda function memory and timeout limit

Answer

Increase the Lambda timeout to 15 minutes

Answer

Use Amazon SQS to queue the events and process them in batches

Question 13

A data engineer needs to transfer 50 TB of historical data from an on-premises Hadoop cluster to Amazon S3. The company has a 1 Gbps internet connection and wants to complete the transfer within 5 days. What is the MOST cost-effective and reliable solution?

Accepted Answer

Use AWS Snowball Edge device to physically ship the data. AWS Snowball Edge is the most cost-effective and reliable solution because transferring 50 TB over a 1 Gbps internet connection would take approximately 5.5 days under ideal conditions (50 TB * 1024 GB/TB * 8 bits/byte / (1 Gbps * 86400 seconds/day) ≈ 4.74 days, but real-world overhead, congestion, and retransmissions push it beyond 5 days). Snowball Edge provides a physical appliance that can be shipped, avoiding network bandwidth limitations entirely, and is designed for large-scale data transfers where internet speeds are insufficient.

Answer

Use S3 multipart upload over the internet

Answer

Set up AWS Direct Connect and transfer over the dedicated line

Answer

Use S3 Transfer Acceleration to speed up the transfer

Question 14

A company uses Amazon EMR to run Spark jobs on a transient cluster that processes data from S3. The jobs are failing with 'OutOfMemory' errors. The data engineer has already increased the executor memory. Which additional configuration change would MOST likely resolve the issue?

Accepted Answer

Increase the number of partitions in the data. The 'OutOfMemory' errors in Spark on EMR typically occur when individual partitions hold too much data for the executor's memory to process. Increasing the number of partitions distributes the data more evenly across available memory, reducing the per-partition size and preventing memory overflow during shuffle or aggregation operations. This directly addresses the root cause of memory pressure, whereas simply increasing executor memory may only delay the failure.

Answer

Use fewer, larger instance types for the core nodes

Answer

Increase the driver memory

Answer

Increase the number of executors

Question 15

A data engineering team needs to orchestrate a complex workflow that involves multiple AWS Glue jobs, Lambda functions, and S3 operations. The workflow must run on a schedule and allow monitoring of each step. Which AWS service should they use?

Accepted Answer

AWS Step Functions. AWS Step Functions is a serverless orchestration service that lets you coordinate multiple AWS services, including AWS Glue jobs, Lambda functions, and S3 operations, into a state machine workflow. It provides built-in scheduling via Amazon EventBridge (formerly CloudWatch Events) and offers visual monitoring, logging, and error handling for each step, making it the ideal choice for complex, multi-step workflows that require observability.

Answer

Amazon Simple Workflow Service (SWF)

Answer

AWS Data Pipeline

Answer

Amazon CloudWatch Events