MLS-C01 Data Engineering Practice Test 6 — 15 Questions

Question 1

A financial services company is building a fraud detection model that requires joining real-time transaction data with a reference dataset of known fraudulent accounts stored in Amazon DynamoDB. The solution must minimize latency and be highly available. The reference dataset is updated frequently (every few minutes). Which architecture should the team use?

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

Accepted Answer

Use Amazon Kinesis Data Analytics to process the stream and join with a DynamoDB table.. Amazon Kinesis Data Analytics (now managed Apache Flink) can directly reference a DynamoDB table as a reference source via the Flink Table API or SQL JOINs, enabling low-latency, stateful stream enrichment without external query overhead. This architecture minimizes latency by performing the join in-memory within the streaming application, and it supports high availability through Kinesis Data Analytics' automatic checkpointing and failover.

Answer

Use Amazon Athena to query the DynamoDB table and join with streaming data.

Answer

Use AWS Glue streaming ETL to read from Kinesis and join with DynamoDB.

Answer

Use Amazon SageMaker to host a model that queries DynamoDB for each inference.

Question 2

A company runs a data lake on Amazon S3 with partitions by year/month/day. A machine learning team needs to read daily data from the last 30 days for model retraining. The data format is Parquet. The team uses Amazon Athena to query the data, but the queries are slow and scanning too much data. The team has already optimized the file sizes and compression. What additional step can reduce the amount of data scanned?

Accepted Answer

Add more partition columns such as hour to reduce the scanned partitions.. Adding more granular partition columns, such as hour, enables more precise partition pruning in Athena. If the query includes a filter on the hour column (e.g., WHERE hour BETWEEN 9 AND 17), Athena will only read the partitions corresponding to those hours, reducing the amount of data scanned. Even if the current query does not filter on hour, this additional partition provides flexibility to reduce scanned data when only specific hours are needed per day. Since the team can modify their query to include an hour filter, this step can significantly reduce the data scanned, especially when combined with existing year/month/day partitions.

Answer

Remove the partition structure and store data as single large files.

Answer

Convert the Parquet files to JSON format for better query performance.

Answer

Use CSV format with Gzip compression.

Question 3

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 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. AWS Lambda (C) is correct because it is a fully serverless, event-driven compute service that can process streaming records (for example, as a consumer of Kinesis or DynamoDB Streams) without provisioning or managing any persistent cluster. Amazon Kinesis Data Analytics (D) is correct because it runs continuously applied SQL or Apache Flink code over streaming data and is a fully managed, serverless service that requires no cluster to operate. AWS Glue (A) is a serverless ETL service, but it is oriented toward batch and job-based data cataloging and transformation rather than continuous streaming transformation. Amazon EMR (B) is not appropriate because it is a managed big-data platform that runs on persistent (or at least provisioned) EC2 clusters, which the question explicitly excludes. AWS Data Pipeline (E) is a managed orchestration service for scheduled, batch-oriented data workflows, not a serverless streaming transformation engine.

Answer

AWS Glue

Answer

Amazon EMR

Answer

AWS Data Pipeline

Question 5

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. Option C is correct because setting the parallelization factor to 1 ensures that only one Lambda invocation processes each shard at a time, preserving the strict ordering of records within a shard as required by Kinesis Data Streams. Option E is correct because enabling S3 bucket versioning allows the team to track and detect duplicate writes by retaining multiple versions of an object, which supports the exactly-once processing goal by making duplicates identifiable and recoverable. Option A is not correct because batch size 1 reduces throughput but does not by itself guarantee exactly-once processing or ordering across retries. Option B is not correct because increasing reserved concurrency can cause multiple concurrent invocations per shard, which breaks ordering guarantees. Option D is not correct because a dead-letter queue handles failed records but does not prevent duplicates or enforce in-order processing.

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 6

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 7

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 Kinesis event source mapping for Lambda uses a batch size that determines how many records are read per invocation. If the consumer is falling behind (IteratorAge increasing) and invocation count is lower than expected, increasing the batch size allows each Lambda invocation to process more records, improving throughput and reducing latency without changing shard count.

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 8

A company is using AWS Glue to run ETL jobs that process data from an Amazon RDS for PostgreSQL database. The jobs are failing with connection timeouts. The security group for the RDS instance allows inbound traffic from the Glue job's security group. What is the most likely cause?

Accepted Answer

The VPC does not have an S3 VPC endpoint. The most likely cause is that the VPC does not have an S3 VPC endpoint. AWS Glue jobs running in a VPC require access to S3 for storing temporary data, scripts, and logs. Without an S3 VPC endpoint, traffic to S3 is routed through a public IP, which fails in private subnets lacking internet connectivity. This failure can cause the Glue job to hang or fail during initialization, and the attempted connection to RDS may time out as a result. The security group inbound rule is correctly configured, so ingress is not the issue. The problem is network egress for S3, not access to RDS itself.

Answer

The security group inbound rule is incorrect

Answer

RDS is in a different subnet

Answer

The IAM role does not have permission to access RDS

Question 9

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 10

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 11

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. Option B is correct because consolidating many small files into larger ones (e.g., targeting ~128 MB–1 GB blocks) reduces the per-file overhead of S3 GET requests and NameNode metadata operations, which is a well-known EMR/HDFS performance best practice. Option C is correct because instance fleets let you specify multiple instance types and subnets with allocation strategies, improving the chance of obtaining capacity and enabling heterogeneous clusters that better match workload needs. Option D is correct because enabling EBS optimization on EC2 instances provides dedicated bandwidth to EBS volumes, which improves I/O throughput for EMR nodes that rely on EBS for HDFS and shuffle data. Option A is not among the marked answers because, while Spot Instances are commonly used for task nodes, the question asks for performance optimization best practices and the marked set does not include it. Option E is also unmarked because using Spot Instances is primarily a cost-optimization practice, not a performance-optimization best practice.

Answer

Use Spot Instances for task nodes

Answer

Use Spot Instances to reduce costs

Question 12

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. The increasing iterator age indicates that consumers are falling behind the stream's production rate, and since CPU is below 50%, the bottleneck is not compute but the number of consumer instances processing shards. Increasing the Auto Scaling maximum and scaling on iterator age allows more KCL workers to be added, each taking leases on shards, which parallelizes processing and reduces latency without over-provisioning.

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 13

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. The root problem is that the Glue job performs a full scan of the S3 bucket every hour, re-reading all files instead of only new ones. Using S3 event notifications to trigger Glue jobs only when new files arrive eliminates the redundant full scans, cutting both processing time and cost as file volume grows 100x. This directly addresses the inefficiency without over-provisioning resources.

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 14

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 loads data from Amazon S3 into Redshift in parallel across all slices, which is far more efficient than row-by-row INSERT statements from a staging table. COPY also supports compression, columnar formats, and automatic distribution/sort key handling, reducing CPU load during the ETL window. This directly addresses slow load performance and high CPU utilization without changing cluster configuration.

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 15

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