MLS-C01 Data Engineering Practice Test 7 — 15 Questions

Question 1

A machine learning team needs to preprocess large volumes of clickstream data stored in Amazon S3 before training a model. The preprocessing includes data cleaning, feature engineering, and normalization. The team wants to use a serverless solution that minimizes operational overhead. Which combination of services should the team use?

Accepted Answer

AWS Glue ETL jobs reading from and writing to S3.. AWS Glue provides a serverless Spark environment for running ETL jobs on data in S3. Amazon SageMaker Processing jobs are also serverless but are more suited for post-training tasks. Option B is wrong because EMR requires cluster management. Option C is wrong because SageMaker Notebooks are interactive, not automated. Option D is wrong because Athena is for ad-hoc queries, not complex transformations.

Answer

Amazon SageMaker Notebooks with custom Python scripts.

Answer

Amazon EMR with Spark clusters.

Answer

Amazon Athena with SQL queries.

Question 2

A company is using Amazon Kinesis Data Analytics for Apache Flink to process real-time data. The data source is a Kinesis data stream, and the output is written to an S3 bucket. Recently, the processing latency has increased significantly. The team suspects that the Flink application is encountering backpressure. Which metric should the team monitor to confirm backpressure?

Accepted Answer

busyTimeMsPerSecond. Backpressure in Flink is indicated by high 'busyTimeMsPerSecond' metric, which shows the time the operator is busy processing. Option A is wrong because 'numRecordsInPerSecond' measures throughput, not backpressure. Option B is wrong because 'currentLowWatermark' is for event time progress. Option D is wrong because 'numberOfFailedCheckpoints' indicates checkpoint failures, not backpressure directly.

Answer

currentLowWatermark

Answer

numberOfFailedCheckpoints

Answer

numRecordsInPerSecond

Question 3

A data scientist wants to query a dataset stored in Amazon S3 using standard SQL without provisioning any servers. The dataset is in CSV format and is updated daily. Which AWS service should be used?

Accepted Answer

Amazon Athena. Amazon Athena is a serverless interactive query service that allows querying data in S3 using standard SQL. Option B is wrong because Redshift is a data warehouse requiring provisioning. Option C is wrong because RDS is a relational database service. Option D is wrong because DynamoDB is a NoSQL database.

Answer

Amazon Redshift

Answer

Amazon RDS

Answer

Amazon DynamoDB

Question 4

A company is building a data pipeline to process sensitive customer data. The pipeline uses AWS Glue for ETL and stores results in Amazon S3. The security team requires that all data be encrypted at rest in S3 using customer-managed AWS KMS keys. Additionally, the Glue job must be able to write encrypted data to S3. What should the data engineer do to meet these requirements?

Accepted Answer

Attach a policy to the Glue job's IAM role that includes kms:GenerateDataKey and kms:Decrypt actions for the KMS key.. The Glue job's IAM role needs kms:GenerateDataKey and kms:Decrypt permissions on the KMS key. Option A is wrong because S3 bucket policy alone doesn't grant the Glue service access to KMS. Option C is wrong because SSE-S3 is Amazon-managed keys, not customer-managed. Option D is wrong because SSE-C requires the customer to manage keys and Glue cannot provide the encryption key in each request.

Answer

Use S3 server-side encryption with customer-provided keys (SSE-C).

Answer

Use S3 server-side encryption with SSE-S3, which is enabled by default.

Answer

Configure an S3 bucket policy to enforce encryption and attach it to the Glue job's IAM role.

Question 5

A large e-commerce company is using Amazon DynamoDB as the source for real-time analytics. The data is streamed to Amazon Kinesis Data Streams using DynamoDB Streams and then processed by an AWS Lambda function. The Lambda function writes the data to an Amazon Elasticsearch Service cluster for search and visualization. Recently, the Lambda function has been failing with throttling errors from the Elasticsearch cluster. What is the MOST effective way to handle this?

Accepted Answer

Configure a Dead Letter Queue (DLQ) on the Lambda function to capture failed records and implement retry logic.. Using a Dead Letter Queue (DLQ) for failed records allows the Lambda function to continue processing without blocking, and the failed records can be reprocessed later. Option A is wrong because increasing Lambda concurrency would exacerbate the throttling. Option B is wrong because increasing shards increases throughput but doesn't address Elasticsearch throttling. Option D is wrong because Lambda cannot directly control Kinesis stream throughput; it processes in batches.

Answer

Increase the Lambda function's reserved concurrency to handle more invocations.

Answer

Increase the number of shards in the Kinesis data stream.

Answer

Decrease the Kinesis stream's retention period to reduce the data volume.

Question 6

A company is using Amazon S3 as a data lake. The data engineering team needs to catalog the schema of the data and make it available for querying with Amazon Athena. Which AWS Glue component should be used?

Accepted Answer

AWS Glue Crawlers. AWS Glue Crawlers automatically scan data sources, infer schemas, and populate the AWS Glue Data Catalog. Option B is wrong because Glue ETL jobs are for transforming data. Option C is wrong because Glue DataBrew is for visual data preparation. Option D is wrong because Glue Studio is a visual ETL development tool.

Answer

AWS Glue Studio

Answer

AWS Glue ETL jobs

Answer

AWS Glue DataBrew

Question 7

A data engineer needs to transfer 50 TB of historical data from an on-premises Hadoop cluster to Amazon S3. The on-premises network has a 100 Mbps connection to AWS. The transfer must be completed within one week. Which approach should the engineer use?

Accepted Answer

Use AWS Snowball Edge device to physically transfer the data.. AWS Snowball Edge is a physical device that can handle large data volumes over slow networks. 50 TB over 100 Mbps would take about 50 days, exceeding the one-week requirement. Option B is wrong because AWS DataSync still uses the network. Option C is wrong because S3 Transfer Acceleration improves speed but not enough. Option D is wrong because VPN is not designed for bulk data transfer.

Answer

Use Amazon S3 Transfer Acceleration.

Answer

Use AWS DataSync to transfer the data over the network.

Answer

Use multiple concurrent AWS CLI copy commands over VPN.

Question 8

A company is using Amazon Redshift for data warehousing. The data engineering team notices that queries are slow and the system is frequently writing to disk due to insufficient memory. Which type of workload management (WLM) configuration change would help reduce disk writes?

Accepted Answer

Increase the memory percentage allocated to the WLM queue.. If queries spill to disk, they need more memory. Increasing the memory percentage allocated to the queue reduces disk spills. Option A is wrong because concurrency slots increase parallelism but may reduce memory per query. Option C is wrong because query monitoring rules only flag issues, not fix them. Option D is wrong because short query acceleration is for short queries, not memory issues.

Answer

Increase the number of query concurrency slots.

Answer

Enable query monitoring rules to abort queries that spill to disk.

Answer

Enable short query acceleration (SQA).

Question 9

Which TWO AWS services can be used to move data from an on-premises database to Amazon S3 on a recurring schedule without writing custom code? (Choose 2.)

Accepted Answer

AWS Glue. AWS Database Migration Service (DMS) can perform continuous replication from on-premises databases to S3. AWS Glue can run scheduled ETL jobs to pull data from on-premises sources via JDBC and write to S3. Option A is wrong because Snowball is a one-time physical transfer. Option D is wrong because Kinesis Data Firehose is for streaming, not scheduled batch. Option E is wrong because Athena is a query service, not a data movement tool.

Answer

AWS Snowball Edge

Answer

Amazon Athena

Answer

Amazon Kinesis Data Firehose

Question 10

Which THREE factors should be considered when choosing between Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose for a real-time data ingestion pipeline? (Choose 3.)

Accepted Answer

Need for custom data processing using AWS Lambda. Kinesis Data Streams provides custom processing with shard-level throughput and retention up to 365 days. Firehose automatically delivers to destinations like S3, Redshift, and Elasticsearch with near-real-time latency. Option A is wrong because both support encryption. Option D is wrong because both support compression before delivery. Option E is wrong because both can handle streaming data, but Firehose is simpler for delivery to S3.

Answer

Ability to compress data before delivery

Answer

Ability to encrypt data at rest

Question 11

Which TWO AWS services can be used to schedule and orchestrate a data pipeline that includes multiple steps such as data extraction, transformation, and loading? (Choose 2.)

Accepted Answer

Amazon Managed Workflows for Apache Airflow (MWAA). AWS Step Functions is a serverless orchestration service that can coordinate multiple AWS services into workflows. Amazon Managed Workflows for Apache Airflow (MWAA) is a managed version of Apache Airflow for orchestrating pipelines. Option A is wrong because Lambda is a compute service, not an orchestrator. Option C is wrong because CloudWatch Events is for event scheduling, not complex orchestration. Option D is wrong because Glue is an ETL service, not an orchestrator, though it can be part of a pipeline.

Answer

AWS Lambda

Answer

AWS Glue

Answer

Amazon CloudWatch Events

Question 12

A data engineer needs to analyze large CSV files stored in Amazon S3 using SQL queries. The data is not frequently accessed, and cost is a primary concern. Which AWS service should be used to query the data directly in S3 without moving it?

Accepted Answer

Amazon Athena. Amazon Athena is a serverless query service that allows SQL queries directly on data in S3, ideal for ad-hoc analysis with a pay-per-query pricing model. Option A (Amazon Redshift Spectrum) also queries S3 but requires an existing Redshift cluster. Option B (AWS Glue) is for ETL. Option D (Amazon EMR) requires cluster management and is more expensive for occasional queries.

Answer

Amazon EMR

Answer

Amazon Redshift Spectrum

Answer

AWS Glue

Question 13

A company is using Amazon Kinesis Data Streams to ingest real-time clickstream data. The data must be transformed before being stored in Amazon S3. The transformations include enrichment with reference data from Amazon DynamoDB. Which AWS service should be used to perform the transformation with minimal operational overhead?

Accepted Answer

Amazon Kinesis Data Analytics for Apache Flink. Amazon Kinesis Data Analytics for Apache Flink allows real-time stream processing with Flink, including enrichment from external sources like DynamoDB. Option A (AWS Lambda) is simpler but has a 15-minute timeout and may not handle high throughput well. Option C (Amazon EMR) requires cluster management. Option D (Amazon Kinesis Data Firehose) is for delivery and can invoke Lambda for transformation, but complex transformations with external lookups are better handled by Data Analytics.

Answer

Amazon Kinesis Data Firehose with data transformation

Answer

AWS Lambda functions invoked by Kinesis Data Streams

Answer

Amazon EMR with Apache Spark Streaming

Question 14

A data engineering team is designing a data lake on Amazon S3. Raw data is ingested in JSON format and must be partitioned by year, month, and day. The team expects high query performance for recent data but infrequent queries for older data. The data is immutable. Which storage tier configuration minimizes costs while meeting performance requirements?

Accepted Answer

Use S3 Intelligent-Tiering for the entire data lake. Using S3 Intelligent-Tiering for the entire data lake automatically optimizes costs by moving data between frequent and infrequent access tiers based on usage patterns, without performance impact. Option A uses Glacier for recent data, which would cause retrieval delays. Option C uses S3 Standard for all data, which is cost-inefficient for older data. Option D uses a lifecycle policy to S3 One Zone-IA, which is not cost-optimal for infrequent queries and may have durability concerns.

Answer

Store all data in S3 Standard, then move to S3 Glacier after 30 days using a lifecycle policy

Answer

Store recent partitions in S3 Standard, older partitions in S3 One Zone-IA

Answer

Keep all data in S3 Standard because query performance is critical

Question 15

A data engineer is tasked with building a system to process a continuous stream of IoT sensor data. The data must be processed in near real-time, and the results must be stored in Amazon S3 partitioned by hour. Which AWS service is the most cost-effective and simplest to implement?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is the simplest and most cost-effective way to ingest streaming data and deliver it to S3 with automatic partitioning by time. Option A (Kinesis Data Streams) requires custom consumers. Option C (Amazon SQS) is for message queues, not streaming. Option D (AWS Database Migration Service) is for database migration.

Answer

Amazon Simple Queue Service (SQS) with AWS Lambda

Answer

Amazon Kinesis Data Streams with Amazon EC2 consumers

Answer

AWS Database Migration Service (DMS) for continuous replication