MLS-C01 Practice Test 48 — 15 Questions

Question 1

A data engineer is designing a data pipeline that ingests data from a relational database into a data lake on Amazon S3. The data must be incrementally loaded daily. Which TWO AWS services can be used together to achieve this?

Accepted Answer

AWS Glue. AWS Database Migration Service (DMS) can perform continuous replication or scheduled tasks to move data from a relational database to S3. AWS Glue can also connect to databases and run incremental ETL jobs using job bookmarks. Option A (Athena) is for querying, Option B (Kinesis) is for streaming, Option E (Redshift) is a data warehouse.

Answer

Amazon Kinesis Data Streams

Answer

Amazon Athena

Answer

Amazon Redshift

Question 2

A data engineering team is building a real-time fraud detection system. Transactions are ingested via Amazon Kinesis Data Streams, and a machine learning model (deployed on Amazon SageMaker) scores each transaction. The team needs to store the raw transactions and the model's predictions in Amazon S3 for later analysis. Which architecture should the team use?

Accepted Answer

Use Amazon Kinesis Data Analytics for Apache Flink to enrich records with SageMaker predictions, then output to Firehose for S3.. Option C is correct. Use Kinesis Data Analytics with a Flink application to enrich each record with the SageMaker prediction, then output to Kinesis Data Firehose for delivery to S3. Option A is wrong because Lambda cannot directly invoke SageMaker for every record in high-throughput streams due to concurrency limits. Option B is wrong because Kinesis Data Firehose does not support invoking SageMaker directly. Option D is wrong because Lambda is not suitable for high-frequency real-time scoring.

Answer

Use AWS Lambda to read from Kinesis, invoke SageMaker, and write directly to S3.

Answer

Use Amazon Kinesis Data Firehose with a transformation Lambda to call SageMaker.

Answer

Use AWS Lambda to invoke the SageMaker endpoint for each record, then write to S3 via Firehose.

Question 3

A data engineering team is designing a data pipeline to process large CSV files (10-50 GB each) stored in Amazon S3. The pipeline must transform the data using AWS Glue and load it into Amazon Redshift for analytics. The team wants to minimize costs while ensuring the pipeline can handle peak loads. Which approach is the most cost-effective?

Accepted Answer

Use AWS Glue with Spark and dynamic frames, scaling the number of workers based on file size.. Option D (use AWS Glue with Spark and dynamic frame) is correct because Glue's Spark-based ETL can handle large files efficiently, and using dynamic frames allows schema inference without manual parsing. Option A (use a single r5.xlarge) may not handle peak loads. Option B (Lambda) has time and memory limits unsuitable for large files. Option C (EMR with Hive) is more complex and typically more expensive than Glue for this use case.

Answer

Use AWS Lambda to process each file and load into Redshift.

Answer

Use Amazon EMR with Hive to transform the data and load into Redshift.

Answer

Use an AWS Glue Python shell job with a single r5.xlarge worker.

Question 4

Which TWO steps are required to set up cross-account access to an Amazon S3 data lake for AWS Glue jobs running in a different AWS account? (Choose two.)

Accepted Answer

Add a bucket policy to the S3 bucket that grants access to the Glue service role from the other account.. To allow cross-account access, the S3 bucket policy must grant access to the Glue service role from the other account, and the Glue job must assume a role that has permissions to access the bucket. Option A (bucket policy) and Option D (IAM role in the second account) are correct. Option B (VPC peering) is not required for S3 access. Option C (cross-account Glue crawler) is not needed. Option E (same account) defeats cross-account.

Answer

Create a cross-account Glue crawler in the source account.

Answer

Set up VPC peering between the two accounts' VPCs.

Answer

Ensure both accounts are in the same AWS organization.

Question 5

A company uses Amazon DynamoDB as the primary data store for a real-time recommendation engine. The data engineering team needs to export a daily snapshot of the DynamoDB table to S3 for offline analytics. The table is large (10 TB) and has a high read/write throughput. Which method will export the data with the least impact on the production workload?

Accepted Answer

Use the DynamoDB export to S3 feature available in the AWS Console or CLI.. Option D is correct because DynamoDB's export to S3 feature uses the table's backup capability, which reads from the storage layer without consuming read capacity units, thus having zero impact on production. Option A (scan) consumes RCUs. Option B (Data Pipeline) also uses scan. Option C (Glue ETL) also consumes RCUs.

Answer

Use AWS Data Pipeline to export the DynamoDB table to S3.

Answer

Use DynamoDB Scan API with parallel scans to export data to S3.

Answer

Use AWS Glue ETL job with a DynamoDB connection to export data.

Question 6

A data pipeline uses AWS Lambda to process small files (10-50 MB) from an S3 bucket and write results to DynamoDB. The Lambda function times out after 15 seconds for larger files. The team wants to handle files up to 100 MB without changing the Lambda code. Which solution is MOST cost-effective?

Accepted Answer

Increase the Lambda function timeout to 5 minutes. Increasing Lambda timeout is the simplest and most cost-effective solution for occasional larger files. Using ECS Fargate or Glue is overkill and more expensive. SQS does not solve the timeout issue.

Answer

Use AWS Glue Python shell job to replace Lambda

Answer

Use Amazon ECS with AWS Fargate to run the processing task

Answer

Configure an SQS queue to buffer the S3 events and batch them

Question 7

Which TWO options are best practices for managing access to data stored in Amazon S3 for a data lake?

Accepted Answer

Use IAM policies to control user and role permissions. IAM policies and bucket policies are standard for access control. S3 ACLs are legacy and not recommended. SSE-S3 is encryption, not access control. Pre-signed URLs are for temporary access, not general governance.

Answer

Use S3 access control lists (ACLs) for granular permissions

Answer

Enable default encryption with SSE-S3

Answer

Generate pre-signed URLs for all data access

Question 8

A company uses Amazon Kinesis Data Firehose to deliver streaming data to Amazon S3. They notice that the data is delivered in 5-minute intervals even though they set the buffer interval to 60 seconds. What could be the cause?

Accepted Answer

The buffer size is set to a value larger than the incoming data rate.. Firehose has a minimum buffer interval of 60 seconds and a maximum of 900 seconds. The actual delivery interval is controlled by both buffer size and interval; if the buffer size is not reached, Firehose waits up to the maximum interval. The default buffer size is 5 MB. If data rate is low, Firehose will wait 5 minutes (default max interval) unless the buffer size is lowered.

Answer

The source Kinesis stream has insufficient shards.

Answer

The S3 bucket is in a different region.

Answer

The IAM role does not have permission to write to S3.

Question 9

A company is using AWS Glue to run ETL jobs that process data in an S3 data lake. The jobs are failing with out-of-memory errors when processing large files. Which configuration change should be made to resolve this issue?

Accepted Answer

Increase the number of DPUs allocated to the job. Option B is correct because increasing the number of DPUs (Data Processing Units) allocates more memory and compute to the Glue job. Option A is wrong because partitioning does not address memory per task; Option C is wrong because job bookmark is for incremental processing, not memory; Option D is wrong because G.1X worker type has limited memory; G.2X or higher is better, but increasing DPUs on the default worker type is simpler.

Answer

Change the worker type to G.1X

Answer

Partition the input data into smaller files

Answer

Enable job bookmark to process only new data

Question 10

A data engineering team needs to ingest streaming data from thousands of IoT devices into Amazon S3 for near-real-time analytics. The solution must handle data that arrives in bursts and must be able to reprocess failed records automatically. Which combination of AWS services should the team use?

Accepted Answer

Amazon Kinesis Data Streams with AWS Lambda. Option B is correct because Amazon Kinesis Data Streams can ingest high-throughput streaming data and retain it for up to 365 days, allowing reprocessing. AWS Lambda can be used to transform and load the data into S3. Option A is wrong because Amazon SQS is not optimized for streaming ingestion and lacks replay capability. Option C is wrong because Amazon Glue is a batch ETL service, not for streaming. Option D is wrong because Amazon DynamoDB Streams are tied to DynamoDB table changes, not direct IoT ingestion.

Answer

AWS Glue with Amazon S3

Answer

Amazon SQS with AWS Lambda

Answer

Amazon DynamoDB Streams with AWS Lambda

Question 11

A company uses Amazon Athena to query a data lake in Amazon S3. The data is partitioned by year, month, day, and hour. The team notices that queries are slow and expensive. The team wants to improve performance and reduce costs. Which THREE actions should the team take?

Accepted Answer

Ensure queries filter on partition columns (year, month, day, hour).. Options A, D, and E are correct. Converting to columnar formats like Parquet reduces the amount of data scanned. Partition pruning using WHERE clauses on partition columns reduces scanned partitions. Compressing data reduces storage and scan volume. Option B is wrong because increasing partitions beyond need can increase overhead. Option C is wrong because using CSV is less efficient.

Answer

Increase the number of partitions by adding a partition for minute.

Answer

Use CSV format with GZIP compression.

Question 12

A data engineer needs to set up a data pipeline that ingests data from an Amazon RDS MySQL database into Amazon S3. The pipeline should run daily and capture incremental changes (inserts, updates, deletes) from the source database. Which AWS service should be used as the data ingestion tool?

Accepted Answer

AWS Database Migration Service (DMS) with continuous change data capture (CDC).. AWS Database Migration Service (DMS) supports continuous replication and can capture changes using CDC. Option A (DMS with CDC) is correct. Option B (Glue) can do batch but not native CDC. Option C (Data Pipeline) is older and less suited for CDC. Option D (Kinesis) is for streaming, not database replication.

Answer

Amazon Kinesis Data Streams with a Lambda function.

Answer

AWS Data Pipeline with a SQL activity.

Answer

AWS Glue with a scheduled crawler.

Question 13

A company is streaming real-time sensor data from IoT devices to Amazon Kinesis Data Streams. The data is then consumed by an AWS Lambda function that enriches the records with metadata from an Amazon DynamoDB table and writes the results to an Amazon S3 bucket. Recently, the Lambda function has been failing with 'ProvisionedThroughputExceededException' errors from DynamoDB. The data volume is variable, with occasional bursts. Which solution should a data engineer implement to resolve this issue without losing data?

Accepted Answer

Enable DynamoDB auto scaling for the table to automatically adjust read capacity based on demand.. Option B is correct because enabling DynamoDB auto scaling dynamically adjusts read/write capacity based on traffic patterns, handling bursts without manual intervention. Option A (increasing read capacity units) is costly and may not handle all peaks. Option C (SQS) introduces latency and does not address the DynamoDB throttling directly. Option D (SNS) is for push notifications, not for resolving throughput issues.

Answer

Increase the DynamoDB table's provisioned read capacity units to a high static value.

Answer

Use an Amazon SQS queue to buffer the Lambda requests before querying DynamoDB.

Answer

Configure an Amazon SNS topic to throttle the data stream before it reaches Lambda.

Question 14

A company is using Amazon Redshift for data warehousing. The data engineering team observes that query performance degrades over time due to data skew. Which three strategies should the team implement to improve performance?

Accepted Answer

Choose appropriate distribution keys based on join and group-by columns.. Option A, B, and E are correct. Choosing appropriate distribution keys reduces data movement. Regular VACUUM and ANALYZE reclaims space and updates statistics. Using sort keys improves query performance by reducing scans. Option C (increasing node count) may help but is costly and not a targeted fix for skew. Option D (dropping indexes) is not applicable to Redshift (no indexes).

Answer

Increase the number of nodes in the Redshift cluster.

Answer

Drop unused indexes on large tables.

Question 15

An organization is migrating its on-premises Hadoop cluster to AWS. The cluster runs Spark jobs that process 50 TB of data daily. The data is stored in HDFS with 3x replication. Which storage option on AWS provides the best price-performance for this workload?

Accepted Answer

Use Amazon EMR with S3 as the data store via EMRFS. Amazon EMR with S3 as storage (using EMRFS) allows separating compute and storage. S3 is durable and cost-effective, avoiding 3x replication overhead. HDFS on EBS would require similar replication and is more expensive. S3 with EMR is the standard recommendation.

Answer

Use AWS Glue to run Spark jobs with data stored in S3

Answer

Use Amazon Redshift Spectrum to query the data directly in S3

Answer

Use Amazon EMR with HDFS on EBS volumes