MLS-C01 Data Engineering Practice Test 8 — 15 Questions

Question 1

A company is using AWS Glue to run ETL jobs that transform data from Amazon S3 to Amazon Redshift. The jobs are currently failing due to insufficient memory. The data volume varies, with occasional spikes. Which solution should be used to handle the variable memory requirements efficiently?

Accepted Answer

Use AWS Glue Flex execution to allocate resources dynamically. AWS Glue Flex execution allows jobs to use flexible resources that can handle varying memory needs, and it is cost-effective for variable workloads. Option A (increasing DPU) would waste resources during low volume. Option C (using Apache Spark on EMR) increases management overhead. Option D (splitting jobs) adds complexity and may not handle spikes.

Answer

Migrate the ETL jobs to Amazon EMR with Apache Spark

Answer

Increase the number of DPUs (Data Processing Units) for all jobs

Answer

Split the jobs into smaller steps and run them sequentially

Question 2

A data pipeline uses Amazon Kinesis Data Streams to ingest event data. The data is consumed by an AWS Lambda function, which writes to Amazon DynamoDB. The Lambda function is experiencing throttling errors, and the DynamoDB write capacity is underutilized. The events must be processed in order per shard. Which solution most effectively addresses the throttling?

Accepted Answer

Increase the number of shards in the Kinesis Data Stream. Adding more shards to the Kinesis stream increases the number of concurrent Lambda invocations, spreading the load. Option B (increasing DynamoDB write capacity) does not address Lambda throttling. Option C (using SQS FIFO) would decouple but may cause duplicates. Option D (increasing Lambda reserved concurrency) alone may not help if Lambda is throttled due to concurrency limits; adding shards is more effective.

Answer

Use an SQS FIFO queue between Kinesis and Lambda to buffer

Answer

Increase the Lambda function's reserved concurrency

Answer

Increase the write capacity units of the DynamoDB table

Question 3

A data scientist needs to run a one-time SQL query on a large dataset in Amazon S3. The dataset is stored in Parquet format and is about 500 GB. The query requires complex aggregations and joins. Which AWS service should be used to minimize cost and setup time?

Accepted Answer

Amazon Athena. Amazon Athena is serverless and charges per query, ideal for one-time queries on S3 data. Option B (Amazon EMR) requires cluster setup and management. Option C (Amazon Redshift) requires provisioning a cluster. Option D (Amazon RDS) is not designed for direct S3 querying.

Answer

Amazon Redshift

Answer

Amazon RDS for MySQL

Answer

Amazon EMR with Spark SQL

Question 4

A company is building a data lake on Amazon S3. Raw data is ingested from multiple sources in different formats (CSV, JSON, Parquet). The data must be cataloged and made queryable using Amazon Athena. The data schema may evolve over time. Which approach minimizes manual effort and supports schema evolution?

Accepted Answer

Use AWS Glue Crawlers to automatically create and update the Glue Data Catalog. Using AWS Glue Crawlers automatically infers and updates schemas in the Glue Data Catalog, supporting schema evolution. Option B (manual schema creation) is error-prone and not scalable. Option C (EMR only) does not leverage Glue Catalog. Option D (Athena only) can query but does not manage schema evolution.

Answer

Use Athena only, without a catalog, by directly querying files

Answer

Use Amazon EMR to process data and write to a Hive metastore

Answer

Manually create tables in Athena using DDL statements

Question 5

An e-commerce company uses Amazon Kinesis Data Firehose to deliver clickstream data to Amazon S3. The data arrives at unpredictable rates, with occasional bursts. The company needs to ensure data is delivered within 60 seconds of ingestion, and the data must be partitioned by year/month/day/hour. Which configuration meets these requirements?

Accepted Answer

Set the buffer interval to 60 seconds and enable dynamic partitioning. Setting the buffer interval to 60 seconds and enabling dynamic partitioning ensures data is delivered within 60 seconds and automatically partitioned by time. Option B (buffer size 1 MB) would cause excessive small files. Option C (Lambda transformation) adds latency. Option D (Glue streaming) is not directly integrated with Firehose.

Answer

Set the buffer size to 1 MB and disable dynamic partitioning

Answer

Use a Lambda function to process data and write to S3 with partitioning

Answer

Use AWS Glue streaming ETL to read from Firehose and write to S3

Question 6

Which TWO AWS services can be used to transform data in transit before storing it in Amazon S3? (Choose TWO.)

Accepted Answer

AWS Glue. AWS Glue can perform ETL transformations on data in motion. Amazon Kinesis Data Firehose can invoke Lambda functions to transform data before delivery. Options B, D, and E are not used for transforming data in transit.

Answer

Amazon Redshift Spectrum

Answer

AWS Data Pipeline

Answer

Amazon Athena

Question 7

A company is designing a data pipeline to analyze customer behavior. The pipeline must handle real-time streaming data and batch data. The data must be stored in a data lake on Amazon S3 and also made available for interactive queries. Which THREE services should be combined to build this pipeline? (Choose THREE.)

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams ingests real-time data. AWS Glue can perform ETL and catalog the data. Amazon Athena allows interactive querying on S3. Options B, D, and E are not needed or redundant.

Answer

Amazon Redshift

Answer

Amazon DynamoDB Streams

Question 8

A data engineering team is migrating on-premises Hadoop workloads to AWS. The workloads include batch processing using Apache Spark and interactive SQL queries. The data is stored in HDFS. Which TWO AWS services should be used to replace HDFS and provide a scalable, durable storage layer? (Choose TWO.)

Accepted Answer

Amazon EMR with EMRFS. Amazon S3 is the primary storage for data lakes, replacing HDFS. Amazon EMR with EMRFS can access S3 as if it were HDFS. Option B (Amazon FSx for Lustre) is a high-performance file system but not a direct HDFS replacement for durability. Option C (Amazon EBS) is block storage, not scalable. Option D (Amazon RDS) is relational database. Option E (Amazon DynamoDB) is NoSQL.

Answer

Amazon EBS

Answer

Amazon FSx for Lustre

Answer

Amazon RDS

Question 9

A data engineering team needs to ingest streaming data from thousands of IoT devices into a data lake on Amazon S3 for near-real-time analytics. The data must be partitioned by device ID and timestamp, and the team must minimize data loss during ingestion failures. Which solution is MOST appropriate?

Accepted Answer

Use Amazon Kinesis Data Firehose to write directly to S3 with dynamic partitioning.. Option B is correct because Kinesis Data Firehose can directly write to S3 with partitioning and automatic retries, minimizing data loss. Option A is wrong because Kinesis Data Streams requires a separate consumer to write to S3, adding complexity. Option C is wrong because Lambda has a 15-minute limit and may lose data if the function fails. Option D is wrong because S3 Transfer Acceleration is for speeding up uploads, not for streaming ingestion.

Answer

Use Amazon Kinesis Data Streams with a Lambda function that writes to S3.

Answer

Use Amazon S3 Transfer Acceleration with direct uploads from devices.

Answer

Use AWS Lambda to receive data via API Gateway and write to S3.

Question 10

A data scientist needs to query a 2 TB dataset stored in Amazon S3 using Amazon Athena. The data is in CSV format and is used for exploratory analysis. Queries are currently slow and expensive. Which action will improve query performance and reduce cost?

Accepted Answer

Convert the data to Parquet format and partition by a commonly filtered column.. Option D is correct because converting CSV to Parquet reduces scan size and query cost, and partitioning further limits data scanned. Option A is wrong because increasing workers is not applicable to Athena. Option B is wrong because converting to JSON may increase data size and cost. Option C is wrong because Athena does not use indexes.

Answer

Convert the data to JSON format to improve compression.

Answer

Increase the number of workers in the Athena query engine.

Answer

Create a composite index on the data using Athena's index feature.

Question 11

A company uses AWS Glue ETL jobs to process data from an Amazon RDS for MySQL database into Amazon S3. The job runs daily and takes 6 hours to complete. The team wants to reduce runtime and cost. The source table has 50 million rows and is updated continuously. Which combination of changes would be MOST effective?

Accepted Answer

Use JDBC connections with pushdown predicates and increase the number of DPUs.. Option B is correct because JDBC connections with pushdown predicates reduce data transferred, and increasing DPUs can parallelize processing. Option A is wrong because increasing DPUs without pushdown may cause bottleneck on source. Option C is wrong because a single worker cannot process 50M rows quickly. Option D is wrong because triggers do not optimize runtime.

Answer

Use a single worker with a larger instance type.

Answer

Increase the number of DPUs and enable job bookmarking.

Answer

Change the job trigger from time-based to event-based.

Question 12

A data pipeline uses Amazon Kinesis Data Streams to ingest clickstream data. The data is consumed by an AWS Lambda function that transforms and writes to Amazon DynamoDB. The Lambda function is throttled during traffic spikes, causing data to be reprocessed. Which solution should the team implement to handle the throttling without losing data?

Accepted Answer

Increase the number of shards in the Kinesis stream and configure a dead-letter queue (DLQ) for the Lambda function.. Option C is correct because increasing the number of shards increases the streaming capacity, and using a DLQ captures failed records for later reprocessing. Option A is wrong because DynamoDB auto scaling does not address Lambda throttling. Option B is wrong because reducing batch size may increase processing overhead. Option D is wrong because SQS is not needed as Kinesis already buffers data.

Answer

Use Amazon SQS as an intermediate buffer between Kinesis and Lambda.

Answer

Enable DynamoDB auto scaling to handle writes.

Answer

Reduce the batch size in the Lambda event source mapping.

Question 13

A company wants to use Amazon SageMaker to train a model on a dataset stored in Amazon S3. The dataset is 100 GB and consists of millions of small JSON files. What should the data engineering team do to optimize training performance?

Accepted Answer

Combine the small JSON files into larger Parquet files using a Spark job on Amazon EMR.. Option D is correct because combining small files into larger ones reduces S3 LIST overhead and improves I/O performance. Option A is wrong because Athena is not needed for training. Option B is wrong because EBS is ephemeral and not shared across instances. Option C is wrong because S3 Select is for server-side filtering, not for training performance.

Answer

Copy the data to an Amazon EBS volume attached to the training instance.

Answer

Use Amazon Athena to convert the data into a single CSV file.

Answer

Use S3 Select to filter data before training.

Question 14

A financial services company needs to build a data lake on Amazon S3 that meets regulatory requirements for data retention and encryption. Data must be encrypted at rest and in transit, and access must be audited. The data lake will be queried by Amazon Athena and Amazon Redshift Spectrum. Which combination of actions should be taken?

Accepted Answer

Enable S3 default encryption with SSE-KMS and enable AWS CloudTrail for S3 data events.. Option D is correct because server-side encryption with KMS (SSE-KMS) provides encryption at rest, and CloudTrail logs S3 API calls for auditing. Option A is wrong because SSE-S3 does not provide key management control. Option B is wrong because SSL/TLS is for in-transit encryption, not at rest. Option C is wrong because IAM does not provide encryption.

Answer

Use IAM policies to control access and enable S3 server access logging.

Answer

Use SSL/TLS for all connections and enable S3 versioning.

Answer

Enable S3 default encryption with SSE-S3 and use S3 access logs.

Question 15

A data engineering team is building a pipeline to process terabytes of log data daily using Amazon EMR with Spark. The data arrives in hourly batches and must be processed within 4 hours. The team needs to minimize cost. Which cluster configuration is MOST cost-effective?

Accepted Answer

Use a transient cluster with a mix of on-demand and spot instances, terminated after the job completes.. Option B is correct because spot instances offer significant cost savings and are suitable for fault-tolerant Spark jobs. Option A is wrong because on-demand is more expensive. Option C is wrong because a single large instance reduces parallelism. Option D is wrong because EMR Serverless may be more expensive for predictable, large workloads.

Answer

Use a single large instance with multiple cores to avoid data shuffling.

Answer

Use a long-running cluster of on-demand instances to avoid startup time.

Answer

Use Amazon EMR Serverless to automatically scale.