MLS-C01 Data Engineering Practice Test 15 — 15 Questions

Question 1

A company wants to use Amazon SageMaker to train a model using data stored in Amazon S3. The data is sensitive and must be encrypted at rest and in transit. Which THREE steps should be taken to ensure data security?

Accepted Answer

Configure the SageMaker training job to use an IAM role with least privilege and enable network isolation. Encrypting the S3 bucket with KMS ensures encryption at rest. Using VPC endpoints for S3 ensures data does not traverse the public internet. Enabling encryption in transit between SageMaker and S3 (using HTTPS) is essential. Option A (public internet) is not secure; Option E (Redshift) is irrelevant.

Answer

Store the data in Amazon Redshift instead of S3

Answer

Allow internet access for the SageMaker notebook instance

Question 2

A data engineer needs to collect and analyze log data from multiple EC2 instances in real-time. The solution should be serverless and scalable. Which TWO AWS services should be used?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is the correct choice because it is a fully managed, serverless service that can capture, transform, and load streaming log data from EC2 instances into destinations like Amazon S3 or Amazon OpenSearch Service in near real-time, with no infrastructure to manage. It automatically scales to handle high-throughput data streams, making it ideal for real-time log analytics.

Answer

Amazon EMR

Answer

Amazon Athena

Answer

Amazon S3

Question 3

A company is using AWS Glue ETL jobs to transform data. The jobs are failing due to insufficient memory. The data processing involves complex joins and aggregations. Which THREE actions can improve job performance and reduce memory usage?

Accepted Answer

Filter and project data early in the transformation to reduce data volume. Option A is correct because filtering and projecting data early in the transformation reduces the volume of data that must be processed in subsequent operations like joins and aggregations. By using pushdown predicates and selecting only necessary columns, you minimize the data shuffled across the cluster, which directly reduces memory pressure and improves job performance in AWS Glue ETL.

Answer

Decrease the number of DPUs allocated to the job

Answer

Use a single node cluster to avoid shuffle overhead

Question 4

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 5

A data engineer is designing a data pipeline that transforms raw JSON files (each 50-200 KB) in Amazon S3 into Parquet format using AWS Glue. The pipeline must minimize data processing costs and handle a high volume of small files (millions per day). The engineer configures a Glue ETL job with Spark, but the job is slow and expensive due to overhead of reading many small files. Which optimization should the engineer implement to reduce cost and improve performance?

Accepted Answer

Use S3 object grouping or batch operations to combine small files before Glue processing.. Option C is correct. Using S3 object grouping (e.g., S3 batch operations or partitioning) to create larger files reduces the number of tasks and overhead in Spark. Option A is wrong because increasing DPUs increases cost without addressing the small file problem. Option B is wrong because converting to CSV is not more efficient than Parquet; Parquet is columnar and efficient. Option D is wrong because increasing worker type also increases cost without solving small file issue.

Answer

Increase the worker type to G.2X for more memory per worker.

Answer

Increase the number of DPUs allocated to the Glue job.

Answer

Change the output format from Parquet to CSV to reduce compression overhead.

Question 6

A company uses Amazon Kinesis Data Firehose to deliver streaming data to Amazon S3. The data contains personally identifiable information (PII) that must be redacted before storage. Which AWS service can be integrated with Kinesis Data Firehose to transform the data in real time?

Accepted Answer

AWS Lambda. Option A is correct. AWS Lambda can be used as a transformation function within a Kinesis Data Firehose delivery stream to redact PII. Option B is wrong because Amazon EMR is for big data processing, not inline transformation. Option C is wrong because Amazon Kinesis Data Analytics is for analyzing streams, not for transformation. Option D is wrong because Amazon Athena is a query service, not a transformation service.

Answer

Amazon Athena

Answer

Amazon Kinesis Data Analytics

Answer

Amazon EMR

Question 7

A data engineering team needs to build a data lake on Amazon S3 that will be queried by Amazon Athena and Amazon Redshift Spectrum. The data will be ingested from multiple sources in various formats (CSV, JSON, Parquet). Which partitioning strategy will provide the best query performance for date-range queries?

Accepted Answer

Partition by year, month, and day in a hierarchical structure.. Option D is correct because partitioning by year, month, and day as separate prefixes (e.g., s3://bucket/year=2024/month=01/day=15/) allows Athena and Redshift Spectrum to prune partitions efficiently. Option A is wrong because a single partition per day results in too many partitions for large datasets. Option B is wrong because no partitioning leads to full table scans. Option C is wrong because partition by source first then date may be less optimal if queries often filter by date across sources.

Answer

Partition by date with one partition per day in a flat structure.

Answer

Do not partition; let Athena scan the entire dataset.

Answer

Partition by source system first, then by date.

Question 8

A company has an AWS Glue ETL job that reads data from an Amazon RDS for MySQL table and writes to Amazon S3 in Parquet format. The job runs daily and processes 500 GB of data. Recently, the job has been failing with memory errors during the write phase. The data schema is wide (200 columns). Which change should a data engineer make to the Glue job to resolve the memory issue?

Accepted Answer

Configure the write operation with 'groupSize' to limit records per file.. Option B is correct. Using the 'groupSize' or 'maxRecordsPerFile' option in Glue's DynamicFrame writer can control the number of records per Parquet file, reducing memory pressure. Option A is wrong because increasing DPUs may help but is a costlier solution. Option C is wrong because JDBC connection is for reading, not writing. Option D is wrong because using CSV is less efficient and doesn't address memory.

Answer

Increase the number of DPUs for the Glue job.

Answer

Change the output format from Parquet to CSV.

Answer

Use the JDBC connection with fetchSize parameter.

Question 9

A data engineer needs to transfer 50 TB of historical data from an on-premises Hadoop cluster to Amazon S3. The company has a 100 Mbps internet connection and a tight deadline of two weeks. Which AWS service should the engineer use to transfer the data most efficiently?

Accepted Answer

AWS Snowball Edge. Option B is correct. AWS Snowball Edge is a physical device that can transfer large amounts of data faster than over the internet. Option A is wrong because AWS DataSync over internet would be too slow (100 Mbps). Option C is wrong because AWS Storage Gateway is for ongoing hybrid storage, not bulk transfer. Option D is wrong because Amazon S3 Transfer Acceleration improves speed but still over internet, not enough for 50 TB in two weeks over 100 Mbps.

Answer

AWS Storage Gateway (Volume Gateway)

Answer

Amazon S3 Transfer Acceleration

Answer

AWS DataSync over the internet

Question 10

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 11

A company uses Amazon Redshift for its data warehouse. The data engineering team needs to load 10 TB of data from Amazon S3 into Redshift every night. The team wants to minimize the load time and use the fewest number of COPY commands. The data is in CSV format and is partitioned by date in S3. Which approach should the team take?

Accepted Answer

Use a manifest file with a single COPY command.. Option D is correct. Using a manifest file with a single COPY command allows Redshift to load multiple files in parallel, optimizing throughput. Option A is wrong because using multiple COPY commands increases overhead. Option B is wrong because loading all data in one file is impractical for 10 TB. Option C is wrong because AWS Glue adds unnecessary overhead and cost for a simple load task.

Answer

Use multiple COPY commands, one per partition.

Answer

Concatenate all data into a single large file before loading.

Answer

Use AWS Glue to transform the data and then load into Redshift.

Question 12

A data engineer needs to schedule an AWS Glue ETL job to run every hour. The job reads from an Amazon DynamoDB table and writes to Amazon S3. Which AWS service should the engineer use to trigger the Glue job on schedule?

Accepted Answer

Amazon EventBridge (CloudWatch Events). Option A is correct. Amazon CloudWatch Events (now Amazon EventBridge) can trigger Glue jobs on a schedule. Option B is wrong because AWS Lambda is not a scheduler. Option C is wrong because Amazon Kinesis is for streaming. Option D is wrong because AWS Step Functions is for orchestrating workflows, but scheduling is typically done via EventBridge.

Answer

Amazon Kinesis Data Streams

Answer

AWS Step Functions

Answer

AWS Lambda

Question 13

A data engineering team is designing a data pipeline that processes streaming data from Amazon Kinesis Data Streams using AWS Lambda. The team notices that some records are being processed multiple times (duplicates). Which TWO steps should the team take to ensure exactly-once processing?

Accepted Answer

Design the Lambda function to be idempotent.. Options A and C are correct. Making the Lambda function idempotent ensures that processing the same record multiple times does not cause duplicates downstream. Using a unique identifier per record and checking against a DynamoDB table allows deduplication. Option B is wrong because increasing batch size increases duplicates. Option D is wrong because disabling retries may cause data loss. Option E is wrong because Kinesis does not guarantee exactly-once delivery; deduplication is needed.

Answer

Increase the batch size to reduce the number of invocations.

Answer

Use Kinesis Producer Library (KPL) to guarantee exactly-once delivery.

Answer

Disable retries on the Lambda function.

Question 14

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 15

A data engineer is building a data pipeline using AWS Glue. The pipeline reads data from Amazon S3, transforms it, and writes it back to S3 in a different format. The engineer needs to handle schema evolution (new columns added over time). Which TWO features of AWS Glue can help manage schema evolution?

Accepted Answer

AWS Glue Data Catalog. Options B and D are correct. The Glue Data Catalog can store schema and update it as new columns are added. DynamicFrame in Glue ETL can handle schema changes automatically by allowing optional fields. Option A is wrong because AWS Lake Formation is for data lake security, not schema evolution. Option C is wrong because Amazon Athena is a query engine, not a schema evolution tool. Option E is wrong because S3 object tags are not for schema management.

Answer

AWS Lake Formation

Answer

Amazon Athena

Answer

Amazon S3 object tags