MLS-C01 Data Engineering Practice Test 9 — 15 Questions

Question 1

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 1 Gbps connection to AWS. The transfer must be completed within 10 days. What is the MOST efficient approach?

Accepted Answer

Use AWS Snowball Edge to physically ship the data.. Option C is correct because AWS Snowball Edge can transfer large volumes faster than a network, especially with limited bandwidth. Option A is wrong because a 1 Gbps line would take over 5 days for 50 TB, but may be unreliable. Option B is wrong because 50 TB over VPN would be very slow. Option D is wrong because AWS DataSync is network-based and limited by bandwidth.

Answer

Use Amazon S3 Transfer Acceleration to speed up the upload.

Answer

Use AWS DataSync over the existing network connection.

Answer

Set up a VPN connection and use multi-part upload directly to S3.

Question 2

A company runs a real-time fraud detection pipeline using Amazon Kinesis Data Analytics. The pipeline reads from a Kinesis data stream, performs sliding window aggregations, and writes results to a DynamoDB table. The application is experiencing high latency during peak hours. Which action would MOST effectively reduce latency?

Accepted Answer

Increase the number of shards in the Kinesis data stream.. Option A is correct because increasing the number of shards increases parallelism in both the stream and the Kinesis Data Analytics application. Option B is wrong because DynamoDB auto scaling helps with writes but not streaming latency. Option C is wrong because a larger window increases latency. Option D is wrong because decreasing parallelism reduces throughput.

Answer

Enable DynamoDB auto scaling to handle write spikes.

Answer

Decrease the parallelism level in the Kinesis Data Analytics application.

Answer

Increase the sliding window size to reduce computational frequency.

Question 3

A data engineering team is designing a data pipeline to process streaming data from social media feeds. The data must be deduplicated, enriched with customer information from a relational database, and stored in Amazon S3 in Parquet format. Which AWS services should the team use to build this pipeline? (Select TWO.)

Accepted Answer

AWS Glue. Option A is correct because Kinesis Data Streams ingests streaming social media data. Option D is correct because AWS Glue ETL jobs can read from the stream, perform deduplication and enrichment using JDBC connections to the relational database, and write to S3 in Parquet. Option B is wrong because Kinesis Data Firehose does not support enrichment with a relational database. Option C is wrong because Athena is a query engine, not an ETL tool. Option E is wrong because SageMaker is for ML, not data pipeline.

Answer

Amazon Kinesis Data Firehose

Answer

Amazon Athena

Answer

Amazon SageMaker

Question 4

A company uses AWS Glue Data Catalog to manage metadata for its data lake on Amazon S3. The data lake contains terabytes of data in CSV format. The data engineering team wants to improve query performance in Amazon Athena and reduce costs. Which actions should the team take? (Select THREE.)

Accepted Answer

Compress the data using Snappy or GZIP.. Option B is correct because converting to columnar formats like Parquet reduces data scanned and improves performance. Option C is correct because partitioning limits the amount of data scanned per query. Option D is correct because compressing data reduces storage cost and data scanned. Option A is wrong because CSV is not efficient. Option E is wrong because it does not directly address performance or cost for Athena.

Answer

Create views in Athena to simplify queries.

Answer

Convert the data to JSON format.

Question 5

A data engineering team needs to schedule a nightly ETL job that extracts data from an Amazon RDS for PostgreSQL instance, transforms it using Spark, and loads it into Amazon S3. The team wants to use AWS Glue for this task. Which components are required? (Select TWO.)

Accepted Answer

An AWS Glue ETL job with a Spark script.. Option A is correct because a connection to the PostgreSQL database is needed for extraction. Option D is correct because an AWS Glue ETL job with Spark script performs the transformation. Option B is wrong because a crawler is for cataloging, not for ETL. Option C is wrong because a development endpoint is for interactive development, not production scheduling. Option E is wrong because a notebook is for development, not for scheduled jobs.

Answer

An AWS Glue crawler to populate the Data Catalog.

Answer

An AWS Glue development endpoint.

Answer

An AWS Glue notebook for data exploration.

Question 6

Refer to the exhibit. An IAM policy is attached to a data engineering role. The role is used by an AWS Glue ETL job that reads from 'raw/' and writes to 'processed/'. The job fails with an access denied error when trying to write to 'processed/'. What is the likely cause?

Accepted Answer

The Deny statement on s3:DeleteObject prevents overwriting objects.. Option B is correct because the Deny statement explicitly denies s3:DeleteObject on all objects, but PutObject operations on existing objects may require DeleteObject permission for overwrite (depending on the object versioning configuration). However, for new objects, PutObject should work. The more common issue is that the policy does not allow 's3:ListBucket' at the bucket level, which is needed for certain operations. Actually, the error is likely due to missing 's3:ListBucket' permission. But among options, B is most plausible: the Deny on DeleteObject may interfere if the job attempts to overwrite existing objects. Option A is wrong because the policy allows both. Option C is wrong because the resource matches. Option D is wrong because the role is attached correctly.

Answer

The role is not correctly attached to the Glue job.

Answer

The policy does not allow both s3:GetObject and s3:PutObject on the same resource.

Answer

The policy specifies incorrect ARN for the 'processed' folder.

Question 7

Refer to the exhibit. A data engineer examines the output of 'aws glue get-job-run' for a failed job. The job run state is FAILED, but ErrorMessage is empty. The job ran for 3600 seconds (1 hour) before failing. What is the MOST likely cause of the failure?

Accepted Answer

The job ran out of memory due to insufficient DPU allocation.. Option D is correct because the job ran for exactly 1 hour and then failed with no error message, indicating it hit the timeout. The default timeout is 2880 minutes (48 hours), but the job may have a custom timeout set. However, the exhibit shows Timeout: 2880 minutes, which is not hit. Wait, ExecutionTime: 3600 seconds = 60 minutes. The Timeout is 2880 minutes, so not timeout. Another common cause is out-of-memory. But the question states 'empty error message' - often Glue jobs fail silently due to resource constraints like memory. Option D: The job ran out of memory because MaxCapacity is 10 DPUs, which may be insufficient for the data size. Option A is wrong because permissions would show Access Denied error. Option B is wrong because Python errors would appear in logs. Option C is wrong because no error message suggests it's not a connection timeout.

Answer

The JDBC connection to the source database timed out.

Answer

The IAM role does not have sufficient permissions to access S3.

Answer

The Python script has a syntax error.

Question 8

Refer to the exhibit. A CloudFormation template creates an S3 bucket. The data engineering team stores daily log files in this bucket and queries them using Amazon Athena. After 30 days, queries on logs older than 30 days start failing with 'Access Denied' errors. What is the MOST likely reason?

Accepted Answer

The lifecycle rule transitions objects to GLACIER after 30 days, making them inaccessible to Athena.. Option C is correct because the lifecycle rule transitions objects to GLACIER after 30 days, and Athena cannot query objects in GLACIER storage class. Option A is wrong because transition to GLACIER does not affect permissions. Option B is wrong because object is not deleted until 365 days. Option D is wrong because SSE-S3 is not mentioned.

Answer

The bucket uses default encryption with SSE-S3, which Athena does not support.

Answer

The lifecycle rule deletes objects after 30 days.

Answer

The bucket policy denies access to objects older than 30 days.

Question 9

A company captures streaming data from IoT devices using Amazon Kinesis Data Streams. The data is consumed by a custom application that processes records in near real-time. Recently, the application has been falling behind, and the stream is showing increased 'iterator age' metrics in CloudWatch. Which action is MOST likely to reduce the iterator age?

Accepted Answer

Increase the number of shards in the stream. Option D is correct because increasing the number of shards increases the capacity of the stream, allowing more parallel consumers and reducing backlog. Option A is wrong because reducing retention period does not affect processing speed. Option B is wrong because increasing retention period may increase backlog. Option C is wrong because decreasing shards reduces capacity, worsening the issue.

Answer

Increase the data retention period of the stream

Answer

Decrease the number of shards in the stream

Answer

Reduce the data retention period of the stream

Question 10

A data engineer needs to build a pipeline that ingests CSV files from an S3 bucket, validates the schema, and loads the data into an Amazon Redshift cluster. The pipeline must handle schema evolution gracefully by adding new columns as they appear in the source files. Which combination of AWS services and configurations would meet these requirements with minimal operational overhead?

Accepted Answer

Use AWS Glue to create a crawler and an ETL job that writes to Redshift, with 'resolveChoice' to handle new columns. Option C is correct because AWS Glue can crawl the S3 data to infer schema, and Glue ETL jobs can handle schema evolution using DynamicFrame and resolveChoice. Option A is wrong because Kinesis Data Firehose is for streaming, not batch CSV files. Option B is wrong because Redshift Spectrum does not handle schema evolution automatically. Option D is wrong because Athena is an interactive query engine, not an ETL pipeline.

Answer

Use AWS Glue to create a crawler that updates the schema, then use Redshift Spectrum to query the data directly from S3

Answer

Use Amazon Kinesis Data Firehose to ingest the files and load into Redshift, with a Lambda function to detect schema changes

Answer

Use Amazon Athena to create external tables with schema-on-read, and insert results into Redshift using INSERT INTO

Question 11

A machine learning team is preparing a dataset for model training. The data is stored in an Amazon S3 bucket with objects that are each approximately 100 MB in size. The team wants to use Amazon SageMaker for training. To optimize training performance, which data format and storage configuration should be used?

Accepted Answer

Store data as RecordIO-Protobuf files and use SageMaker Pipe input mode. Option B is correct because SageMaker Pipe input mode streams data directly from S3, avoiding disk I/O, and RecordIO-Protobuf is an optimized binary format. Option A is wrong because File mode copies data to disk, increasing latency. Option C is wrong because CSV is not as efficient as binary. Option D is wrong because File mode with CSV is not optimal.

Answer

Store data as RecordIO-Protobuf files and use SageMaker File input mode

Answer

Store data as CSV files and use SageMaker Pipe input mode

Answer

Store data as CSV files and use SageMaker File input mode

Question 12

A company uses Amazon Kinesis Data Analytics for Apache Flink to process real-time clickstream data. The application uses event time and watermarks for windowed aggregations. The team notices that the output from tumbling windows is delayed, and many late records are being dropped. What is the MOST likely cause?

Accepted Answer

The allowed lateness is set too low, causing late records to be discarded. Option B is correct because late records are dropped when the watermark has passed the window's end; increasing the allowed lateness gives more time for late records to arrive. Option A is wrong because idle sources cause watermarks to stall, not drop late records. Option C is wrong because checkpointing interval does not affect watermark progress. Option D is wrong because parallelism affects throughput, not watermark behavior.

Answer

The checkpointing interval is too long, causing state to be lost

Answer

The parallelism is too low, causing backpressure

Answer

The source is marking itself as idle, causing watermarks to stall

Question 13

A research lab stores large genomic datasets in Amazon S3 Glacier Deep Archive. They need to run a one-time analysis on a subset of 10 PB of data. The analysis will use an Amazon EMR cluster with Amazon S3 as the data source. What is the MOST cost-effective and performant way to make the data available for the EMR cluster?

Accepted Answer

Initiate a Bulk retrieval request and restore the data to S3 Standard for the duration of the analysis. Option D is correct because Bulk retrieval is the cheapest retrieval tier for Glacier Deep Archive, and restoring to S3 Standard allows the EMR cluster to read efficiently. Option A is wrong because reading directly from Glacier is not supported by EMR. Option B is wrong because Expedited is more expensive and not needed for a one-time batch job. Option C is wrong because restoring to S3 Standard-IA adds retrieval costs for data that is accessed once.

Answer

Restore the data to S3 Standard-IA and delete after the analysis

Answer

Configure the EMR cluster to read directly from Glacier Deep Archive using S3 Console

Answer

Initiate an Expedited retrieval request and use the temporary copy for the EMR cluster

Question 14

An ML engineer is using Amazon SageMaker to train a model on a dataset that contains personal identifiable information (PII). The data must be encrypted at rest and in transit. The company uses AWS KMS for key management. How should the engineer configure the SageMaker training job to meet these encryption requirements?

Accepted Answer

Specify a KMS key for the training job's VolumeKmsKeyId and enable inter-container traffic encryption. Option C is correct because SageMaker supports KMS encryption for both the ML storage volume (at rest) and inter-container traffic (in transit). Option A is wrong because S3 Server-Side Encryption only covers data at rest in S3, not during training. Option B is wrong because SSL does not encrypt the storage volume. Option D is wrong because disabling inter-container encryption is not secure.

Answer

Enable S3 Server-Side Encryption (SSE-S3) on the input data bucket

Answer

Use a custom Docker image with built-in encryption and disable inter-container traffic encryption for performance

Answer

Use a VPC with an S3 VPC Endpoint and enable SSL for the endpoint

Question 15

A company uses AWS Glue ETL jobs to transform data from Amazon RDS for MySQL to Amazon S3. The transformation includes aggregations and joins. The job runs daily and processes approximately 100 GB of data. Recently, the job started failing with memory errors on the worker nodes. Which approach would MOST effectively resolve the issue without changing the logic?

Accepted Answer

Increase the number of workers in the job configuration. Option A is correct because increasing the number of workers distributes the workload and reduces memory pressure per node. Option B is wrong because reducing workers increases memory pressure. Option C is wrong because changing to G.1X increases memory per worker but may not be as cost-effective as adding more G.2X workers. Option D is wrong because Python shell is not suitable for large data transformations.

Answer

Switch from a Spark ETL job to a Python shell job

Answer

Decrease the number of workers to reduce overhead

Answer

Change the worker type from G.2X to G.1X to increase memory per worker