MLS-C01 Data Engineering Practice Test 6 — 15 Questions

Question 1

A data engineer runs the CLI command to download an object from S3. The bucket owner is 123456789012, and the engineer's IAM user has s3:GetObject permission on the bucket. The object was uploaded by a different AWS account. What is the MOST likely reason for the AccessDenied error?

Accepted Answer

The object is owned by a different AWS account, and the bucket owner has not been granted access. By default, objects uploaded by another account are owned by the uploading account, and the bucket owner does not have access unless explicitly granted via bucket policy or ACL. The --expected-bucket-owner parameter only checks the bucket owner, not the object. The engineer's permissions are on the bucket, but the object is owned by another account, so the bucket owner needs additional permissions.

Answer

The --expected-bucket-owner parameter is incorrect

Answer

The bucket policy denies access to the engineer's IAM user

Answer

The IAM policy does not allow s3:GetObject for that specific key

Question 2

An S3 event notification is configured to trigger a Lambda function when new objects are created. The Lambda function processes the event JSON shown. Which field should the function use to read the new object from S3?

Accepted Answer

s3.bucket.arn and s3.object.key. The event JSON contains the bucket name under s3.bucket.name and the object key under s3.object.key. The function should use these to construct the S3 URI and read the object. The other fields are not sufficient.

Answer

s3.s3SchemaVersion

Answer

awsRegion

Answer

eventName

Question 3

A data engineer needs to ingest streaming data from an on-premises Kafka cluster into Amazon S3 with minimal operational overhead. Which AWS service should be used to stream the data into S3 without managing servers?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is the correct service for loading streaming data into S3 without managing servers. Option A (Amazon Kinesis Data Streams) requires a consumer to process data; Option B (Amazon MSK) is a managed Kafka service but still requires management; Option D (AWS Glue) is for ETL jobs, not real-time streaming.

Answer

Amazon Kinesis Data Streams

Answer

AWS Glue

Answer

Amazon Managed Streaming for Apache Kafka (Amazon MSK)

Question 4

A company is using AWS Glue ETL jobs to process data stored in Amazon S3. The jobs currently run sequentially and take too long. The data engineer wants to reduce job duration without rewriting the code. Which action is most effective?

Accepted Answer

Increase the number of DPUs (Data Processing Units) for the job. Increasing the number of DPUs (Data Processing Units) allocated to the Glue job can parallelize the processing and reduce runtime without code changes. Option A (changing to a larger instance type) is not applicable because Glue uses DPUs, not EC2 instances. Option B (using a different data format) may help but is not a direct solution for parallelization. Option D (enabling job bookmarks) helps with incremental processing but does not speed up the existing job.

Answer

Change the underlying EC2 instance type to a compute-optimized instance

Answer

Convert the data from CSV to Parquet format

Answer

Enable job bookmarks to skip already processed data

Question 5

A data science team uses Amazon SageMaker to train models on a dataset stored in Amazon S3. The dataset is 2 TB and is accessed by multiple training jobs. The team notices that training jobs are slow due to high S3 GET request latency. Which solution would provide the fastest and most cost-effective data access?

Accepted Answer

Mount an Amazon FSx for Lustre file system integrated with the S3 bucket. Using Amazon FSx for Lustre as a managed Lustre file system with S3 integration provides high-throughput, low-latency access for training jobs. Option A (Cluster Placement Group) reduces network latency but does not improve S3 access. Option B (Elastic Fabric Adapter) improves inter-node communication, not data access from S3. Option D (S3 Transfer Acceleration) improves upload speed, not GET latency for large datasets.

Answer

Place all training instances in a Cluster Placement Group

Answer

Enable S3 Transfer Acceleration on the bucket

Answer

Use Elastic Fabric Adapter (EFA) for training instances

Question 6

A company runs a daily ETL job that reads data from Amazon RDS, transforms it using AWS Glue, and writes the results to Amazon S3. The job started failing yesterday with the error: 'Rate exceeded'. What is the most likely cause and solution?

Accepted Answer

The RDS database is overwhelmed by the number of connections; reduce the Glue job's parallelism or increase RDS instance size. The 'Rate exceeded' error typically indicates that the job is exceeding the RDS database's maximum connections or IOPS limits. The best solution is to reduce the parallelism or increase the database capacity. Option A (increasing S3 bucket limits) does not address the RDS issue. Option B (increasing Glue DPUs) may exacerbate the issue. Option D (enabling Glue job bookmarks) does not solve rate limiting.

Answer

The Glue job is using too many DPUs; reduce the number of DPUs

Answer

The S3 bucket has reached its request rate limit; request a limit increase

Answer

Enable job bookmarks in the Glue job to process only new data

Question 7

A company wants to analyze historical data stored in Amazon S3 using Amazon Athena. The data is in CSV format and is partitioned by date. Which action will provide the best query performance and cost optimization?

Accepted Answer

Convert the data to Parquet format and use the existing partition structure. Converting data to Parquet and partitioning improves query performance and reduces cost because Athena scans less data. Option B (only partitioning) helps but Parquet is columnar and more efficient. Option C (increasing S3 events) is irrelevant. Option D (using Glue to compress) may help but Parquet already includes compression.

Answer

Use AWS Glue to compress the CSV files with gzip

Answer

Create an S3 event notification to trigger a Lambda function that warms up Athena

Answer

Keep CSV format but ensure partitions are in the format year=YYYY/month=MM/day=DD

Question 8

A company uses AWS Lake Formation to manage permissions on a data lake stored in Amazon S3. A data analyst tries to query a table using Amazon Athena but receives an 'Access Denied' error. The analyst has SELECT permission on the table in Lake Formation. What is the most likely cause?

Accepted Answer

The S3 bucket is not registered with Lake Formation. Lake Formation requires that the underlying S3 bucket be registered with Lake Formation and that the IAM role used by Athena has Lake Formation permissions. If the S3 bucket is not registered, Lake Formation cannot grant access to it. Option A (missing IAM actions) could also be a cause, but the most common issue is that the bucket is not registered. Option B (KMS key) not likely. Option D (no partition) would not cause Access Denied.

Answer

The S3 bucket is encrypted with a KMS key that the analyst does not have access to

Answer

The table does not have any partitions defined

Answer

The IAM role used by Athena does not have lakeformation:GetDataAccess permission

Question 9

A data pipeline uses Amazon Kinesis Data Streams with a Lambda consumer to process clickstream data. The Lambda function sometimes times out because of spikes in traffic. The team wants to buffer the data before processing to handle spikes. Which approach is most effective?

Accepted Answer

Use Kinesis Data Firehose as an intermediary that buffers data and delivers to Lambda in batches. Using Kinesis Data Firehose between the stream and Lambda buffers data and can deliver it to Lambda in batches, reducing timeouts. Option A (increasing Lambda timeout) may help but is not a buffer. Option B (increasing shards) increases throughput but does not buffer. Option D (using SQS) adds complexity and delay.

Answer

Send the data to an Amazon SQS queue and have Lambda poll from it

Answer

Increase the number of shards in the Kinesis stream

Answer

Increase the Lambda function timeout to 15 minutes

Question 10

A company runs a nightly AWS Glue ETL job that processes data from an Amazon Redshift cluster and writes to Amazon S3. The job fails intermittently with 'ERROR: cannot execute INSERT in a read-only transaction'. What is the most likely cause?

Accepted Answer

The Redshift cluster is in a read-only state due to a failover or maintenance. This error occurs when the Glue job tries to write to a Redshift table that is in a read-only transaction, often because the Redshift cluster is in a read-only state due to a failover or maintenance. Option A (JDBC timeout) gives a different error. Option B (connection pool) would give a different error. Option D (IAM permissions) would give an access denied error.

Answer

The IAM role used by Glue does not have permissions to insert into Redshift

Answer

The JDBC driver version is incompatible with the Redshift cluster

Answer

The Glue job's connection pool is exhausted

Question 11

A company uses Amazon EMR to run Spark jobs on a large dataset stored in Amazon S3. The jobs are failing with 'OutOfMemoryError' in the executors. The data is not skewed. Which configuration change will most likely resolve the issue?

Accepted Answer

Increase the spark.executor.memoryOverhead setting. Increasing the executor memory overhead provides additional memory for JVM overhead and can prevent OutOfMemoryError. Option A (increasing cores) may increase parallelism but not memory. Option B (decreasing shuffle partitions) may reduce memory usage but is not a direct fix. Option D (using Kryo serialization) reduces memory usage but is not as effective as increasing overhead.

Answer

Enable Kryo serialization

Answer

Decrease the number of shuffle partitions

Answer

Increase the number of executor cores

Question 12

A data engineer is designing a data ingestion pipeline that will receive up to 5 GB of data per hour from thousands of IoT devices. The data must be stored in Amazon S3 and analyzed in near real-time. Which TWO services should be used together to meet these requirements? (Choose TWO.)

Accepted Answer

Amazon Kinesis Data Analytics. Amazon Kinesis Data Firehose can ingest streaming data and deliver to S3. Amazon Kinesis Data Analytics can perform near real-time analysis on the data stream. Option A (Amazon SQS) is for decoupling applications, not streaming analytics. Option B (AWS Lambda) can be used for processing but is not a streaming analytics service. Option E (Amazon Athena) is for ad-hoc queries on S3, not real-time.

Answer

AWS Lambda

Answer

Amazon Athena

Answer

Amazon Simple Queue Service (Amazon SQS)

Question 13

A company needs to transfer 10 TB of data from an on-premises data center to Amazon S3. The network bandwidth is limited to 100 Mbps, and the transfer must complete within 5 days. Which TWO options are viable? (Choose TWO.)

Accepted Answer

Use AWS Snowball Edge device to ship the data. AWS Snowball Edge is a physical device for large data transfers over slow networks. AWS DataSync can be used if there is some bandwidth, but with 100 Mbps, it would take ~10 days, exceeding the 5-day window. Option B (S3 Transfer Acceleration) uses existing internet, which is still limited. Option C (Direct Connect) would require setup time and may not meet the window. Option E (S3 Multipart Upload) is for uploading files, not for bulk transfer over slow link.

Answer

Use S3 Transfer Acceleration to speed up the transfer

Answer

Use S3 Multipart Upload to upload files in parallel

Answer

Use AWS DataSync over the existing internet connection

Answer

Use AWS Direct Connect to establish a dedicated network connection

Question 14

A company uses Amazon Redshift for data warehousing. The data engineering team notices that query performance has degraded over time. Which THREE actions should the team take to improve performance? (Choose THREE.)

Accepted Answer

Define appropriate sort keys on large tables. Sort keys help the query optimizer scan less data. Distribution keys reduce data shuffling. ANALYZE commands update statistics for the optimizer. Option B (increasing node count) is costly and not always necessary. Option D (deleting old data) may help but is not a direct performance tuning technique.

Answer

Increase the number of nodes in the Redshift cluster

Answer

Delete old data that is no longer needed

Question 15

A data engineer is building a data pipeline that ingests streaming data from IoT devices. The data must be processed in near real-time and stored in Amazon S3 for further analysis. Which AWS service should be used to capture and process the streaming data before storing it in S3?

Accepted Answer

Use Amazon Kinesis Data Streams to capture the data and Amazon Kinesis Data Firehose to deliver it to S3.. Amazon Kinesis Data Streams is designed for real-time data ingestion and can be integrated with Lambda for processing. Kinesis Data Firehose can then load the data into S3. Option A is wrong because S3 is a storage service, not a streaming ingestion service. Option B is wrong because AWS Glue is for ETL and cataloging, not real-time streaming. Option D is wrong because SQS is a message queue, not optimized for streaming analytics.

Answer

Use Amazon S3 with S3 Event Notifications to trigger AWS Lambda for processing.

Answer

Use AWS Glue to perform ETL on the streaming data.

Answer

Use Amazon Simple Queue Service (SQS) to buffer the data and then process it with AWS Lambda.