MLS-C01 Practice Exam 53 — 20 Questions

Question 1

A data engineer is troubleshooting an AWS Glue job that reads from and writes to the S3 bucket 'data-lake-bucket'. The job fails when trying to write to the 'sensitive/' prefix. The IAM policy attached to the Glue job's IAM role is shown in the exhibit. What is the MOST likely reason for the failure?

Accepted Answer

The IAM role has an explicit deny for s3:PutObject on the 'sensitive/' prefix. Option B is correct. Even though the first statement allows s3:PutObject on the entire bucket, the second statement explicitly denies s3:PutObject on the 'sensitive/' prefix. Explicit deny overrides any allow. Option A is wrong because the policy allows GetObject. Option C is wrong because the policy covers the bucket. Option D is wrong because there is a deny statement.

Answer

The IAM role does not have permission to read objects from the bucket

Answer

The IAM policy does not specify the bucket resource correctly

Answer

The IAM policy lacks a required condition for encryption

Question 2

A company is using AWS Glue to run ETL jobs that process data from Amazon RDS to Amazon S3. The ETL jobs are failing intermittently with write timeout errors when writing to S3. The company wants to implement a retry mechanism for transient errors. What should the company do?

Accepted Answer

Configure the AWS Glue job to retry on failure by setting the 'Max retries' parameter. Option D is correct because configuring job retry in AWS Glue automatically retries failed jobs. Option A is wrong because CloudWatch alarms do not retry. Option B is wrong because SQS is not integrated with Glue jobs. Option C is wrong because increasing EBS volume does not address S3 write errors.

Answer

Increase the size of the Amazon EBS volumes attached to the Glue job

Answer

Use Amazon CloudWatch to monitor the job and manually restart on failure

Answer

Place the failed job messages in an Amazon SQS queue and reprocess them

Question 3

A company uses Amazon Kinesis Data Streams to ingest clickstream data. The stream has 8 shards. A Lambda function processes each record and writes to Amazon DynamoDB. The Lambda function sometimes fails due to DynamoDB write throttling, causing duplicate processing of records after retries. The data engineering team needs to ensure exactly-once processing semantics for the DynamoDB writes. What should the team do?

Accepted Answer

Use DynamoDB conditional writes with the Kinesis sequence number as a unique attribute to make writes idempotent.. Option B is correct because DynamoDB transactions allow conditional writes. Using the Kinesis sequence number as a condition check ensures idempotency: if a record with that sequence number already exists, the write is skipped. Option A is wrong because increasing write capacity may reduce throttling but does not guarantee exactly-once; duplicates can still occur. Option C is wrong because SQS delays are for visibility timeout, not idempotency. Option D is wrong because FIFO queues support exactly-once delivery but the Lambda would still need to deduplicate within the batch.

Answer

Use an Amazon SQS FIFO queue between Kinesis and Lambda to deduplicate records.

Answer

Configure the Lambda event source mapping with a maximum retry count of 0 and a DLQ.

Answer

Increase the DynamoDB write capacity units to avoid throttling.

Question 4

A data engineer runs the AWS CLI command shown and notices a zero-byte file in the results. What is the most likely cause of this zero-byte file?

Accepted Answer

The file was created by a failed Spark task that wrote no data.. Zero-byte files often occur when an ETL job fails partway through writing, or when a task starts but writes no data. A completed write would have non-zero size. The other options are less likely: prefix typo wouldn't produce a file; correct permissions wouldn't cause zero bytes; compression would produce some output.

Answer

The S3 bucket has a lifecycle policy that deleted the content.

Answer

The file was written with the wrong prefix.

Answer

The file was compressed, reducing size to zero.

Question 5

A company needs to move 10 TB of data from an on-premises NAS to Amazon S3 over a 100 Mbps internet connection. The transfer must complete within 3 days. Which solution is the most appropriate?

Accepted Answer

Use AWS Snowball Edge to transfer the data. Option D is correct because AWS Snowball Edge is a physical device that can transfer large data volumes faster than internet. Option A is wrong because over 100 Mbps, 10 TB would take ~10 days; Option B is wrong because AWS DataSync requires network; Option C is wrong because S3 Transfer Acceleration improves speed only up to ~200% at best, still insufficient.

Answer

Use AWS DataSync to transfer over the internet

Answer

Enable S3 Transfer Acceleration on the bucket

Answer

Use AWS CLI to copy data directly over the internet

Question 6

A company is using Amazon Kinesis Data Firehose to load streaming data into an S3 bucket. The data schema evolves over time, with new columns added. The data must be queryable using Amazon Athena. What is the BEST way to handle schema changes?

Accepted Answer

Use AWS Glue Crawlers to automatically detect schema changes and update the table metadata. Option D is correct. Using Glue Crawlers to update the schema and partitioning by date allows Athena to handle schema evolution gracefully. Option A (schema-on-read) is how Athena works, but manual updates are not needed. Option B (convert to JSON) is not necessary. Option C (recreate table) is disruptive.

Answer

Manually update the Athena table definition each time a new column is added

Answer

Configure Firehose to convert the data to Apache JSON format

Answer

Recreate the Athena table daily to pick up new columns

Question 7

A data engineer runs the AWS CLI command shown in the exhibit to find large log files in S3. The command returns an empty list, but the engineer knows there are files larger than 1 MB in that prefix. What is the MOST likely issue?

Accepted Answer

The command does not paginate through all objects; only the first 1000 are returned. Option A is correct. The list-objects command returns up to 1000 objects per call. If there are more objects, pagination is needed. Option B is incorrect because the query syntax is correct. Option C is incorrect because the prefix is fine. Option D is incorrect because the unit is bytes, so 1000000 is 1 MB.

Answer

The JMESPath query syntax is incorrect

Answer

The prefix is incorrect; there are no objects under that prefix

Answer

The Size value is in kilobytes, not bytes

Question 8

A company is migrating its on-premises Apache Hadoop cluster to AWS. The cluster processes large datasets using Spark jobs. The company wants to minimize operational overhead and use native AWS services. Which combination of services should the company use?

Accepted Answer

Amazon EMR with Spark and Amazon S3. Option B is correct because Amazon EMR is a managed Hadoop framework that supports Spark, and S3 is a scalable storage layer. Option A is wrong because EC2 would require managing the cluster manually. Option C is wrong because Redshift is a data warehouse, not a Hadoop replacement. Option D is wrong because Athena is for querying, not for running Spark jobs.

Answer

Amazon Redshift with Spectrum and Amazon S3

Answer

Amazon Athena and AWS Glue

Answer

Amazon EC2 instances with Apache Spark installed and Amazon S3

Question 9

A data pipeline uses AWS Glue to crawl an S3 bucket and create a table in the AWS Glue Data Catalog. The data is in Parquet format with partitions by date. After a new partition is added to S3, the crawler runs but the new partition is not reflected in the table. What is the most likely cause?

Accepted Answer

The new partition folder does not follow the Hive-style partition naming convention expected by the crawler.. Option A is correct because the Glue crawler may be configured to only crawl new folders if the crawler's configuration is set to 'Crawl new folders only' but the partition path may not match the expected pattern, or the crawler's 'Schema updates' setting might be set to 'Ignore'. More commonly, the crawler's 'Update the table definition' option is set to 'Add new columns only' or 'Ignore the change and don't update the table'; the correct setting to add partitions is 'Add new columns only' or 'Add new partitions only'. However, the most typical issue is that the crawler is set to 'Crawl all folders each time' and still not picking up because the partition path is not in a recognized Hive-style format (e.g., date=2023-01-01/). Option A points to the partition path format. Option B is wrong because Glue can handle Parquet. Option C is wrong because the crawler does not need Lambda triggers. Option D is wrong because the crawler can handle up to many partitions.

Answer

The crawler requires an AWS Lambda trigger to be configured for new partitions.

Answer

The Parquet schema in the new partition does not match the existing table schema.

Answer

The S3 bucket has too many partitions, exceeding the Glue crawler limit.

Question 10

A company uses Amazon EMR to run Spark jobs on a cluster with 10 core nodes of type r5.xlarge. The jobs are I/O intensive and read large amounts of data from S3. The team notices high network throughput but low CPU utilization. Which configuration change would improve job performance at the same cost?

Accepted Answer

Replace the core nodes with r5d.xlarge instances that have local SSDs.. Option B is correct because r5d instances include local NVMe SSDs, which can be used for caching intermediate data, reducing network I/O and improving performance for I/O intensive jobs. Option A is wrong because increasing core nodes increases cost. Option C is wrong because using spot instances reduces cost but not performance. Option D is wrong because moving to m5 instances (general purpose) may not improve I/O.

Answer

Change the instance type to m5.xlarge (general purpose) to balance resources.

Answer

Increase the number of core nodes to 20.

Answer

Use spot instances for the core nodes to save cost and reinvest in more nodes.

Question 11

A company uses AWS Glue to run ETL jobs that transform data from Amazon RDS for MySQL to Amazon S3. The current job runs daily and takes 3 hours to process 100 GB of data. The company expects data volume to grow 10x in the next year. They need to reduce job runtime and cost. Which approach should they take?

Accepted Answer

Use parallel reads with pushdown predicates in the Glue job's source connection, and write the output in columnar format (Parquet) partitioned by date.. Option D is correct because parallel reads from RDS with pushdown predicates reduce the load on the source and speed up extraction; using columnar formats like Parquet reduces storage and scanning costs in Athena. Option A is wrong because increasing DPUs without changing the extraction method may not help if the bottleneck is the source database. Option B is wrong because S3 Select is for server-side filtering, not for Glue jobs. Option C is wrong because Redshift Spectrum is for querying data in S3, not for transforming it.

Answer

Use S3 Select with Glue to filter data before transformation.

Answer

Increase the number of Glue DPUs to 100 and enable job bookmarking.

Answer

Use Amazon Redshift Spectrum to perform transformations in place on S3.

Question 12

A data scientist is training a deep learning model on a GPU instance. The training data is stored in S3 and is 50 GB. To reduce I/O bottlenecks, which storage option should be used to cache the data locally on the instance?

Accepted Answer

Mount an Amazon FSx for Lustre file system linked to the S3 bucket. Option A is correct because Amazon FSx for Lustre provides a high-performance file system integrated with S3 that can cache data locally. Option B is wrong because EBS with io2 volumes offers high IOPS but is not optimized for S3 caching; Option C is wrong because EFS is a shared file system with lower throughput; Option D is wrong because Instance Store is ephemeral and not persistent.

Answer

Attach an Amazon EFS file system to the instance and copy data from S3

Answer

Provision an Amazon EBS io2 volume and copy data from S3 using AWS DataSync

Answer

Use instance store volumes to cache the data from S3

Question 13

A data engineer is configuring an IAM policy to allow users to upload objects to an S3 bucket only if the objects are encrypted using SSE-S3. However, users are getting AccessDenied errors when uploading objects without specifying encryption. What is the most likely cause?

Accepted Answer

The condition requires encryption to be specified, but the upload does not specify it. Option B is correct because the policy allows PutObject only when encryption is AES256, but denies when no encryption is specified because the condition is not met. Option A is wrong because it's not a service control policy; Option C is wrong because the bucket policy is not shown; Option D is wrong because the condition checks for AES256, not KMS.

Answer

The condition should check for aws:SourceIp instead of encryption

Answer

The policy is attached to the wrong IAM user

Answer

The bucket policy denies all PutObject without encryption

Question 14

A company uses Amazon Redshift for data warehousing. The data engineering team needs to load data from multiple S3 buckets into Redshift daily. Each bucket contains files in different formats (CSV, JSON, Parquet). Which AWS service is BEST suited to automate this ingestion process?

Accepted Answer

AWS Glue. Option A is correct. AWS Glue can crawl the S3 buckets to discover schema and run ETL jobs to transform and load data into Redshift. Option B (DMS) is for database migration. Option C (EMR) requires more management. Option D (Data Pipeline) is less flexible.

Answer

Amazon EMR with Apache Spark

Answer

AWS Data Pipeline

Answer

AWS Database Migration Service (DMS)

Question 15

A company is designing a data pipeline to process log files from multiple sources. The logs are written to Amazon S3 every hour. The data is then transformed using AWS Glue ETL jobs and loaded into Amazon Redshift for analysis. The company needs to ensure that the data is available for analysis within 30 minutes of being written to S3. Currently, the Glue job is triggered hourly, but the company wants to reduce the latency. Which solution should the company implement?

Accepted Answer

Use Amazon S3 event notifications to invoke an AWS Lambda function that starts the Glue job automatically. Option C is correct because configuring an S3 event notification to invoke AWS Lambda, which starts the Glue job, allows near-real-time processing within minutes. Option A is wrong because hourly triggers do not reduce latency. Option B is wrong because increasing the crawler frequency does not trigger ETL jobs. Option D is wrong because Redshift Spectrum does not transform data.

Answer

Increase the frequency of the Glue crawler to run every 5 minutes

Answer

Use Amazon Redshift Spectrum to query the data directly from S3 without transformation

Answer

Reduce the Glue job trigger frequency to every 15 minutes

Question 16

Refer to the exhibit. A data engineer is creating an IAM policy for an AWS Glue ETL job that reads encrypted objects from an S3 bucket, transforms them, and writes the results back to the same bucket. The bucket uses SSE-KMS encryption with the KMS key specified. The ETL job is failing with an "Access Denied" error when trying to write data. What is the likely cause?

Accepted Answer

The policy is missing the kms:Encrypt permission. Option C is correct because the policy grants s3:PutObject, which is needed to write, but the KMS permissions include kms:Decrypt and kms:GenerateDataKey, which are sufficient for reading and writing with SSE-KMS. The issue is that the job role must also have kms:Encrypt to write encrypted objects. Option A is wrong because the policy includes s3:PutObject. Option B is wrong because the policy includes both KMS actions needed for reading. Option D is wrong because there is no s3:PutObject condition missing.

Answer

The policy is missing the kms:Decrypt permission

Answer

The policy is missing the s3:PutObjectAcl permission

Answer

The policy is missing the s3:PutObject permission

Question 17

A company runs a critical data pipeline using Apache Spark on Amazon EMR. The pipeline reads data from Amazon S3, performs complex transformations, and writes results back to S3. The job runs every hour and must complete within 30 minutes. Recently, the job has been taking longer and occasionally failing due to executor losses. The team suspects memory pressure. Which action should the team take to improve stability and performance without increasing cost?

Accepted Answer

Enable Spark dynamic allocation to adjust executors based on workload.. Option C is correct because enabling dynamic allocation allows Spark to release idle executors and request more when needed, reducing memory pressure. Option A is wrong because increasing the number of core nodes increases cost. Option B is wrong because increasing executor memory per node may cause YARN containers to fail if the instance memory is exceeded. Option D is wrong because reducing shuffle partitions may reduce parallelism and increase memory per task.

Answer

Increase the spark.executor.memory setting to allocate more memory per executor.

Answer

Increase the number of core nodes in the EMR cluster.

Answer

Decrease the number of shuffle partitions (spark.sql.shuffle.partitions) to reduce overhead.

Question 18

An AWS Glue ETL job failed with the error 'Insufficient memory allocated for the job'. The job run details show AllocatedCapacity: 5, WorkerType: Standard, NumberOfWorkers: 5. Which change should be made to resolve the issue?

Accepted Answer

Increase the number of workers to 10. Option C is correct because the error indicates insufficient memory; increasing the number of workers (DPUs) provides more memory. Option A is wrong because worker type Standard has 16 GB memory; G.2X has 32 GB, but the error is about allocated capacity, not worker type. Option B is wrong because job timeout is not the issue. Option D is wrong because starting a new job won't fix the resource allocation.

Answer

Delete and recreate the job with a different name

Answer

Increase the job timeout to 3600 minutes

Answer

Change the worker type to G.2X

Question 19

A data engineer is designing a data lake on Amazon S3. The data comes from various sources and must be stored in a way that supports both batch and real-time analytics. The engineer needs to partition the data to optimize query performance in Amazon Athena. Which partitioning strategy is MOST appropriate?

Accepted Answer

Partition by year, then month, then day, then hour. Option C is correct. Partitioning by year/month/day/hour allows efficient querying for both batch (daily) and real-time (hourly) use cases, and is a common practice. Option A (source type) may cause small files. Option B (random) is not helpful. Option D (single partition) defeats the purpose.

Answer

Partition by a hash of the record ID to distribute data evenly

Answer

Do not partition; use a single prefix for all data

Answer

Partition by source type and then by date

Question 20

A company stores sensitive customer data in Amazon S3. The company must ensure that data is encrypted at rest. The company also needs to manage the encryption keys using an AWS service that allows automatic rotation of keys. Which solution meets these requirements?

Accepted Answer

Use Server-Side Encryption with AWS KMS-Managed Keys (SSE-KMS) and enable automatic key rotation. Option B is correct because AWS KMS with automatic key rotation meets the encryption and key management requirements. Option A is wrong because SSE-S3 uses S3-managed keys that cannot be rotated manually. Option C is wrong because SSE-C requires the customer to manage keys. Option D is wrong because CloudHSM is a hardware security module but does not automatically rotate keys.

Answer

Use client-side encryption with AWS CloudHSM

Answer

Use Server-Side Encryption with Customer-Provided Keys (SSE-C)

Answer

Use Server-Side Encryption with Amazon S3-Managed Keys (SSE-S3)

MLS-C01 Practice Exam 53 — 20 Questions

Exhibit

MLS-C01 Practice Exam 53 — 20 Questions

Exhibit