MLS-C01 Practice Exam 55 — 20 Questions

Question 1

A data scientist is using Amazon SageMaker to train a model. The training data is stored in Amazon S3 and is approximately 500 GB. The data scientist notices that the training job is taking a long time to start because the data is being copied to the training instance's storage. The data scientist wants to reduce the startup time for subsequent training jobs. Which action should the data scientist take?

Accepted Answer

Use Pipe input mode instead of File input mode for the training job. Option A is correct because using Pipe input mode streams data directly from S3 to the training algorithm without downloading, reducing startup time. Option B is wrong because FSx for Lustre is not needed for simple streaming. Option C is wrong because increasing instance storage does not address the data transfer issue. Option D is wrong because using EBS optimized instances does not change the data loading mechanism.

Answer

Use an EBS-optimized instance type

Answer

Use Amazon FSx for Lustre as a high-performance file system mounted to the training instance

Answer

Increase the size of the training instance's Amazon EBS storage volume

Question 2

A company needs to move 50 TB of data from an on-premises data center to Amazon S3. The company has a limited internet bandwidth of 100 Mbps. The data transfer must be completed within 10 days. Which TWO services should the company use together to meet these requirements?

Accepted Answer

Amazon S3 as the destination. Options A and E are correct. AWS Snowball Edge is a physical device for large data transfer, and S3 is the destination. Option B is wrong because internet transfer at 100 Mbps would take more than 50 days. Option C is wrong because Direct Connect is for dedicated network, not for physical transfer. Option D is wrong because VPN is not designed for bulk data transfer.

Answer

AWS Direct Connect

Answer

AWS Site-to-Site VPN

Answer

AWS DataSync over the internet

Question 3

A company runs a nightly batch job that reads data from Amazon RDS for PostgreSQL, transforms it using AWS Glue, and writes the output to Amazon S3 in Parquet format. The job takes 2 hours to complete, but the data volume has grown, and the job now takes 4 hours, exceeding the allowed window. The team needs to reduce the job duration without increasing cost. Which action is MOST effective?

Accepted Answer

Partition the output S3 data by a high-cardinality column used in filtering during transformation.. Option D is correct because partitioning the Parquet output by a frequently filtered column reduces the amount of data processed downstream and can speed up the Glue job if the transformation includes filtering. However, the question asks to reduce job duration: increasing parallelism by increasing the number of DPUs (option C) would increase cost, but the team does not want to increase cost. Option A is wrong because Glue's job bookmark can help incremental loads but does not speed up the full load. Option B is wrong because converting to CSV may increase size and processing time. Option D is the best: partitioning the output can improve write performance and reduce data volume for subsequent steps, but note that it might not drastically reduce the job duration itself. Actually, the most effective without increasing cost is to optimize the transformation logic. But among given options, D is plausible. Let's reconsider: Option C increases cost. Option D partitions output, which may slightly reduce write time but not transformation time. Option A and B are not effective. Perhaps the best is to use a larger instance with same cost? Not possible. Option D is the only one that does not increase cost and may help. I'll stick with D.

Answer

Increase the number of Glue DPUs (Data Processing Units) allocated to the job.

Answer

Enable AWS Glue job bookmark to skip already processed data.

Answer

Change the output format from Parquet to CSV to reduce write time.

Question 4

A company uses Amazon Kinesis Data Firehose to deliver data to Amazon S3. The data must be transformed before delivery using AWS Lambda. The Lambda function adds a timestamp field. The Firehose stream receives up to 10,000 records per second. The transformation currently takes 500 ms per record. What should the team do to ensure the transformation can keep up with the incoming data without data loss?

Accepted Answer

Increase the Lambda concurrency limit for the function to handle parallel invocations.. Option B is correct because increasing the Lambda concurrency limit ensures that multiple Lambda invocations can run in parallel to handle the high throughput. Option A is wrong because increasing the buffer size would cause delays and potential data loss if the buffer fills up. Option C is wrong because increasing the number of shards applies to Kinesis Data Streams, not Firehose. Option D is wrong because Lambda functions in a VPC may have reduced network performance and are not needed for adding a timestamp.

Answer

Increase the number of shards in the Kinesis stream.

Answer

Place the Lambda function in a VPC to improve network performance.

Answer

Increase the S3 buffer size and buffer interval in the Firehose delivery stream.

Question 5

A company uses AWS Glue crawlers to populate the AWS Glue Data Catalog from Amazon S3. The data is partitioned by year/month/day/hour. The crawler runs every hour and adds new partitions. However, the data engineer notices that the crawler is taking longer to run as the number of partitions grows, and sometimes it misses new partitions. What is the most cost-effective and reliable way to address this?

Accepted Answer

Enable the crawler's partition index feature.. Option B is correct because enabling the crawler's partition index feature allows Glue to quickly find new partitions without re-scanning the entire table. Option A is wrong because increasing the crawler's frequency will not help if the crawler is already missing partitions due to scanning overhead. Option C is wrong because manually adding partitions is error-prone and does not scale. Option D is wrong because Athena MSCK REPAIR TABLE is a manual command and requires running it after data arrives, not automated.

Answer

Manually add new partitions using ALTER TABLE ADD PARTITION in Athena.

Answer

Use the Athena MSCK REPAIR TABLE command after the crawler runs.

Answer

Increase the crawler's schedule to run every 30 minutes.

Question 6

A data engineer is designing an ETL pipeline using AWS Glue to process data from Amazon S3 and load it into Amazon Redshift. The pipeline must handle incremental data loads and ensure data consistency. Which THREE features should the engineer use to achieve this? (Choose THREE.)

Accepted Answer

Pushdown predicates to filter partitions in S3. Option A (job bookmark) enables incremental processing. Option C (pushdown predicate) reduces data scanned. Option E (transactional table) ensures consistency. Option B (partition filter) is less efficient. Option D (data preview) is for development.

Answer

Glue data preview to validate transformation logic

Answer

Glue partition filters to limit data scanned

Question 7

A data engineering team is designing a data lake on Amazon S3. The data is ingested from multiple sources in JSON, CSV, and Parquet formats. The team needs to make the data available for analysis using Amazon Athena and Amazon Redshift Spectrum. The team wants to minimize data transformation costs and storage overhead. Which data storage approach should the team use?

Accepted Answer

Convert all data to Apache Parquet before storing in S3. Option B is correct because storing data in columnar formats like Parquet reduces storage and improves query performance. Option A is wrong because storing all data as raw JSON inflates storage. Option C is wrong because converting data to a single format increases transformation costs. Option D is wrong because using a relational database is not a data lake approach.

Answer

Load the data into Amazon Redshift cluster and then unload to S3 in Parquet

Answer

Store the data in its original format in S3 and use Athena to query directly

Answer

Store the data in its original format and use AWS Glue to convert to Parquet when queried

Question 8

A company is streaming e-commerce events to Amazon Kinesis Data Streams. The data science team needs to join events from multiple shards in near real-time and then store the joined results in Amazon S3. Which solution would meet these requirements with the LEAST operational overhead?

Accepted Answer

Use Amazon Kinesis Data Analytics for Apache Flink to read from the Kinesis stream, perform a join operation using Flink SQL, and write the results to S3 using a sink connector.. Option C is correct because Amazon Kinesis Data Analytics for Apache Flink can read from a Kinesis stream, perform stateful joins across shards using Flink's SQL or DataStream API, and write results to S3. Option A is wrong because while Glue ETL can process data, it is batch-oriented and not designed for near real-time streaming joins. Option B is wrong because Lambda with Kinesis triggers processes each shard independently; joining across shards would require external state management and is not a typical pattern. Option D is wrong because Kinesis Data Firehose cannot perform joins; it only writes data to destinations.

Answer

Use AWS Lambda functions with Kinesis triggers to process each record, join across shards using a DynamoDB table for state, and write to S3.

Answer

Use Amazon Kinesis Data Firehose to buffer the data and write to S3, then use Amazon Athena to join the data after it is stored.

Answer

Use AWS Glue ETL jobs that read from the Kinesis stream via the Kinesis connector and write the joined results to S3.

Question 9

A data engineer needs to transfer 50 TB of data from an on-premises HDFS cluster to Amazon S3. The data must be encrypted in transit and at rest. The on-premises network has a 1 Gbps connection to AWS. The transfer must complete within 5 days. Which solution is MOST cost-effective and meets the requirements?

Accepted Answer

Use AWS DataSync with a DataSync agent installed on-premises to transfer the data to S3.. Option C is correct. With 1 Gbps, the maximum theoretical transfer in 5 days is about 54 TB (1 Gbps = 0.125 GB/s, 0.125 * 86400 * 5 = 54000 GB = 54 TB). So it is feasible. AWS DataSync can transfer data from HDFS via a private endpoint using the DataSync agent, with encryption in transit (TLS) and at rest (S3 SSE). Option A is wrong because S3 Transfer Acceleration only speeds up uploads over public internet, not from HDFS directly. Option B is wrong because Snowball Edge would be faster but more expensive for this volume that can fit in the time window. Option D is wrong because AWS Glue is for ETL, not data transfer.

Answer

Use S3 Transfer Acceleration to upload the data directly from HDFS to S3.

Answer

Order an AWS Snowball Edge device and copy the data to it, then ship it back.

Answer

Use AWS Glue to read from HDFS and write to S3 in a continuous ETL job.

Question 10

A company uses AWS Glue to run ETL jobs. The data engineer wants to monitor job performance and troubleshoot failures. Which THREE AWS services or features should they use together? (Choose three.)

Accepted Answer

AWS Glue job bookmarks. Correct options: A, C, E. CloudWatch Logs stores Glue job logs, CloudWatch metrics tracks job performance metrics, and Glue job bookmarks track processed data. Option B (Athena) is for querying, not monitoring. Option D (S3 Events) can trigger jobs but not monitor them.

Answer

Amazon S3 Event Notifications

Answer

Amazon Athena

Question 11

A Lambda function is triggered by S3 events. The event payload shown in the exhibit is received by the Lambda function. The function is supposed to process the CSV file and load it into DynamoDB. However, the function fails because it cannot read the file. What is the MOST likely cause?

Accepted Answer

The Lambda function's IAM role does not have s3:GetObject permission. Option C is correct. The Lambda function needs an IAM role with s3:GetObject permission to read the object. Option A is wrong because the event is valid. Option B is wrong because DynamoDB permissions are separate. Option D is wrong because the bucket exists.

Answer

The Lambda function lacks DynamoDB write permissions

Answer

The S3 bucket does not exist

Answer

The S3 event notification is misconfigured

Question 12

A company is building a data pipeline that uses Amazon Kinesis Data Streams to ingest real-time events. The pipeline then uses AWS Lambda to process the events and store results in Amazon DynamoDB. The company wants to ensure that the Lambda function can process all events without data loss and without duplicating processing. Which TWO configuration steps should the company take?

Accepted Answer

Set the Lambda function's batch window to a small value (e.g., 1 second) to reduce processing latency. Options A and C are correct. Setting the Lambda batch window to a small value ensures low latency, and enabling the iterator age metric helps monitor lag. Option B is wrong because increasing the retention period does not prevent duplication. Option D is wrong because using a single shard limits throughput. Option E is wrong because disabling retries could cause data loss.

Answer

Increase the data retention period of the Kinesis stream to 7 days to allow reprocessing

Answer

Use a single shard for the Kinesis stream to ensure order and avoid parallel processing issues

Answer

Configure the Lambda function to disable retries on failure to avoid duplicate processing

Question 13

A data engineering team uses Amazon EMR with Spark to transform large datasets in S3. The team notices that the Spark jobs on the EMR cluster are failing with out-of-memory errors. The cluster uses instance types with moderate memory. Which configuration change would MOST effectively reduce memory pressure without increasing cost?

Accepted Answer

Enable shuffle compression and set spark.shuffle.compress to true.. Option C is correct because enabling compression reduces the amount of data shuffled over the network and stored in memory, thus reducing memory usage. Option A is wrong because increasing the number of executor cores may increase parallelism but does not directly reduce memory per task. Option B is wrong because using more instances would increase cost. Option D is wrong because Kryo serialization reduces object size but is not as effective as compression for shuffle data.

Answer

Use a larger EMR cluster with more instances of the same type.

Answer

Increase the number of executor cores to improve parallelism.

Answer

Switch from Java serialization to Kryo serialization.

Question 14

A company wants to build a data lake on Amazon S3. The data lake will store raw data in its original format and also transformed data in Parquet. The data is generated by various sources and must be cataloged for discovery. Which service should the company use to automatically discover, catalog, and make the data searchable?

Accepted Answer

AWS Glue Data Catalog. Option D is correct. AWS Glue Data Catalog is a central metadata repository that can automatically crawl S3 data sources to populate the catalog. Option A is wrong because Athena is a query engine, not a catalog. Option B is wrong because EMR is a processing framework. Option C is wrong because S3 is storage.

Answer

Amazon S3

Answer

Amazon Athena

Answer

Amazon EMR

Question 15

A data engineer is tasked with building a pipeline to process streaming data from IoT devices. The devices send data in JSON format every second. The pipeline must aggregate data in 5-minute windows and store the results in Amazon S3. The engineer needs to handle late-arriving data (up to 1 hour) and ensure exactly-once semantics. Which combination of AWS services should they use?

Accepted Answer

Amazon Kinesis Data Streams for ingestion, Amazon Kinesis Data Analytics for windowed aggregations, and Amazon Kinesis Data Firehose to write to Amazon S3.. Option A is correct because Kinesis Data Analytics supports windowed aggregations, can handle late data via watermarking, and provides exactly-once processing when used with Kinesis Data Streams. Option B is wrong because Kinesis Data Firehose does not allow custom windowed aggregations. Option C is wrong because Glue Streaming is a batch-oriented service. Option D is wrong because Lambda does not have built-in support for exactly-once semantics for streaming applications.

Answer

Amazon Kinesis Data Streams for ingestion, AWS Glue Streaming ETL for aggregation, and Amazon S3 for storage.

Answer

Amazon SQS for ingestion, AWS Lambda for aggregation, and Amazon S3 for storage.

Answer

Amazon Kinesis Data Streams for ingestion, Amazon Kinesis Data Firehose for transformation, and Amazon S3 for storage.

Question 16

A data engineer is designing a data lake on Amazon S3. The data comes from various sources, including IoT devices, web logs, and transactional databases. The engineer needs to organize the data in a way that supports efficient querying using Amazon Athena and allows for easy management of access permissions. Which S3 bucket structure is the most appropriate?

Accepted Answer

Use a prefix structure like s3://bucket/source/year/month/day/.. Option B is correct because partitioning by source, year, month, day allows Athena to prune partitions, reducing scan costs and improving performance. Option A is wrong because storing all data in a flat structure forces full scans. Option C is wrong because prefix-based access controls can be applied at the source level within the partitioned structure. Option D is wrong because using date as the first partition level is less intuitive for managing permissions by source.

Answer

Store all data in a single prefix without any partitioning.

Answer

Store all data in separate S3 buckets for each source and date.

Answer

Use a prefix structure like s3://bucket/date/source/.

Question 17

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 the data must be transferred within 5 days. Which AWS service is best suited for this task?

Accepted Answer

AWS Snowball Edge. Option C is correct because AWS Snowball Edge is a physical device that can transfer large amounts of data over a network faster than a 100 Mbps connection. Option A is wrong because AWS DataSync is designed for network transfers, but 100 Mbps would take much longer than 5 days. Option B is wrong because AWS Storage Gateway is for ongoing hybrid cloud storage, not large-scale data migration. Option D is wrong because S3 Transfer Acceleration speeds up internet transfers but still limited by the 100 Mbps connection.

Answer

AWS DataSync

Answer

Amazon S3 Transfer Acceleration

Answer

AWS Storage Gateway

Question 18

A company runs a data lake on Amazon S3 with AWS Glue for ETL. The data science team needs to train machine learning models on historical data, but they are concerned about data quality issues such as missing values, duplicates, and outliers. The team wants to build a data quality monitoring solution that automatically detects anomalies and alerts the data engineering team. Which THREE steps should the team take to implement this solution? (Choose THREE.)

Accepted Answer

Use AWS Glue DataBrew to create data quality rules that check for missing values, duplicates, and outliers, and schedule them to run regularly.. Option A is correct because AWS Glue DataBrew provides built-in data quality checks and profiling. Option C is correct because Deequ is an open-source library that can run on Amazon EMR or Glue to compute data quality metrics. Option D is correct because CloudWatch alarms can be set on custom metrics to send alerts via SNS. Option B is incorrect because SageMaker is for model training, not data quality monitoring. Option E is incorrect because Kinesis Data Analytics is for real-time streaming analytics, not batch data quality checks.

Answer

Use Amazon Kinesis Data Analytics to continuously monitor streaming data for data quality issues.

Answer

Use Amazon SageMaker Processing jobs to run custom data quality scripts and store results in SageMaker Experiments.

Question 19

A company is migrating on-premises data to AWS. The data includes both structured and unstructured files, totaling 200 TB. The company has a 1 Gbps dedicated network connection to AWS. They want to minimize migration time and cost. Which TWO AWS services or features should they use together? (Choose two.)

Accepted Answer

AWS Snowball Edge. Correct options: A and D. AWS DataSync can efficiently transfer data over the network to S3, and Snowball Edge can be used for the largest files to reduce network load. Option B (Direct Connect) is already implied by the 1 Gbps connection; it's not an additional service. Option C (S3 Transfer Acceleration) is for speeding up internet transfers, not needed over a dedicated connection. Option E (Glue) is used for ETL, not for bulk data transfer.

Answer

Amazon S3 Transfer Acceleration

Answer

AWS Glue

Answer

AWS Direct Connect

Question 20

A data engineering team is designing a streaming data pipeline that ingests 10,000 events per second. Each event is 2 KB. The pipeline must process events with a latency of less than 1 second. The team is considering using Amazon Kinesis Data Streams with 10 shards. Which TWO additional configurations should the team implement to meet the latency requirement? (Choose two.)

Accepted Answer

Enable auto-scaling of shards based on throughput.. Correct options: B and D. Using enhanced fan-out allows multiple consumers to read from the stream with dedicated throughput, reducing latency. Auto-scaling shards ensures sufficient capacity as load varies. Option A (increase shards to 100) is over-provisioning and increases cost. Option C (record aggregation) is for reducing PUT costs but doesn't affect read latency. Option E (S3 delivery) is not relevant to latency.

Answer

Use record aggregation to reduce the number of records.

Answer

Configure Kinesis Data Firehose to deliver data to Amazon S3.

Answer

Increase the number of shards to 100.