MLS-C01 Practice Exam 51 — 20 Questions

Question 1

A data engineer needs to extract data from an Amazon RDS for MySQL database into Amazon S3 for further processing. The data volume is 2 TB and the job must run daily within a 1-hour window. Which AWS service is most suitable for this task?

Accepted Answer

AWS Glue. AWS Glue is designed for extract, transform, and load (ETL) jobs and can connect to JDBC sources like RDS and write to S3. DMS is for database migration, not scheduled ETL. Athena is for querying data in S3. Kinesis is for streaming data.

Answer

Amazon Kinesis Data Firehose

Answer

AWS Database Migration Service (DMS)

Answer

Amazon Athena

Question 2

A company is migrating its on-premises Hadoop cluster to AWS. They have a large amount of historical data stored in HDFS. Which approach is the most efficient for transferring this data to Amazon S3?

Accepted Answer

Use AWS Snowball Edge devices.. AWS Snowball Edge is ideal for large data transfers when network bandwidth is limited. AWS DataSync is for network transfers, but slower for huge datasets. S3 Transfer Acceleration improves speed but still network. Direct Connect is network-based.

Answer

Use AWS Direct Connect.

Answer

Use AWS DataSync over the internet.

Answer

Use S3 Transfer Acceleration.

Question 3

A company uses AWS Glue jobs with job bookmarks enabled to process incremental data. They notice that the job processes all data each time instead of only new data. What is the most likely reason?

Accepted Answer

The MaxConcurrentRuns is set to 3, which can cause bookmark conflicts.. Job bookmarks require a unique identifier to track processed data. If the source data lacks a monotonically increasing column or the schema does not include a suitable key, bookmarks may not work. Setting MaxConcurrentRuns > 1 can cause issues with bookmarks. The correct answer: MaxConcurrentRuns is set to 3, which can interfere with bookmark state.

Answer

The TempDir is not configured correctly.

Answer

The job bookmark option is set to 'job-bookmark-enable' but should be 'job-bookmark-disable'.

Answer

The source data does not have a column that can be used as a bookmark key.

Question 4

A data engineering team is designing a data lake on AWS. They need to store raw data in S3 and allow multiple analytics services to query the data. Which TWO services can be used to catalog and provide schema information for the data?

Accepted Answer

AWS Glue Data Catalog. Option B (AWS Glue Data Catalog) is a metadata catalog. Option D (Amazon Athena) uses Glue Data Catalog as its schema store. Option A (DynamoDB) is not a catalog; Option C (RDS) is a database; Option E (Kinesis) is streaming.

Answer

Amazon Kinesis Data Streams

Answer

Amazon RDS

Answer

Amazon DynamoDB

Question 5

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 6

A company is using Amazon Kinesis Data Streams to ingest real-time clickstream data. The data is consumed by a Kinesis Data Analytics application that runs SQL queries. The application has been failing intermittently with 'ProvisionedThroughputExceededException' errors. Which action should be taken to resolve this issue?

Accepted Answer

Increase the number of shards in the Kinesis data stream.. The error indicates that the shard's read throughput limit (5 transactions/second per shard) is being exceeded. Increasing the number of shards increases the total throughput. Option A (increase shard count) is the correct solution. Option B (increase record size) could worsen the problem. Option C (use Kinesis Firehose) changes the architecture but does not address the shard throughput. Option D (disable error logging) does not solve the underlying issue.

Answer

Disable error logging in the Kinesis Data Analytics application.

Answer

Increase the record size in the Kinesis data stream.

Answer

Switch from Kinesis Data Analytics to Kinesis Data Firehose.

Question 7

Which THREE factors should be considered when choosing between Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose for a real-time data ingestion pipeline? (Choose three.)

Accepted Answer

The need for custom real-time processing logic using consumer applications.. Three key differences: Kinesis Data Streams supports custom processing with consumers (option A), requires manual scaling (option D), and has lower latency (option B). Firehose is for simple delivery with minimal code, auto-scaling, and higher latency due to buffering. Option C (exactly-once delivery) is not guaranteed by either. Option E (built-in analysis) is not a feature of Firehose.

Answer

The need for built-in data transformation and analytics.

Answer

The requirement for exactly-once delivery semantics.

Question 8

A data scientist is training a deep learning model using a large dataset stored in S3. The training job runs on a SageMaker training instance with a GPU. The data engineer notices that the GPU utilization is low, and the training is I/O bound. The data is read directly from S3 using the SageMaker SDK. Which change should the data engineer recommend to improve GPU utilization?

Accepted Answer

Use SageMaker Pipe mode to stream data directly from S3 to the training container.. Option B is correct because enabling SageMaker Pipe mode streams data directly from S3 to the training container without writing to disk, reducing I/O bottlenecks and improving GPU utilization. Option A (increasing batch size) may cause memory issues. Option C (using EFS) adds network latency. Option D (using EBS) still involves disk I/O.

Answer

Increase the batch size in the training script to process more data per step.

Answer

Mount the S3 bucket to the training instance using Amazon Elastic File System (EFS).

Answer

Copy the entire dataset to an Amazon EBS volume attached to the training instance.

Question 9

A company is building a data lake on Amazon S3. Data arrives from multiple sources in different formats (CSV, JSON, Parquet). The engineering team wants to query this data using Amazon Athena with minimal transformation. Which approach minimizes query cost and improves performance?

Accepted Answer

Use AWS Glue to convert all data to Parquet format, partition by date, and store in a separate S3 bucket. Using Parquet with partitioning and compression reduces the amount of data scanned by Athena, lowering cost and improving performance. Converting to a single format is not necessary, but optimized formats like Parquet are beneficial. Glue can convert data, but it adds overhead.

Answer

Use Amazon Redshift Spectrum to query the data directly without transformation

Answer

Use Amazon EMR to convert data to CSV format and repartition

Answer

Store data as-is in S3 and create external tables in Athena for each format

Question 10

A data engineer uses AWS Glue to run ETL jobs that transform data from JSON to Parquet. The job runs successfully but takes 30 minutes longer than expected. CloudWatch metrics show high memory utilization and disk spills. What is the most likely cause?

Accepted Answer

The data is skewed and not evenly distributed across partitions. High memory utilization and disk spills indicate that the data is not evenly distributed, causing some executors to process more data than others. This is often due to data skew. Increasing DPUs might help, but addressing skew is more effective.

Answer

The number of DPUs is too low

Answer

The sink bucket has insufficient I/O throughput

Answer

The source data format is too large

Question 11

Which THREE AWS services can be used together to build a serverless data pipeline that ingests streaming data, transforms it, and loads it into Amazon Redshift for analysis?

Accepted Answer

Amazon Kinesis Data Firehose. Kinesis Data Streams ingests streaming data. Lambda processes it. Firehose delivers to Redshift. EMR is not serverless (managed). SQS is not ideal for streaming. Glue can also be used but is not the only option.

Answer

Amazon EMR

Answer

Amazon SQS

Question 12

A data engineer needs to automate the transformation of CSV files to Parquet format as soon as they are uploaded to an S3 bucket. The transformed files should be stored in another S3 bucket. Which solution is the most cost-effective and requires the least maintenance?

Accepted Answer

Configure an S3 event notification to invoke a Lambda function.. S3 Event Notification to Lambda is serverless and cost-effective. Glue jobs have a minimum billing of 1 minute. EMR is overkill. EC2 requires management.

Answer

Configure an S3 event notification to invoke an AWS Glue job.

Answer

Run an Amazon EMR cluster continuously to watch for new files.

Answer

Set up an EC2 instance with a cron job to poll the S3 bucket.

Question 13

A data engineer needs to store streaming data from thousands of IoT devices for real-time analytics. Which AWS service is most suitable for ingesting and storing this data for subsequent processing by Amazon Kinesis Data Analytics?

Accepted Answer

Amazon Kinesis Data Streams. Option C is correct because Kinesis Data Streams is designed for real-time data ingestion and storage, and integrates with Kinesis Data Analytics. Option A is wrong because S3 is not real-time; Option B is wrong because DynamoDB is for NoSQL, not streaming; Option D is wrong because RDS is a relational database not suited for high-velocity streaming.

Answer

Amazon S3

Answer

Amazon RDS

Answer

Amazon DynamoDB

Question 14

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 15

A company is using Amazon DynamoDB to store sensor data. The data is exported to Amazon S3 using DynamoDB Streams and AWS Lambda for long-term archival. Recently, the Lambda function has been failing due to 'ProvisionedThroughputExceededException' on the DynamoDB stream. What is the most likely cause?

Accepted Answer

The Lambda function is processing records too slowly, causing the stream to throttle.. The error indicates that the DynamoDB stream's read throughput is being exceeded. The Lambda function's event source mapping reads from the stream, and if it is processing too slowly or there are too many shards, it can throttle. Option B (increase the batch size) reduces the number of reads per second. Option A (increase Lambda memory) may not help. Option C (disable stream) would stop the export. Option D (increase DynamoDB write capacity) does not affect stream reads.

Answer

The DynamoDB stream is disabled.

Answer

The DynamoDB table's write capacity is too low.

Answer

The Lambda function does not have enough memory allocated.

Question 16

Refer to the exhibit. A company is using the Kinesis stream 'my-stream' with one shard. The producer is sending 1000 records per second, each 1 KB. The consumer is reading from the stream using the Kinesis Client Library (KCL). The consumer is able to process 500 records per second per shard. What is the most likely cause of the consumer falling behind?

Accepted Answer

The stream has only one shard, which limits the read throughput to 1 MB/s.. The stream has only 1 shard, which provides a read throughput of 2 MB/s (or 1000 records per second of 1 KB each). The consumer can process 500 records per second, which is half the incoming rate, so it will fall behind. Increasing the number of shards would increase read throughput. Option A (not enough shards) is correct. Option B (retention period) does not affect ingestion. Option C (encryption) may add overhead but is not the main issue. Option D (consumer code) could be optimized, but the root cause is shard count.

Answer

The retention period is set to 24 hours, which is too short.

Answer

The stream uses KMS encryption, which adds latency.

Answer

The consumer application is not using enhanced fan-out.

Question 17

A data engineer is building a data pipeline that uses AWS Lambda to process records from an SQS queue and write results to an S3 bucket. The Lambda function processes each record individually and writes a separate file to S3. The team notices high latency and wants to reduce the number of S3 PUT requests to improve performance and reduce cost. Which approach should the data engineer take?

Accepted Answer

Aggregate multiple records into a single file in a DynamoDB table, then periodically write the aggregated data to S3.. Option A is correct because buffering records in a DynamoDB table and then using a batch write to S3 reduces the number of PUT requests. Option B (increasing Lambda memory) does not reduce S3 PUT count. Option C (S3 batch operations) is for existing objects, not for incoming data. Option D (multipart upload) is for large objects, not for many small objects.

Answer

Use S3 multipart upload for each record to improve throughput.

Answer

Increase the Lambda function's memory allocation to improve processing speed.

Answer

Use S3 Batch Operations to process the records in batches.

Question 18

A data scientist needs to run a one-time query on 10 TB of data stored in S3 using Amazon Athena. The query scans 5 TB and returns a small result set. Which approach minimizes cost?

Accepted Answer

Query the data directly in Athena without any preprocessing. Athena charges based on data scanned. Partitioning and using columnar formats like Parquet reduce the amount of data scanned. Redshift Spectrum would require a cluster. EMR is more expensive for one-time queries.

Answer

Create an S3 Select query to filter data before Athena

Answer

Use Amazon Redshift Spectrum to query the data

Answer

Use AWS Glue to convert the data to Parquet format and repartition by date

Question 19

A company stores sensitive customer data in an S3 bucket. The security team requires that all data be encrypted at rest with a key that is automatically rotated every year. Which solution meets these requirements with the least operational overhead?

Accepted Answer

Use SSE-S3 (Amazon S3-managed keys). SSE-S3 uses Amazon S3-managed keys (SSE-S3) which are automatically rotated. SSE-KMS with automatic rotation also works but requires KMS key management. SSE-C requires customer-managed keys. SSE-KMS with manual rotation adds overhead.

Answer

Use SSE-KMS with a customer-managed key and automatic rotation

Answer

Use SSE-C (customer-provided keys)

Answer

Use SSE-KMS with a customer-managed key and manual rotation

Question 20

A data engineer is investigating why an Athena query against the my-data-lake bucket is slow. The query filters on year, month, and day. The exhibit shows the metadata of one Parquet file. What is the MOST likely cause of the slow query?

Accepted Answer

The file is too large, causing Athena to process it in a single task. The file is 1 GB (1073741824 bytes), which is large for a single Parquet file. Athena splits files into tasks; a single large file cannot be parallelized, causing slow performance. Partitioning is fine, but file size matters. The metadata is not missing. Storage class is standard. Versioning is not enabled.

Answer

The version ID is null, causing data inconsistency

Answer

The partition columns are not being used in the query

Answer

The storage class is STANDARD, which is slower than GLACIER