MLS-C01 Practice Test 42 — 15 Questions

Question 1

An e-commerce company uses Amazon DynamoDB as the primary data store for user sessions. They want to run analytics on historical session data using Amazon Athena. What is the recommended approach to export DynamoDB data to S3 in a format optimized for Athena?

Accepted Answer

Use AWS Glue ETL to read from DynamoDB and write to S3 as Parquet. AWS Glue ETL can read from DynamoDB and write to S3 in Parquet format, which is optimal for Athena. Option A is wrong because DynamoDB Streams to Lambda to S3 is complex and not optimal. Option B is wrong because Data Pipeline is legacy. Option D is wrong because Kinesis is for streaming.

Answer

Use AWS Data Pipeline to copy data to S3 as CSV

Answer

Use Amazon Kinesis Data Firehose to stream data from DynamoDB to S3

Answer

Use DynamoDB Streams with AWS Lambda to write to S3 as JSON

Question 2

Which TWO of the following are valid ways to reduce query costs in Amazon Athena? (Choose 2)

Accepted Answer

Partition the data in S3. Option A (partitioning) reduces data scanned, lowering cost. Option C (using columnar formats) also reduces scanned data. Option B is wrong because increasing limit doesn't reduce cost. Option D is wrong because encryption doesn't reduce cost. Option E is wrong because unload is for exporting, not cost reduction.

Answer

Use UNLOAD to export query results to S3

Answer

Increase the query timeout limit

Answer

Enable encryption at rest on S3

Question 3

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 4

A company is using Amazon Kinesis Data Analytics for Apache Flink to process real-time data. The data source is a Kinesis data stream, and the output is written to an S3 bucket. Recently, the processing latency has increased significantly. The team suspects that the Flink application is encountering backpressure. Which metric should the team monitor to confirm backpressure?

Accepted Answer

busyTimeMsPerSecond. Backpressure in Flink is indicated by high 'busyTimeMsPerSecond' metric, which shows the time the operator is busy processing. Option A is wrong because 'numRecordsInPerSecond' measures throughput, not backpressure. Option B is wrong because 'currentLowWatermark' is for event time progress. Option D is wrong because 'numberOfFailedCheckpoints' indicates checkpoint failures, not backpressure directly.

Answer

currentLowWatermark

Answer

numberOfFailedCheckpoints

Answer

numRecordsInPerSecond

Question 5

A data engineer needs to analyze large CSV files stored in Amazon S3 using SQL queries. The data is not frequently accessed, and cost is a primary concern. Which AWS service should be used to query the data directly in S3 without moving it?

Accepted Answer

Amazon Athena. Amazon Athena is a serverless query service that allows SQL queries directly on data in S3, ideal for ad-hoc analysis with a pay-per-query pricing model. Option A (Amazon Redshift Spectrum) also queries S3 but requires an existing Redshift cluster. Option B (AWS Glue) is for ETL. Option D (Amazon EMR) requires cluster management and is more expensive for occasional queries.

Answer

Amazon EMR

Answer

Amazon Redshift Spectrum

Answer

AWS Glue

Question 6

A data scientist needs to query a 2 TB dataset stored in Amazon S3 using Amazon Athena. The data is in CSV format and is used for exploratory analysis. Queries are currently slow and expensive. Which action will improve query performance and reduce cost?

Accepted Answer

Convert the data to Parquet format and partition by a commonly filtered column.. Option D is correct because converting CSV to Parquet reduces scan size and query cost, and partitioning further limits data scanned. Option A is wrong because increasing workers is not applicable to Athena. Option B is wrong because converting to JSON may increase data size and cost. Option C is wrong because Athena does not use indexes.

Answer

Convert the data to JSON format to improve compression.

Answer

Increase the number of workers in the Athena query engine.

Answer

Create a composite index on the data using Athena's index feature.

Question 7

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 8

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 9

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 10

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 11

A company is building a data pipeline to process sensitive customer data. The pipeline uses AWS Glue for ETL and stores results in Amazon S3. The security team requires that all data be encrypted at rest in S3 using customer-managed AWS KMS keys. Additionally, the Glue job must be able to write encrypted data to S3. What should the data engineer do to meet these requirements?

Accepted Answer

Attach a policy to the Glue job's IAM role that includes kms:GenerateDataKey and kms:Decrypt actions for the KMS key.. The Glue job's IAM role needs kms:GenerateDataKey and kms:Decrypt permissions on the KMS key. Option A is wrong because S3 bucket policy alone doesn't grant the Glue service access to KMS. Option C is wrong because SSE-S3 is Amazon-managed keys, not customer-managed. Option D is wrong because SSE-C requires the customer to manage keys and Glue cannot provide the encryption key in each request.

Answer

Use S3 server-side encryption with customer-provided keys (SSE-C).

Answer

Use S3 server-side encryption with SSE-S3, which is enabled by default.

Answer

Configure an S3 bucket policy to enforce encryption and attach it to the Glue job's IAM role.

Question 12

A data engineering team is designing a data lake on Amazon S3. Raw data is ingested in JSON format and must be partitioned by year, month, and day. The team expects high query performance for recent data but infrequent queries for older data. The data is immutable. Which storage tier configuration minimizes costs while meeting performance requirements?

Accepted Answer

Use S3 Intelligent-Tiering for the entire data lake. Using S3 Intelligent-Tiering for the entire data lake automatically optimizes costs by moving data between frequent and infrequent access tiers based on usage patterns, without performance impact. Option A uses Glacier for recent data, which would cause retrieval delays. Option C uses S3 Standard for all data, which is cost-inefficient for older data. Option D uses a lifecycle policy to S3 One Zone-IA, which is not cost-optimal for infrequent queries and may have durability concerns.

Answer

Store all data in S3 Standard, then move to S3 Glacier after 30 days using a lifecycle policy

Answer

Store recent partitions in S3 Standard, older partitions in S3 One Zone-IA

Answer

Keep all data in S3 Standard because query performance is critical

Question 13

A company is using AWS Glue to run ETL jobs that transform data from Amazon S3 to Amazon Redshift. The jobs are currently failing due to insufficient memory. The data volume varies, with occasional spikes. Which solution should be used to handle the variable memory requirements efficiently?

Accepted Answer

Use AWS Glue Flex execution to allocate resources dynamically. AWS Glue Flex execution allows jobs to use flexible resources that can handle varying memory needs, and it is cost-effective for variable workloads. Option A (increasing DPU) would waste resources during low volume. Option C (using Apache Spark on EMR) increases management overhead. Option D (splitting jobs) adds complexity and may not handle spikes.

Answer

Migrate the ETL jobs to Amazon EMR with Apache Spark

Answer

Increase the number of DPUs (Data Processing Units) for all jobs

Answer

Split the jobs into smaller steps and run them sequentially

Question 14

A company is building a data lake on Amazon S3. Raw data is ingested from multiple sources in different formats (CSV, JSON, Parquet). The data must be cataloged and made queryable using Amazon Athena. The data schema may evolve over time. Which approach minimizes manual effort and supports schema evolution?

Accepted Answer

Use AWS Glue Crawlers to automatically create and update the Glue Data Catalog. Using AWS Glue Crawlers automatically infers and updates schemas in the Glue Data Catalog, supporting schema evolution. Option B (manual schema creation) is error-prone and not scalable. Option C (EMR only) does not leverage Glue Catalog. Option D (Athena only) can query but does not manage schema evolution.

Answer

Use Athena only, without a catalog, by directly querying files

Answer

Use Amazon EMR to process data and write to a Hive metastore

Answer

Manually create tables in Athena using DDL statements

Question 15

An e-commerce company uses Amazon Kinesis Data Firehose to deliver clickstream data to Amazon S3. The data arrives at unpredictable rates, with occasional bursts. The company needs to ensure data is delivered within 60 seconds of ingestion, and the data must be partitioned by year/month/day/hour. Which configuration meets these requirements?

Accepted Answer

Set the buffer interval to 60 seconds and enable dynamic partitioning. Setting the buffer interval to 60 seconds and enabling dynamic partitioning ensures data is delivered within 60 seconds and automatically partitioned by time. Option B (buffer size 1 MB) would cause excessive small files. Option C (Lambda transformation) adds latency. Option D (Glue streaming) is not directly integrated with Firehose.

Answer

Set the buffer size to 1 MB and disable dynamic partitioning

Answer

Use a Lambda function to process data and write to S3 with partitioning

Answer

Use AWS Glue streaming ETL to read from Firehose and write to S3