MLS-C01 Data Engineering Practice Test 26 — 15 Questions

Question 1

A data engineer needs to schedule an AWS Glue ETL job to run every hour. Which service should be used to trigger the job?

Accepted Answer

Amazon CloudWatch Events (EventBridge). Amazon CloudWatch Events (now part of Amazon EventBridge) is the correct service for scheduling AWS Glue ETL jobs on a recurring basis, such as every hour. It allows you to create a time-based rule using a cron or rate expression that triggers an AWS Glue job directly as a target, without needing additional compute or orchestration logic.

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AWS Lambda

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AWS Step Functions

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AWS Data Pipeline

Question 2

A company is using Amazon Athena to query a data lake in S3. Queries are slow and expensive. The data is stored as JSON. Which action will improve query performance and reduce cost?

Accepted Answer

Convert the data to Parquet format. Converting JSON data to Parquet format significantly improves Athena query performance and reduces cost. Parquet is a columnar storage format that allows Athena to scan only the columns needed for a query, drastically reducing the amount of data read from S3. This minimizes I/O and compute costs, as Athena charges based on the amount of data scanned. In contrast, JSON is row-based and requires scanning entire files even for queries that only touch a few columns.

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Compress the JSON files using gzip

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Partition the data by date

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Increase the number of Athena workers

Question 3

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. AWS Snowball Edge is a physical petabyte-scale data transfer device designed for scenarios where network bandwidth is insufficient to meet transfer deadlines. At 100 Mbps, transferring 10 TB would take approximately 10-11 days (10 TB = 80,000,000 Mb ÷ 100 Mbps ≈ 800,000 seconds ≈ 9.26 days at theoretical max, realistically longer), far exceeding the 3-day requirement. Snowball Edge bypasses the internet entirely by shipping data physically, making it the only viable option here.

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Use AWS DataSync to transfer over the internet

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Enable S3 Transfer Acceleration on the bucket

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Use AWS CLI to copy data directly over the internet

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 service can be used to catalog and provide schema information for the data?

Accepted Answer

AWS Glue Data Catalog. AWS Glue Data Catalog is a fully managed metadata repository that stores table definitions, schema information, and partition details for data in S3. Amazon Athena, while it can query data in S3 using SQL, does not provide its own catalog; it relies on the Glue Data Catalog for schema information. Therefore, only AWS Glue Data Catalog directly catalogs and provides schema information.

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Amazon Kinesis Data Streams

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Amazon RDS

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Amazon DynamoDB

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Amazon Athena

Question 5

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. The most likely cause of AccessDenied errors is that the IAM policy includes a condition that requires encryption to be specified (e.g., 's3:x-amz-server-side-encryption' equals 'AES256'), but the upload request does not include the encryption header. When users upload without specifying encryption, the condition evaluates to false, and the policy denies the action.

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The condition should check for aws:SourceIp instead of encryption

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The policy is attached to the wrong IAM user

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The bucket policy denies all PutObject without encryption

Question 6

A data engineer needs to transform large CSV files stored in Amazon S3 into Parquet format before loading into Amazon Redshift. The transformation logic is complex and requires custom Python code. Which AWS service should be used to perform this transformation with minimal operational overhead?

Accepted Answer

AWS Glue. AWS Glue is the correct answer because it is a fully managed, serverless ETL service that can handle large CSV files, convert them to Parquet, and load into Amazon Redshift with minimal operational overhead. AWS Glue provides a built-in Spark environment and supports custom Python code via Spark jobs. Option B (AWS Lambda) has a 15-minute timeout and is not designed for large-scale data transformations. Option C (Amazon EMR) requires managing clusters, increasing operational overhead. Option D (AWS Data Pipeline) is a legacy service with less flexibility and is not optimized for complex transformations like CSV to Parquet.

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AWS Lambda

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Amazon EMR

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AWS Data Pipeline

Question 7

A company is streaming clickstream data from a website to Amazon Kinesis Data Streams. The data is consumed by a Lambda function that enriches each record with geolocation information before writing to an S3 bucket. Recently, the Lambda function has been failing with throttling errors. What is the MOST likely cause?

Accepted Answer

The Lambda function's concurrent execution limit has been reached. The most likely cause is that the Lambda function's concurrent execution limit has been reached. Kinesis Data Streams invokes Lambda functions per shard, and with high throughput or many shards, concurrent invocations can exceed the default Lambda concurrency limit (1000 per region). This results in throttling errors. Option A is incorrect because the Lambda payload limit for asynchronous invocation (used by Kinesis) is 256 KB, not 6 MB. Option C is incorrect; setting reserved concurrency too high would not cause throttling—it could actually help avoid throttling. Option D is incorrect because the default shard limit is 500, but shard limits are a Kinesis concern, not directly causing Lambda throttling.

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The Lambda function's payload size exceeds the 6 MB limit

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The Lambda function's reserved concurrency is set too high

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The Kinesis stream has exceeded the default shard limit of 500

Question 8

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. AWS Glue Crawlers scan the S3 data, infer the schema, and automatically update the AWS Glue Data Catalog, which Athena uses as its metastore. When Firehose writes new columns into S3, the next crawler run detects the added fields and updates the table metadata, so Athena queries immediately reflect the new schema without manual intervention. This is the standard serverless pattern for schema evolution in an Athena-on-S3 pipeline.

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

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Configure Firehose to convert the data to Apache JSON format

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Recreate the Athena table daily to pick up new columns

Question 9

A company runs a daily batch ETL job using AWS Glue that reads from Amazon RDS (MySQL), transforms the data, and writes to Amazon Redshift. The job takes 6 hours and processes 500 GB of data. Management wants to reduce the runtime. Which action would be MOST effective?

Accepted Answer

Increase the number of DPUs allocated to the Glue job. Increasing the number of DPUs (Data Processing Units) allocated to the AWS Glue job directly increases the parallelism of the Spark-based ETL job, allowing it to process the 500 GB of data faster. Since the bottleneck is the Glue job's compute capacity, adding more DPUs reduces runtime without changing the source or target infrastructure.

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Increase the node size of the Redshift cluster

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Use the Redshift COPY command to load data directly from RDS

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Use Amazon RDS with Provisioned IOPS SSD storage

Question 10

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. AWS Glue is a fully managed ETL service that natively supports crawling S3 data sources, automatically detecting schemas for CSV, JSON, and Parquet formats, and generating transformation jobs that load data into Redshift. It integrates directly with the Glue Data Catalog and Redshift, allowing scheduled daily ingestion with minimal operational overhead. This makes it the best fit for automating multi-format, multi-bucket ingestion into Redshift.

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Amazon EMR with Apache Spark

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AWS Data Pipeline

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AWS Database Migration Service (DMS)

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. The Lambda function fails because it 'cannot read the file' — that is an S3 read operation, which requires the s3:GetObject permission on the Lambda execution role. Without s3:GetObject, the function's boto3/aws-sdk call to GetObject returns AccessDenied, which matches the symptom. DynamoDB permissions are irrelevant to reading the file.

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The Lambda function lacks DynamoDB write permissions

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The S3 bucket does not exist

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The S3 event notification is misconfigured

Question 12

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. The requirement is to reduce latency so that data is available for analysis within 30 minutes of being written to S3. Currently, the Glue job runs hourly, which is too slow. Using S3 event notifications to trigger a Lambda function that starts the Glue job immediately upon new data arrival ensures the ETL process begins as soon as data is written, minimizing latency. This event-driven approach is more efficient than scheduled triggers.

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

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

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Reduce the Glue job trigger frequency to every 15 minutes

Question 13

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. Converting all data to Apache Parquet before storing in S3 minimizes storage overhead and improves query performance. Parquet is a columnar format that provides efficient compression and encoding schemes, reducing storage costs. It is natively supported by Amazon Athena and Redshift Spectrum, enabling fast analytics without on-the-fly conversion. Option B (storing in original format) increases storage costs and can degrade query performance, especially with JSON or CSV. Option C incurs transformation costs each time data is queried, negating any storage benefit. Option A adds unnecessary transformation steps and cluster costs. Therefore, upfront conversion to Parquet is the most cost-effective strategy for this use case.

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

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

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

Question 14

A company uses Amazon Kinesis Data Streams to ingest clickstream data from a website. The data is consumed by a custom application that runs on Amazon EC2 instances. The company notices that the consumer application is falling behind the producer, causing data to be throttled. Which action should the company take to improve the consumer's throughput?

Accepted Answer

Increase the number of shards in the Kinesis data stream. Increasing the number of shards in the Kinesis data stream increases the total read throughput available to consumers, because each shard supports up to 2 MB/sec read and 5 transactions per second. If the consumer application is falling behind and data is being throttled, adding shards provides more parallelism and higher aggregate read capacity. The consumer application must also scale its processing (e.g., more EC2 instances or threads) to read from the additional shards.

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Reduce the data retention period of the stream

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Increase the maximum concurrency of the AWS Lambda function that processes the stream

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Use Amazon Kinesis Data Firehose to deliver data to Amazon S3

Question 15

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. AWS Glue jobs support a 'Max retries' parameter (set to 0–10) that automatically retries the entire job run when it fails. For intermittent S3 write timeouts caused by transient network or service issues, configuring Max retries is the native, low-effort way to handle transient failures without manual intervention.

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

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

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