MLS-C01 Data Engineering Practice Test 10 — 15 Questions

Question 1

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?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Aggregate multiple records into a single file in a DynamoDB table, then periodically write the aggregated data to S3.. It reduces the number of S3 PUT requests by aggregating multiple records into a single file in DynamoDB and then periodically writing the aggregated data to S3. This approach directly addresses the high latency and cost issue caused by writing a separate S3 object per record, as S3 PUT requests are billed per operation and have overhead. By batching records before writing, the pipeline reduces the total number of PUT requests, improving throughput and lowering costs.

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 2

A company has a large number of small CSV files (hundreds of thousands) in an S3 bucket. A data engineer needs to run a SQL query on this data using Amazon Athena. The queries are currently slow and expensive. Which two actions will improve query performance and reduce cost?

Accepted Answer

Partition the data by a commonly filtered column (e.g., date).. Option C is correct because partitioning the data by a commonly filtered column such as date lets Athena use partition pruning, so it scans only the relevant S3 prefixes instead of the entire dataset, which directly reduces both query runtime and bytes scanned (and therefore cost). Option E is correct because converting the CSV data to a columnar format like Parquet or ORC enables column pruning and predicate pushdown, and Athena charges based on data scanned, so reading only needed columns from compressed columnar files dramatically lowers cost and improves performance. Option A is not appropriate because S3 request-rate scaling is an automatic S3 feature and does not address Athena's per-query data scanning cost or the inefficiency of row-based CSV scans. Option B is only a partial measure: gzip compression reduces bytes scanned and cost, but CSV remains row-based, so Athena still reads all columns and cannot benefit from column pruning the way Parquet or ORC allow. Option D is incorrect because splitting files into smaller ones increases the number of partitions and creates the small-file problem, adding overhead and generally worsening, not improving, Athena performance.

Answer

Increase the S3 request rate per prefix to improve read throughput.

Answer

Compress the CSV files using gzip.

Answer

Increase the number of partitions by splitting files into smaller ones.

Question 3

A data engineer needs to design a data ingestion pipeline that ingests data from a MySQL database hosted on-premises into Amazon S3 for analytics. The pipeline must capture change data (CDC) and run continuously with low latency. Which two services should the data engineer use?

Accepted Answer

AWS Database Migration Service (DMS) with ongoing replication.. Option A is correct because AWS Database Migration Service (DMS) supports ongoing replication (change data capture) from a MySQL source, continuously reading the binary log (binlog) and applying changes with low latency, which matches the CDC and continuous-ingestion requirements. Option B is correct because DMS can use Amazon S3 as a target endpoint, writing CDC records directly to S3 for analytics without needing an intermediate service. Option C (Amazon AppFlow) is a SaaS-to-AWS integration service and does not provide MySQL binlog-based CDC for on-premises databases. Option D (AWS Glue ETL jobs scheduled at regular intervals) is batch-oriented and cannot deliver continuous low-latency CDC. Option E (Amazon Kinesis Data Streams) is a streaming service but is not the ingestion mechanism that captures MySQL change data on-premises into S3 in this scenario.

Answer

Amazon AppFlow.

Answer

AWS Glue ETL jobs scheduled at regular intervals.

Answer

Amazon Kinesis Data Streams.

Question 4

Refer to the exhibit. An IAM policy is attached to a data engineering team's role. The team needs to upload data to the 'confidential' prefix in the 'my-data-lake' bucket. However, they are receiving 'AccessDenied' errors. What is the likely cause?

Accepted Answer

The Deny statement with the condition explicitly denies access to the 'confidential' prefix for accounts other than 123456789012.. The Deny statement explicitly denies access to the 'confidential' prefix when the request comes from an AWS account other than 123456789012. Since the data engineering team's role likely belongs to a different account, the Deny condition matches and overrides any Allow statements, resulting in an 'AccessDenied' error. In IAM, an explicit Deny always takes precedence over an Allow, so even if the Allow statement grants PutObject, the Deny blocks the upload.

Answer

The condition in the Deny statement requires the team to use a specific source IP address.

Answer

The Allow statement only grants GetObject and PutObject, but the team needs ListBucket.

Answer

The Allow statement's resource does not include the 'confidential' prefix.

Question 5

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 the most suitable service because it provides a fully managed ETL (Extract, Transform, Load) capability that can efficiently extract 2 TB of data from Amazon RDS for MySQL and write it to Amazon S3. Glue can leverage JDBC connections to the RDS instance, scale horizontally with its dynamic worker allocation, and complete the job within a 1-hour window by using appropriate worker types (e.g., G.2X or G.8X) and partitioning strategies. Additionally, Glue integrates natively with the AWS Glue Data Catalog and can handle incremental or full-load extraction with minimal overhead.

Answer

Amazon Kinesis Data Firehose

Answer

AWS Database Migration Service (DMS)

Answer

Amazon Athena

Question 6

A data pipeline uses AWS Lambda to process small files (10-50 MB) from an S3 bucket and write results to DynamoDB. The Lambda function times out after 15 seconds for larger files. The team wants to handle files up to 100 MB without changing the Lambda code. Which solution is MOST cost-effective?

Accepted Answer

Increase the Lambda function timeout to 5 minutes. Increasing the Lambda function timeout to 5 minutes directly addresses the 15-second timeout issue for larger files (up to 100 MB) without requiring any code changes. This is the most cost-effective solution as it avoids additional infrastructure costs (e.g., Glue, ECS, SQS) and leverages Lambda's existing pay-per-execution pricing model, which remains economical for occasional longer-running invocations.

Answer

Use AWS Glue Python shell job to replace Lambda

Answer

Use Amazon ECS with AWS Fargate to run the processing task

Answer

Configure an SQS queue to buffer the S3 events and batch them

Question 7

An organization is migrating its on-premises Hadoop cluster to AWS. The cluster runs Spark jobs that process 50 TB of data daily. The data is stored in HDFS with 3x replication. Which storage option on AWS provides the best price-performance for this workload?

Accepted Answer

Use Amazon EMR with S3 as the data store via EMRFS. Amazon EMR with S3 as the data store via EMRFS provides the best price-performance for this workload because it eliminates the need for 3x replication (S3 is inherently durable and replicated across multiple AZs), reduces storage costs, and allows compute and storage to scale independently. EMRFS enables Spark jobs to read/write directly to S3 with consistency guarantees, matching the throughput requirements of 50 TB daily processing without the overhead of managing HDFS on EBS volumes.

Answer

Use AWS Glue to run Spark jobs with data stored in S3

Answer

Use Amazon Redshift Spectrum to query the data directly in S3

Answer

Use Amazon EMR with HDFS on EBS volumes

Question 8

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 (AES-256) that are automatically rotated annually by AWS, meeting the encryption-at-rest and automatic rotation requirements with zero operational overhead. This is the simplest option because no key management or rotation configuration is needed from the customer.

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 9

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 Parquet file is 1 GB in size, which is too large for efficient processing in Athena. Athena splits data into tasks for parallel execution, but a single large file cannot be split, causing the query to run slowly. Partitioning on year, month, and day is already applied and is not the issue. The other options are incorrect: version ID null is irrelevant, the query does use partition columns, and standard storage is faster than Glacier.

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

Question 10

A Glue job fails with an AccessDenied error when trying to write to the S3 bucket my-data-lake. The IAM policy attached to the job role is shown in the exhibit. What is the MOST likely reason for the failure?

Accepted Answer

The job role does not have permissions to decrypt the KMS key used for server-side encryption. The most likely cause is that the Glue job role lacks kms:Decrypt (and typically kms:GenerateDataKey) permissions on the KMS key protecting the S3 bucket. When S3 uses SSE-KMS, every PutObject call requires the caller to have both s3:PutObject and the relevant KMS key permissions; the AccessDenied is returned by KMS, not S3. Without kms:Decrypt/kms:GenerateDataKey on the CMK, the write fails even though the S3 actions are correctly granted.

Answer

The s3:ListBucket action is missing on the bucket level

Answer

The s3:PutObject action is not sufficient; the job needs s3:PutObjectAcl

Answer

The resource ARN for s3:PutObject should include a specific prefix

Question 11

A data scientist needs to process a large volume of streaming data from IoT devices and store the results in Amazon S3 for further analysis. Which AWS service is most suitable for ingesting and processing this data in near real-time?

Accepted Answer

Amazon Kinesis Data Analytics. Amazon Kinesis Data Analytics is the most suitable service because it can process streaming data from IoT devices in near real-time using SQL or Apache Flink, and directly output the results to Amazon S3. It is designed for continuous, low-latency ingestion and analysis of data streams, making it ideal for this use case.

Answer

Amazon Redshift

Answer

AWS Glue

Answer

Amazon EMR

Question 12

A company is using AWS Glue to run ETL jobs that transform data from Amazon S3 to Amazon Redshift. The jobs are failing intermittently with timeouts. What is the most likely cause?

Accepted Answer

The AWS Glue job does not have enough DPUs (Data Processing Units) allocated.. Intermittent timeouts in AWS Glue ETL jobs typically indicate insufficient resource allocation. DPUs (Data Processing Units) define the compute capacity for the job; if too few are allocated, the job may run slowly and exceed the default timeout (e.g., 2880 minutes) or internal service limits, especially when processing large datasets from S3 to Redshift. Increasing the DPU count or using the G.1X/G.2X worker types can resolve this.

Answer

The S3 bucket policy is too restrictive.

Answer

The Amazon Redshift cluster is in maintenance mode.

Answer

The source data is not compressed.

Question 13

A company uses Amazon Kinesis Data Streams to collect clickstream data. The data is consumed by a Lambda function that writes to DynamoDB. Occasionally, the Lambda function fails due to throttling from DynamoDB. How can the company resolve this issue without losing data?

Accepted Answer

Decrease the batch size in the Lambda event source mapping.. Decreasing the batch size in the Lambda event source mapping reduces the number of records sent to each Lambda invocation. This lowers the write throughput demand on DynamoDB per invocation, mitigating throttling while still allowing Lambda to retry failed records individually. The Kinesis stream retains data for up to 365 days, so no data is lost as long as the Lambda function eventually processes all records.

Answer

Ignore the throttling errors and let Lambda retry.

Answer

Increase the number of shards in the Kinesis stream.

Answer

Use an Amazon SQS queue as a buffer between Kinesis and Lambda.

Question 14

A company needs to perform complex transformations on large datasets stored in Amazon S3 using Apache Spark. They want to minimize operational overhead. Which AWS service should they use?

Accepted Answer

Amazon EMR. Amazon EMR is the correct choice because it is a managed big data platform that natively runs Apache Spark, allowing you to perform complex transformations on large datasets stored in Amazon S3 without provisioning or managing underlying infrastructure. EMR automatically handles cluster lifecycle, scaling, and tuning, minimizing operational overhead while providing full Spark compatibility.

Answer

Amazon EC2 with manually configured Spark

Answer

Amazon Athena

Answer

AWS Glue

Question 15

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 the most efficient approach for migrating large volumes of historical data from on-premises HDFS to Amazon S3 because it physically transfers the data using ruggedized devices, bypassing network bandwidth limitations. For terabyte-to-petabyte scale migrations, Snowball Edge can transfer data at speeds up to 100 Gbps per device, and multiple devices can be used in parallel. This avoids the high cost and long transfer times associated with over-the-network solutions, especially when internet or Direct Connect bandwidth is constrained.

Answer

Use AWS Direct Connect.

Answer

Use AWS DataSync over the internet.

Answer

Use S3 Transfer Acceleration.