MLS-C01 Data Engineering Practice Test 9 — 15 Questions

Question 1

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?

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

Accepted Answer

The Lambda function is processing records too slowly, causing the stream to throttle.. A is correct because the 'ProvisionedThroughputExceededException' on a DynamoDB stream indicates that the stream's read throughput is being throttled. When a Lambda function processes records too slowly, it cannot keep up with the rate of new records being written to the stream, causing the stream shards to throttle the Lambda consumer. This is a common issue when the Lambda function's processing time per record is high or when the function is invoked with a large batch size that exceeds its processing capacity.

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 2

A data scientist is building a training dataset from data stored in Amazon S3. The data consists of JSON files each containing a 'timestamp' field. The scientist wants to use AWS Glue to catalog the data and enable querying via Amazon Athena. However, Athena queries are returning zero results for time-range filters. What is the most likely cause?

Accepted Answer

The JSON files are not in the correct format for Athena.. Athena supports querying JSON data, but the JSON files must have a schema that Athena can interpret. If the 'timestamp' field is in an unrecognized date/time format or the JSON structure is inconsistent, Athena may fail to parse the data correctly, resulting in zero rows for time-range filters. Option C is correct because the most likely cause is the JSON files not being in a format that Athena can parse properly for timestamp filtering. Option A is wrong because permission issues would cause an access denied error. Option B is wrong because Athena supports nested JSON. Option D is wrong because even if the timestamp is not a partition column, filtering on it should still return results if the data matches the filter condition.

Answer

The AWS Glue crawler does not have permissions to read the S3 bucket.

Answer

Athena cannot query nested JSON objects.

Answer

The 'timestamp' field is not defined as a partition column in the Glue table.

Question 3

A company is streaming data from IoT devices to Amazon Kinesis Data Firehose, which writes to an Amazon S3 bucket. The data is then processed by an AWS Glue ETL job and loaded into Amazon Redshift. The team notices that some records are missing in Redshift. They suspect data loss during the Firehose delivery. Which configuration parameter should be checked first?

Accepted Answer

The buffer interval (e.g., 60 seconds) and buffer size.. Amazon Kinesis Data Firehose buffers incoming records before delivering them to S3, and the buffer interval (e.g., 60 seconds) and buffer size (e.g., 5 MB) directly control how long data waits and how much data is batched. If these are misconfigured or if the buffer flushes before all records are captured, records can appear missing downstream. Checking these parameters first is the standard troubleshooting step for suspected Firehose delivery gaps.

Answer

The AWS KMS key used for encryption.

Answer

The CloudWatch error logging configuration.

Answer

The compression format (GZIP, Snappy, etc.).

Question 4

A company uses Amazon Kinesis Data Streams with a shard count of 5. The data producer sends 1000 records per second, each 1 KB in size. The consumer application reads from the stream using the Kinesis Client Library (KCL) and processes records. The consumer is experiencing high latency and falling behind. What is the most effective way to improve consumer throughput?

Accepted Answer

Increase the number of shards in the stream.. The consumer is falling behind because it cannot process records fast enough. The Kinesis Client Library (KCL) typically runs one consumer thread per shard, so with only 5 shards there are only 5 parallel consumers. To improve throughput, the number of shards should be increased to allow more parallel processing. Options like enhanced fan-out would improve read throughput per consumer but do not increase parallelism; the bottleneck here is processing speed, not read throughput. Increasing shards directly increases the number of consumers and thus overall throughput.

Answer

Switch to Kinesis Data Analytics for processing.

Answer

Use enhanced fan-out to dedicate read throughput to the consumer.

Answer

Increase the record size to 5 KB.

Question 5

A company wants to store semi-structured data from IoT sensors in a cost-effective manner for occasional querying. The data is not updated once written. Which Amazon S3 storage class is the most cost-effective for this use case?

Accepted Answer

S3 Glacier Deep Archive. S3 Glacier Deep Archive is the most cost-effective storage class for semi-structured IoT sensor data that is written once and only occasionally queried. It offers the lowest storage cost among S3 classes (approximately $0.00099/GB/month), making it ideal for long-term archival of immutable data where retrieval times of 12–48 hours are acceptable.

Answer

S3 Standard

Answer

S3 One Zone-Infrequent Access

Answer

S3 Intelligent-Tiering

Question 6

Which TWO configurations are required to enable AWS Glue to access data stored in a VPC? (Choose two.)

Accepted Answer

A VPC endpoint for Amazon S3.. Option A is correct because AWS Glue jobs that run inside a VPC need a VPC endpoint for Amazon S3 (a Gateway endpoint) so that traffic to S3 stays within the AWS network and the job can read/write data without requiring internet access. Option B is correct because Glue requires a connection object that defines the VPC, the subnet(s), and the security group(s) so that the Glue job's ENIs are created in the correct network location with the right firewall rules. Option C is not required because a NAT gateway is only needed for outbound internet access, not for reaching S3 when a VPC endpoint is used. Option D is not required because an Internet gateway is only needed for public internet connectivity, which is unnecessary for private S3 access via a VPC endpoint. Option E is not required because S3 bucket policies control access to the bucket, but Glue's VPC access is governed by IAM roles and the VPC endpoint policy, not by allowing the Glue service principal in the bucket policy.

Answer

A NAT gateway in a public subnet.

Answer

An Internet gateway attached to the VPC.

Answer

An S3 bucket policy that allows access from the Glue service principal.

Question 7

Which TWO steps are required to set up cross-account access to an Amazon S3 data lake for AWS Glue jobs running in a different AWS account? (Choose two.)

Accepted Answer

Add a bucket policy to the S3 bucket that grants access to the Glue service role from the other account.. Option A is correct because the S3 bucket in the data-lake account must explicitly grant access to the principal in the other account — typically via a bucket policy that allows the Glue job's IAM role (or the role it assumes) to perform s3:GetObject, s3:ListBucket, and related actions, since S3 bucket policies are the resource-based mechanism for cross-account authorization. Option B is correct because the Glue job runs under an IAM role in its own account, and to obtain credentials valid in the bucket-owning account it must assume a role in that second account (or a role trusted cross-account) that carries the S3 read permissions; this identity-based role plus the bucket policy together satisfy both sides of the cross-account access requirement. Option C is wrong because a Glue crawler is only a metadata-discovery component and creating one cross-account does not itself grant or configure the IAM/S3 permissions needed for the job to read the data. Option D is wrong because VPC peering is a networking construct for private IP traffic and is irrelevant to S3 API authorization, which traverses AWS service endpoints rather than requiring VPC peering. Option E is wrong because AWS Organizations membership is not a prerequisite for cross-account S3 access; permissions can be granted directly through IAM roles and bucket policies regardless of organizational structure.

Answer

Create a cross-account Glue crawler in the source account.

Answer

Set up VPC peering between the two accounts' VPCs.

Answer

Ensure both accounts are in the same AWS organization.

Question 8

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 uses KMS encryption, which adds latency.. The consumer is processing 500 records per second per shard, which is half the producer's rate of 1000 records per second. While the shard's read throughput limit is 2 MB/s, not 1 MB/s, the consumer's processing speed is the bottleneck. Among the options, KMS encryption is the most plausible cause because decryption latency can significantly slow down the consumer, especially if the consumer application is not optimized for encryption overhead.

Answer

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

Answer

The stream has only one shard, which limits the read throughput to 1 MB/s.

Answer

The consumer application is not using enhanced fan-out.

Question 9

Refer to the exhibit. A data engineer is troubleshooting an AWS Glue job that fails with an 'AccessDenied' error when trying to write to the S3 bucket 'my-data-lake'. The IAM policy attached to the Glue service role is shown. What is the missing permission?

Accepted Answer

s3:ListBucket. The policy allows s3:GetObject and s3:PutObject on the bucket's objects, but it does not allow s3:ListBucket on the bucket itself. Many Glue operations require ListBucket to discover objects. Option A (s3:ListBucket) is the missing permission. Option B (s3:PutObjectAcl) is not needed. Option C (s3:GetBucketLocation) is not required. Option D (s3:DeleteObject) is not needed.

Answer

s3:PutObjectAcl

Answer

s3:GetBucketLocation

Answer

s3:DeleteObject

Question 10

Refer to the exhibit. A team deploys this CloudFormation stack. The Kinesis stream is created, but the Firehose delivery stream fails to create with a 'Resource handler returned message: Unable to assume role' error. What is the most likely cause?

Accepted Answer

The IAM role 'firehose-role' does not exist in the AWS account.. The error 'Unable to assume role' indicates that the IAM role specified in the Firehose delivery stream configuration does not exist or is not assumable. In the CloudFormation template, the role 'firehose-role' is referenced but likely not created, or the role's trust policy does not allow Firehose to assume it. The most likely cause is that the IAM role 'firehose-role' does not exist in the AWS account.

Answer

The shard count of 2 is too low for Firehose to read from.

Answer

The Kinesis stream is encrypted with KMS, but Firehose does not have permission to decrypt.

Answer

The retention period of 168 hours is too long for Firehose.

Question 11

A data scientist needs to train a machine learning model using a large dataset (500 GB) stored in an S3 bucket. The training will be performed on a SageMaker notebook instance. The data scientist wants to minimize data transfer costs and reduce training time. Which data ingestion approach should the data engineer recommend?

Accepted Answer

Use the SageMaker SDK to directly read the data from S3 during training without copying it to the notebook.. The SageMaker SDK allows training jobs to read data directly from S3 using the Pipe or File mode, which avoids copying the 500 GB dataset to the notebook instance's EBS volume. This minimizes data transfer costs (no egress from S3 to the notebook) and reduces training time by streaming data directly to the training container without intermediate storage.

Answer

Copy the dataset to the notebook instance's attached EBS volume before training.

Answer

Load the dataset into an Amazon RDS database and query it from the notebook.

Answer

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

Question 12

A company is using AWS Glue to run ETL jobs that transform data from multiple sources into a data lake on S3. The jobs are scheduled to run hourly. Recently, the jobs have been failing intermittently with 'MemoryError' exceptions. The data volume has grown over time. The data engineer needs to resolve this issue cost-effectively. Which action should be taken?

Accepted Answer

Increase the number of DPUs allocated to the Glue job and use a larger worker type.. The 'MemoryError' exception indicates that the Glue job is running out of memory as data volume grows. Increasing the number of DPUs (Data Processing Units) and using a larger worker type (e.g., from Standard to G.1X or G.2X) provides more memory and compute capacity per worker, allowing the job to handle larger datasets without failing. This is the most cost-effective approach because it scales resources only as needed, avoiding over-provisioning.

Answer

Increase the S3 timeout settings in the Glue job configuration.

Answer

Switch the Glue job type from Spark to Python shell to reduce memory overhead.

Answer

Repartition the data using Spark's repartition method before processing.

Question 13

A data engineering team needs to set up a data pipeline that ingests streaming data from an Apache Kafka cluster running on Amazon EKS into an S3 data lake. The data must be stored in Parquet format, partitioned by date and event type. The team wants a fully managed solution with minimal operational overhead. Which solution should they choose?

Accepted Answer

Use Amazon MSK (Managed Streaming for Apache Kafka) and configure an MSK Connect S3 sink connector.. Amazon MSK is a fully managed Apache Kafka service that integrates with MSK Connect, which provides a pre-built S3 sink connector. This connector can directly stream data from Kafka topics to S3 in Parquet format with partitioning by date and event type, requiring no custom code or infrastructure management. This minimizes operational overhead while meeting all requirements for a fully managed solution.

Answer

Set up a Kinesis Data Firehose delivery stream that reads from Kafka and writes to S3.

Answer

Use AWS Glue ETL jobs to pull data from Kafka cluster periodically.

Answer

Create a Kinesis Data Analytics application to read from Kafka and write to S3.

Question 14

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.. SageMaker Pipe mode streams data directly from S3 to the training container, eliminating the need to download the entire dataset to disk. This reduces I/O latency and keeps the GPU fed with data, improving utilization. The current I/O bottleneck occurs because the SDK reads data from S3 as files, causing the GPU to wait for data.

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 15

A company uses Amazon DynamoDB as the primary data store for a real-time recommendation engine. The data engineering team needs to export a daily snapshot of the DynamoDB table to S3 for offline analytics. The table is large (10 TB) and has a high read/write throughput. Which method will export the data with the least impact on the production workload?

Accepted Answer

Use the DynamoDB export to S3 feature available in the AWS Console or CLI.. The native DynamoDB export to S3 feature uses the table's internal backup mechanism (point-in-time recovery) to export data without consuming any read capacity units (RCUs) from the production table. This ensures zero impact on the live workload, even for a 10 TB table with high throughput.

Answer

Use AWS Data Pipeline to export the DynamoDB table to S3.

Answer

Use DynamoDB Scan API with parallel scans to export data to S3.

Answer

Use AWS Glue ETL job with a DynamoDB connection to export data.