MLS-C01 Data Engineering Practice Test 10 — 15 Questions

Question 1

A data scientist needs to run a one-time training job on a 5 TB dataset stored in Amazon S3. The training algorithm requires random access to individual records. Which SageMaker input mode and data format combination would be MOST appropriate?

Accepted Answer

Use File mode with RecordIO-Protobuf format. Option C is correct because File mode downloads the entire dataset to the instance's local disk, enabling random access to any record. Pipe mode does not support random access. Option A is wrong because Pipe mode streams data sequentially. Option B is wrong because Pipe mode does not support random access. Option D is wrong because Pipe mode is not suitable for random access.

Answer

Use Pipe mode with Parquet format

Answer

Use Pipe mode with RecordIO-Protobuf format

Answer

Use Pipe mode with CSV format

Question 2

A team is building a data pipeline using Amazon Kinesis Data Firehose to deliver real-time clickstream data to an Amazon S3 bucket. The data must be partitioned by year, month, day, and hour. Which configuration should the team use to achieve this?

Accepted Answer

Enable dynamic partitioning in Firehose and configure the partition keys as YYYY/MM/dd/HH. Option D is correct because Firehose has a built-in feature to add dynamic partitioning using keys like YYYY/MM/dd/HH based on the timestamp. Option A is wrong because Lambda can partition but adds complexity. Option B is wrong because S3 lifecycle rules do not repartition on delivery. Option C is wrong because partition projection is for Athena, not Firehose.

Answer

Configure an S3 lifecycle rule to move data into partition folders after delivery

Answer

Use an AWS Lambda function to write data to S3 with the desired partition structure

Answer

Use Amazon Athena partition projection to dynamically create partitions

Question 3

A company is building a data lake on Amazon S3 and wants to ensure that data is encrypted at rest using AWS KMS. Which TWO actions are required to achieve this? (Choose TWO.)

Accepted Answer

Configure the KMS key policy to allow the S3 service to use the key. Option B is correct because default bucket encryption with SSE-KMS ensures all objects are encrypted with KMS. Option D is correct because KMS key policy must allow the bucket to use the key. Option A is wrong because bucket policy can restrict access but does not enforce encryption. Option C is wrong because client-side encryption is not required if server-side encryption is used. Option E is wrong because encryption in transit (HTTPS) is separate from at-rest encryption.

Answer

Add a bucket policy that denies PutObject without encryption

Answer

Enable encryption in transit using HTTPS for all S3 API calls

Answer

Use client-side encryption on all data before uploading

Question 4

A company is using Amazon DynamoDB as a source for a machine learning pipeline. The data is exported nightly to Amazon S3 using DynamoDB Streams and an AWS Glue job. The Glue job reads the stream records, transforms them, and writes to S3 in Parquet format. The team notices that the Glue job is taking too long and consuming high DynamoDB read capacity. Which THREE actions would reduce the load on DynamoDB and improve performance? (Choose THREE.)

Accepted Answer

Use Amazon DynamoDB export to S3 (incremental) feature instead of Glue. Option A is correct because enabling DynamoDB Streams with a Lambda function to write to S3 directly avoids Glue's read from DynamoDB. Option B is correct because using DynamoDB export to S3 (incremental) does not consume read capacity. Option D is correct because using S3 as the source for Glue reduces DynamoDB reads. Option C is wrong because increasing read capacity increases load. Option E is wrong because increasing write capacity does not affect reads.

Answer

Increase the DynamoDB write capacity units to handle the stream writes

Answer

Increase the DynamoDB read capacity units to handle Glue's workload

Question 5

A data engineer is designing a data pipeline that uses Amazon Kinesis Data Streams to ingest sensor data. The data must be processed in real-time, and the results must be stored in Amazon DynamoDB. Which TWO AWS services can be used together to achieve this? (Choose TWO.)

Accepted Answer

Amazon Kinesis Data Analytics. Option B is correct because Kinesis Data Analytics can process data in real-time using SQL or Flink. Option E is correct because Lambda can consume from Kinesis and write to DynamoDB. Option A is wrong because Glue is for batch ETL, not real-time. Option C is wrong because S3 is not real-time storage. Option D is wrong because Athena is for querying, not processing.

Answer

Amazon Athena

Answer

AWS Glue

Answer

Amazon S3

Question 6

An IAM policy is attached to a data engineering role that writes to an S3 bucket. The policy is shown in the exhibit. What is the effect of this policy?

Accepted Answer

The role must use SSE-KMS when writing objects; reading is allowed only if the object is encrypted with SSE-KMS. Option B is correct because the first statement allows GetObject and PutObject only when SSE-KMS is used; the second statement denies PutObject if SSE-KMS is not used, effectively enforcing SSE-KMS for PutObject. Option A is wrong because the policy allows PutObject with SSE-KMS. Option C is wrong because GetObject is allowed with SSE-KMS. Option D is wrong because the policy never denies GetObject.

Answer

The role can write objects with any encryption, but reading is restricted to SSE-KMS only

Answer

The role can read and write any object without encryption restrictions

Answer

The role can only read objects; writing is always denied

Question 7

An ML engineer runs the AWS CLI command shown in the exhibit on a file in S3. The engineer wants to use this file in a SageMaker training job. What does the output reveal about the data?

Accepted Answer

The file is 5 GB and is stored as a CSV. Option B is correct because ContentLength is 5,368,709,120 bytes = 5 GB, and the file is a CSV. Option A is wrong because the file is 5 GB, not 5 MB. Option C is wrong because versioning is disabled (VersionId: null). Option D is wrong because the metadata shows the file is CSV, not Parquet.

Answer

The file is 5 MB and is stored as a CSV

Answer

The file is in Parquet format with two features and one label

Answer

The file is versioned and can be accessed by version ID

Question 8

An AWS Glue job is failing with an error that it cannot access an S3 bucket. The IAM role attached to the Glue job is shown in the exhibit. What is the MOST likely cause of the failure?

Accepted Answer

The S3 bucket has a bucket policy that denies access to this role. Option C is correct because the trust policy allows only Glue service to assume the role, but if the S3 bucket has a bucket policy that denies access to the role, the Glue job will fail. Option A is wrong because the trust policy allows Glue. Option B is wrong because the role has S3 full access. Option D is wrong because the role explicitly allows S3 access.

Answer

The role lacks S3 permissions

Answer

The role does not have permission to call S3 APIs

Answer

The trust policy does not allow Glue to assume the role

Question 9

A data engineer needs to process streaming data from an IoT fleet and store the results in Amazon S3 for analysis. The solution must be serverless and handle data that arrives at irregular intervals. Which AWS service should be used to ingest the data?

Accepted Answer

AWS IoT Core. Option B is correct because AWS IoT Core is designed to ingest data from IoT devices securely and at scale, and it integrates with other AWS services for processing. Option A is wrong because Kinesis Data Streams is for real-time streaming but not specifically for IoT device connectivity. Option C is wrong because SQS is a message queue, not optimized for IoT ingestion. Option D is wrong because S3 is storage, not ingestion.

Answer

Amazon S3

Answer

Amazon Simple Queue Service (SQS)

Answer

Amazon Kinesis Data Streams

Question 10

A machine learning team is building a real-time inference pipeline using Amazon SageMaker. The input data is located in an S3 bucket, and the team needs to transform the data before inference using a custom Python script. The transformation should run on a serverless infrastructure and must be triggered automatically when new data arrives in S3. Which combination of services should the team use?

Accepted Answer

Use AWS Lambda functions triggered by S3 events to run the transformation, then invoke a SageMaker endpoint.. Option C is correct because S3 events can trigger Lambda, which runs the custom script, and the output can be sent to a SageMaker endpoint for inference. Option A is wrong because SageMaker Processing is not serverless (it runs on EC2 instances). Option B is wrong because Glue is for ETL, not real-time inference. Option D is wrong because Kinesis Data Firehose is for streaming ingestion, not suitable for S3-triggered batch processing.

Answer

Use AWS Glue jobs triggered by S3 events.

Answer

Use Amazon SageMaker Processing jobs triggered by S3 events.

Answer

Use Amazon Kinesis Data Firehose to transform data and deliver to SageMaker.

Question 11

A data engineer needs to move 10 TB of historical data from an on-premises Hadoop cluster to Amazon S3 for ML training. The data is currently stored in HDFS and is compressible. The network bandwidth between the on-premises data center and AWS is 1 Gbps. The team needs to minimize the time to transfer and also wants to avoid any downtime for the on-premises system. Which solution meets these requirements?

Accepted Answer

Install the AWS DataSync agent on-premises, configure a task to transfer data to S3 with compression enabled.. Option D is correct because AWS DataSync can transfer data over the network efficiently using parallel streams and compression, and it can be installed as an agent on the on-premises cluster. Option A is wrong because Snowball Edge would require shipping, which takes longer and may not be faster than network transfer with optimization. Option B is wrong because Direct Connect provides a dedicated network connection but does not include the data transfer software; DataSync works over Direct Connect. Option C is wrong because S3 Transfer Acceleration improves speed over public internet but may not be as fast as DataSync with compression.

Answer

Set up an AWS Direct Connect connection and use rsync to copy data to S3.

Answer

Enable S3 Transfer Acceleration on the bucket and use the AWS CLI to copy data.

Answer

Use AWS Snowball Edge devices to export the data and ship them to AWS.

Question 12

A company uses Amazon Kinesis Data Streams for real-time clickstream analysis. The data is consumed by a Lambda function that enriches the records and stores them in Amazon S3. Recently, the Lambda function has been failing with throttling errors, and the consumer is falling behind. The team needs to increase the throughput of the consumer without changing the data format or the Lambda function code. What should the team do?

Accepted Answer

Increase the number of shards in the Kinesis data stream.. Option A is correct because increasing the number of shards increases the parallelism of the stream, allowing more Lambda invocations in parallel. Option B is wrong because increasing Lambda concurrency limits may help but the bottleneck is the stream's throughput. Option C is wrong because changing the batch size may help but not as effectively as increasing shards. Option D is wrong because adding a second stream would require splitting the data, which is not a direct solution.

Answer

Add a second Kinesis data stream and send duplicate records to both.

Answer

Increase the batch size in the event source mapping for Lambda.

Answer

Increase the reserved concurrency of the Lambda function.

Question 13

A financial services company is building a fraud detection model that requires joining real-time transaction data with a reference dataset of known fraudulent accounts stored in Amazon DynamoDB. The solution must minimize latency and be highly available. The reference dataset is updated frequently (every few minutes). Which architecture should the team use?

Accepted Answer

Use Amazon Kinesis Data Analytics to process the stream and join with a DynamoDB table.. Option C is correct because Kinesis Data Analytics can perform real-time SQL joins with a DynamoDB table using the reference data feature, providing low latency. Option A is wrong because Glue is for batch ETL, not real-time. Option B is wrong because SageMaker is for ML training, not real-time data processing. Option D is wrong because Athena is for querying S3, not real-time streaming.

Answer

Use Amazon Athena to query the DynamoDB table and join with streaming data.

Answer

Use AWS Glue streaming ETL to read from Kinesis and join with DynamoDB.

Answer

Use Amazon SageMaker to host a model that queries DynamoDB for each inference.

Question 14

A data scientist needs to perform exploratory data analysis on a 100 GB CSV file stored in Amazon S3. The data is not sensitive. The scientist wants to use SQL queries to filter and aggregate the data without setting up a server or moving the data. Which service should be used?

Accepted Answer

Amazon Athena. Option B is correct because Amazon Athena is a serverless query service that allows SQL queries directly on data in S3. Option A is wrong because Redshift Spectrum requires a Redshift cluster. Option C is wrong because EMR requires a cluster. Option D is wrong because Glue is for ETL, not ad-hoc querying.

Answer

AWS Glue

Answer

Amazon EMR

Answer

Amazon Redshift Spectrum

Question 15

A company runs a data lake on Amazon S3 with partitions by year/month/day. A machine learning team needs to read daily data from the last 30 days for model retraining. The data format is Parquet. The team uses Amazon Athena to query the data, but the queries are slow and scanning too much data. The team has already optimized the file sizes and compression. What additional step can reduce the amount of data scanned?

Accepted Answer

Add more partition columns such as hour to reduce the scanned partitions.. Option A is correct because partitioning on additional columns like hour can further prune partitions if queries filter by time ranges. Option B is wrong because converting to JSON would increase data size. Option C is wrong because converting to CSV would also increase size. Option D is wrong because removing partitions would increase scanning.

Answer

Remove the partition structure and store data as single large files.

Answer

Convert the Parquet files to JSON format for better query performance.

Answer

Use CSV format with Gzip compression.