MLS-C01 Data Engineering Practice Test 4 — 15 Questions

Question 1

A team wants to move data from an on-premises Oracle database to Amazon S3 for analytics. The pipeline must run daily and handle incremental updates. Which THREE services should they use together? (Choose three.)

Accepted Answer

Amazon S3. Options A, B, and D are correct. AWS DMS can do continuous replication from Oracle to S3. Glue can transform the data. S3 is the target. Option C is wrong because SageMaker is for ML, not data ingestion. Option E is wrong because Athena is a query engine, not a data movement service.

Answer

Amazon SageMaker

Answer

Amazon Athena

Question 2

A company uses Amazon Kinesis Data Streams to ingest clickstream data. They need to archive raw data to S3 every hour and also enable real-time processing with sub-second latency. Which TWO actions should they take? (Choose two.)

Accepted Answer

Configure a Lambda function as a consumer of the stream for real-time processing.. Options A and D are correct. Option A provides real-time processing via Lambda. Option D archives data to S3 using Firehose with hourly buffering. Option B is wrong because Kinesis Agent is for log files, not archiving. Option C is wrong because Data Analytics does not write to S3 directly. Option E is wrong because S3 events are not used for archiving.

Answer

Use Kinesis Data Analytics to write output to S3.

Answer

Use S3 events to trigger a Lambda function that reads from the stream.

Answer

Install the Kinesis Agent on an EC2 instance to write data to S3.

Question 3

An IAM policy attached to a SageMaker notebook role is shown. The data engineer tries to run an Athena query on a table in the 'my_database' Glue database. The query fails with an access denied error. What is the MOST likely cause?

Accepted Answer

The policy does not allow athena:StartQueryExecution on the Athena workgroup.. Option D is correct because the policy does not grant permission to the Athena workgroup. Athena requires workgroup permissions for StartQueryExecution. Option A is wrong because the policy allows GetObject. Option B is wrong because the policy allows GetTable and GetDatabase. Option C is wrong because S3 actions are allowed on the bucket.

Answer

The policy does not allow s3:PutObject on the query results location.

Answer

The policy does not allow glue:GetTable on the specific database.

Answer

The policy does not allow s3:ListBucket on the bucket.

Question 4

A data engineer runs the AWS CLI command above to inspect a file in S3. They need to determine if the file was modified after a Glue ETL job processed it. What additional information could they obtain from this command?

Accepted Answer

The object's last modified timestamp.. Option D is correct because the LastModified timestamp is provided, which can be used to compare with job completion time. Option A is wrong because head-object does not show object size. Option B is wrong because ContentLength is shown. Option C is wrong because ETag is shown.

Answer

The object's content type.

Answer

The object's storage class.

Answer

The object's ETag.

Question 5

A data engineer created a CloudFormation template for a Glue ETL job as shown. The job processes 500 GB of data and takes 90 minutes to complete. However, the job fails after 60 minutes. What is the MOST likely cause?

Accepted Answer

The Timeout property is set to 60 minutes, but the job requires more time.. Option C is correct because the Timeout is set to 60 minutes, but the job takes 90 minutes, so it is killed. Option A is wrong because the script location is correct. Option B is wrong because the role appears correctly configured. Option D is wrong because MaxRetries is 0, but the job fails due to timeout, not due to retry policy.

Answer

The IAM role does not have sufficient permissions.

Answer

The ScriptLocation S3 bucket is in a different region.

Answer

The MaxRetries property is set to 0, so the job does not retry on failure.

Question 6

A data science team needs to process streaming data from thousands of IoT devices and perform real-time anomaly detection. The data must be persisted in Amazon S3 for batch processing later. Which combination of AWS services should be used to meet these requirements?

Accepted Answer

Amazon Kinesis Data Streams for ingestion, Amazon Kinesis Data Analytics for anomaly detection, and Amazon Kinesis Data Firehose to deliver data to Amazon S3.. Option A is correct because Kinesis Data Streams ingests streaming data, Kinesis Data Analytics performs real-time anomaly detection, and Firehose delivers data to S3 for batch processing. Option B is wrong because SQS is not optimized for streaming and does not have built-in analytics. Option C is wrong because Lambda alone cannot handle high-throughput streaming and lacks persistence to S3. Option D is wrong because Glue is a batch ETL service, not real-time.

Answer

Amazon Kinesis Data Streams for ingestion, AWS Glue for anomaly detection, and Amazon S3 for storage.

Answer

AWS Lambda for both ingestion and anomaly detection, and Amazon S3 for storage.

Answer

Amazon Simple Queue Service (SQS) for ingestion, AWS Lambda for anomaly detection, and Amazon S3 for storage.

Question 7

A company uses Amazon Redshift for its data warehouse. The data engineering team notices that queries are slow and wants to improve performance without changing the schema. Which action is most likely to improve query performance?

Accepted Answer

Increase the number of nodes in the cluster.. Option C is correct because increasing the number of nodes adds compute resources, improving parallel processing. Option A is wrong because changing distribution style would alter the schema. Option B is wrong because decreasing node count reduces resources. Option D is wrong because disabling compression increases storage and I/O.

Answer

Decrease the number of nodes to reduce network overhead.

Answer

Disable compression on all tables to reduce CPU overhead.

Answer

Change the distribution style from AUTO to EVEN.

Question 8

A data pipeline uses AWS Glue to transform data from Amazon RDS to Amazon S3. The team wants to ensure that only new or updated records are processed in each run, minimizing cost and time. Which AWS Glue feature should be used?

Accepted Answer

Enable Glue Job Bookmarks.. Option D is correct because Job Bookmarks track processed data and enable incremental processing. Option A is wrong because Partitioning organizes data but doesn't track changes. Option B is wrong because Crawlers discover schema but don't process incrementally. Option C is wrong because Triggers schedule jobs but don't enable incremental processing.

Answer

Use Glue triggers to run the job on a schedule.

Answer

Use Glue partition pruning to filter data.

Answer

Use Glue crawlers to detect new data.

Question 9

A company is using Amazon SageMaker to train machine learning models. The training data is stored in Amazon S3, but the data includes personally identifiable information (PII) that must be anonymized before training. What is the most efficient way to anonymize the data?

Accepted Answer

Use an AWS Glue ETL job to read from S3, apply anonymization, and write to another S3 bucket.. Option B is correct because AWS Glue can run a transformation job to anonymize PII before training. Option A is wrong because SageMaker Processing jobs are for feature engineering, not data anonymization from S3. Option C is wrong because Athena is for querying, not transforming. Option D is wrong because Redshift Spectrum queries data in S3 but does not anonymize efficiently.

Answer

Use Amazon Athena to query the data and apply anonymization functions.

Answer

Use Amazon Redshift Spectrum to query and anonymize data in S3.

Answer

Use a SageMaker Processing job to read from S3 and apply anonymization.

Question 10

A data engineer needs to move 50 TB of historical data from an on-premises Hadoop cluster to Amazon S3. The network bandwidth is limited to 100 Mbps. Which AWS service should be used to transfer the data most efficiently?

Accepted Answer

AWS Snowball Edge device to physically ship the data.. Option C is correct because AWS Snowball is designed for large data transfers over limited bandwidth. Option A is wrong because Direct Connect requires high bandwidth and doesn't physically move data. Option B is wrong because S3 Transfer Acceleration speeds up transfers over the internet but still limited by bandwidth. Option D is wrong because VPN is not efficient for 50 TB.

Answer

Amazon S3 Transfer Acceleration to speed up the transfer.

Answer

AWS Direct Connect to establish a dedicated network connection.

Answer

AWS Site-to-Site VPN to connect and copy data.

Question 11

A team is building a data lake on Amazon S3 and using AWS Glue to catalog data. They notice that Glue crawlers are taking too long to update the catalog for a large dataset with millions of small files. Which approach will MOST improve crawler performance?

Accepted Answer

Consolidate the small files into larger files (e.g., 100 MB each).. Option D is correct because consolidating small files into larger files reduces the number of objects the crawler must scan. Option A is wrong because increasing crawler frequency doesn't reduce scan time. Option B is wrong because using a custom classifier doesn't reduce scan time. Option C is wrong because partitioning helps but still many files per partition.

Answer

Increase the frequency of the crawler runs.

Answer

Partition the data by date in S3.

Answer

Use a custom classifier to parse the data.

Question 12

A company uses Amazon Kinesis Data Firehose to deliver streaming data to Amazon S3. The data is in JSON format, and the company wants to convert it to Parquet for efficient querying. Which configuration should be used?

Accepted Answer

Enable data transformation in Firehose using an AWS Lambda function to convert JSON to Parquet, and set the output format to Parquet.. Option A is correct because Firehose supports data transformation using Lambda and can convert to Parquet via the output format setting. Option B is wrong because Glue is not directly integrated with Firehose. Option C is wrong because Kinesis Data Analytics does not convert to Parquet for S3 delivery. Option D is wrong because Firehose cannot directly write to Redshift in Parquet format without transformation.

Answer

Use an AWS Glue job to convert the JSON files in S3 to Parquet after delivery.

Answer

Use Amazon Kinesis Data Analytics to convert the stream to Parquet before sending to Firehose.

Answer

Configure Firehose to deliver data directly to Amazon Redshift, which automatically converts to Parquet.

Question 13

A data engineer needs to run a one-time ETL job to transform 500 GB of data from Amazon RDS to Amazon S3. The job should be cost-effective and require minimal infrastructure management. Which AWS service should be used?

Accepted Answer

AWS Glue. Option B is correct because AWS Glue is serverless and suitable for one-time ETL jobs. Option A is wrong because EMR requires cluster management and is more expensive for one-time jobs. Option C is wrong because Data Pipeline is a managed service but still requires provisioning. Option D is wrong because Athena is for querying, not ETL.

Answer

Amazon EMR

Answer

Amazon Athena

Answer

AWS Data Pipeline

Question 14

A company uses Amazon DynamoDB as the primary data store for a real-time application. The data science team wants to analyze the data using Amazon Athena. What is the most efficient way to make the DynamoDB data available for Athena queries?

Accepted Answer

Use DynamoDB Streams to invoke an AWS Lambda function that writes data to Amazon S3 in Parquet format. Then query the data in S3 using Athena.. Option A is correct because DynamoDB Streams can trigger a Lambda function to write to S3, and Athena can query S3. Option B is wrong because EMR can query DynamoDB directly but is more complex. Option C is wrong because Glue can extract data but adds latency. Option D is wrong because Redshift Spectrum uses S3, not DynamoDB directly.

Answer

Use AWS Glue to extract data from DynamoDB and load into S3 on a schedule.

Answer

Use Amazon Redshift Spectrum to query DynamoDB directly.

Answer

Use Amazon EMR to read directly from DynamoDB and run Hive queries.

Question 15

A company is designing a data pipeline that ingests streaming data from social media feeds. The data must be processed in real-time to detect trending topics, and results must be stored in Amazon DynamoDB for low-latency access. Which services should the company use? (Choose TWO.)

Accepted Answer

Amazon Kinesis Data Analytics. Option A (Kinesis Data Streams) is required for ingestion, and Option D (Kinesis Data Analytics) is required for real-time trending detection. Option B (SQS) is not for streaming. Option C (Lambda) can process but is not the best for real-time analytics on streams. Option E (Firehose) is near-real-time, not real-time.

Answer

Amazon Kinesis Data Firehose

Answer

AWS Lambda

Answer

Amazon Simple Queue Service (SQS)