MLS-C01 Practice Test 45 — 15 Questions

Question 1

Refer to the exhibit. An ML engineer runs the above CLI command to inspect files in an S3 bucket. The training data consists of 200 CSV files, each 1 GB. The engineer plans to use Amazon SageMaker to train a model using this data. What should the engineer do to optimize training performance?

Accepted Answer

Convert the CSV files to Parquet format and use Pipe input mode.. Option D is correct because converting to Parquet and using a Pipe input mode reduces I/O and improves throughput. Option A is wrong because copying to EBS is not efficient. Option B is wrong because Athena is for querying, not training. Option C is wrong because increasing instances may help but does not address the inefficiency of CSV format.

Answer

Increase the number of training instances to process files in parallel.

Answer

Use Amazon Athena to transform the data into CSV format with headers.

Answer

Use the File input mode and copy all files to the training instance's EBS volume.

Question 2

A company is using Amazon Kinesis Data Firehose to load streaming data into Amazon S3. The data is in JSON format, and they want to convert it to Parquet before storage. What should they configure?

Accepted Answer

Enable data format conversion in Firehose and specify a Glue table. Kinesis Data Firehose can convert incoming data to Parquet or ORC using a schema from AWS Glue. Option A is wrong because Lambda can also transform but is not the primary method. Option C is wrong because Kinesis Data Analytics is for processing. Option D is wrong because Athena is for querying.

Answer

Use an AWS Lambda function to transform the data

Answer

Run an AWS Glue ETL job after data is in S3

Answer

Use Kinesis Data Analytics for Apache Flink to convert the format

Question 3

A data engineer is designing a data pipeline that ingests 500 GB of data daily from an on-premises Oracle database to Amazon S3. The pipeline must minimize data loss and support change data capture (CDC). Which combination of services should they use?

Accepted Answer

AWS Database Migration Service (DMS) with ongoing replication. AWS Database Migration Service (DMS) with ongoing replication enables CDC from Oracle to S3. Option A is wrong because Glue does not support CDC directly. Option B is wrong because Kinesis requires custom agents. Option D is wrong because Data Pipeline is batch.

Answer

AWS Data Pipeline with SQL query

Answer

Amazon Kinesis Data Streams with a custom Oracle CDC connector

Answer

AWS Glue ETL jobs running on a schedule

Question 4

A data engineer is designing a data lake on Amazon S3 that must support both batch and streaming analytics. The data comes in Parquet format and needs to be queryable by Amazon Athena. Which partitioning strategy will optimize query performance and reduce costs?

Accepted Answer

Partition by date and hour for time-based queries. Partitioning by date and hour allows Athena to prune partitions effectively for time-based queries, reducing data scanned. Option A is wrong because a single partition is not efficient. Option C is wrong because partitioning by a high-cardinality column like device_id creates many small partitions. Option D is wrong because using CSV negates the benefits of columnar storage.

Answer

Store data as CSV without partitioning for simplicity

Answer

Partition by device_id for granular access

Answer

Use a single partition for all data to simplify management

Question 5

A company is using Amazon Kinesis Data Streams to ingest clickstream data. The data is consumed by a fleet of EC2 instances running a custom consumer application. The consumer is falling behind and the shard iterator age is increasing. Which TWO actions should the data engineer take to improve consumer performance? (Choose TWO.)

Accepted Answer

Increase the number of shards in the stream. Increasing the number of shards increases parallelism, and using enhanced fan-out allows each consumer to have its own read throughput. Option B is wrong because decreasing retention period does not improve consumer performance. Option D is wrong because using a Lambda function may not help if the bottleneck is shard throughput. Option E is wrong because using KCL (Kinesis Client Library) is already standard; not a direct fix.

Answer

Decrease the data retention period

Answer

Use an AWS Lambda function to process the data

Answer

Switch to the Kinesis Client Library (KCL)

Question 6

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 7

A company uses AWS Lambda to process events from Amazon S3. The Lambda function transforms the data and writes results to another S3 bucket. Recently, the function has been failing due to timeout errors when processing large files. Which solution should the data engineer implement?

Accepted Answer

Use S3 Batch Operations with a Lambda function to process objects. Using S3 Batch Operations with a Lambda function allows processing large numbers of objects asynchronously, avoiding Lambda timeouts. Option A is wrong because increasing memory also increases CPU but may not solve timeout for large files. Option B is wrong because SQS does not help with processing large files. Option D is wrong because increasing timeout may not be sufficient and is not best practice for large files.

Answer

Increase the Lambda function memory and timeout limit

Answer

Increase the Lambda timeout to 15 minutes

Answer

Use Amazon SQS to queue the events and process them in batches

Question 8

A data engineer runs the above CLI command and sees that the bucket contains many small Parquet files (1 MB each) under the prefix. When querying this data with Athena, the query performance is poor and costs are high. Which approach would MOST improve performance and reduce cost?

Accepted Answer

Consolidate the small files into fewer, larger Parquet files. Consolidating small files into larger files (e.g., 100 MB) reduces the overhead of reading many small files in Athena. Option A is wrong because CSV is not columnar and would not improve performance. Option B is wrong because converting to JSON is similar. Option D is wrong because adding more partitions may create even more files.

Answer

Convert the files to JSON format

Answer

Convert the files to CSV format

Answer

Add more partitions by including hour in the prefix

Question 9

A machine learning team is using Amazon SageMaker to train a model on a dataset stored in S3. The training job reads data from S3 using Pipe input mode, but the training is slow. The team wants to improve data throughput. Which THREE actions should they take?

Accepted Answer

Mount the S3 bucket using an S3 file system and use File mode with a larger instance type.. Using Amazon S3 with a VPC endpoint improves network performance. Using Amazon EFS as a data source can provide higher throughput for sequential access. Using SageMaker's File mode with a larger instance type with more network bandwidth can also improve throughput.

Answer

Enable S3 Transfer Acceleration on the bucket.

Answer

Use Amazon ElastiCache to cache the training data.

Question 10

A company wants to centralize logging from multiple AWS accounts and on-premises servers. The logs must be stored cost-effectively and be searchable. Which TWO services should be used? (Choose TWO.)

Accepted Answer

Amazon Athena. Amazon S3 is a cost-effective storage for log archives, and Amazon Athena allows querying the logs directly on S3 without loading into a database. Option A (CloudWatch Logs) is for real-time monitoring, not long-term storage. Option C (Redshift) is more expensive. Option E (Kinesis) is for streaming, not storage.

Answer

Amazon CloudWatch Logs

Answer

Amazon Redshift

Answer

Amazon Kinesis Data Streams

Question 11

A data engineering team needs to move 10 TB of historical data from an on-premises Hadoop cluster to Amazon S3. The data is currently stored in HDFS. Which service should they use for an efficient transfer?

Accepted Answer

AWS DataSync. AWS DataSync can transfer data from on-premises HDFS to S3 efficiently, with built-in encryption and validation. Option A is wrong because S3 Transfer Acceleration speeds up transfers but requires network. Option C is wrong because Snowball Edge is for offline transfer. Option D is wrong because Kinesis is for streaming, not bulk historical.

Answer

S3 Transfer Acceleration

Answer

Amazon Kinesis Data Streams

Answer

AWS Snowball Edge

Question 12

A data engineer is investigating a slow Athena query on a partitioned table. The table is partitioned by year, month, and day, and the data is stored in S3 with the prefix pattern 'raw/YYYY/MM/DD/'. The engineer runs the above CLI command and sees that there are many small files. Which action would most improve query performance?

Accepted Answer

Use S3DistCp to coalesce files into fewer, larger files.. Athena performs best with larger files. Consolidating small files into fewer, larger files (e.g., using S3DistCp or Glue ETL) reduces the overhead of reading many small files and improves query performance.

Answer

Convert the data to columnar format like Parquet or ORC.

Answer

Increase the number of partitions in the Athena DDL.

Answer

Add more partitions to reduce the amount of data scanned per query.

Question 13

A data engineer is designing a data pipeline to process streaming data from Amazon Kinesis Data Streams and store the results in Amazon S3 in Parquet format. The data must be available for querying in Amazon Athena within minutes of arrival. Which THREE services should be used together? (Choose THREE.)

Accepted Answer

Amazon Kinesis Data Firehose. Kinesis Data Firehose can write data to S3 in Parquet format with near-real-time delivery. AWS Glue provides the Data Catalog for table metadata, and Athena queries the data. Option A (Kinesis Data Analytics) is for real-time analytics on streams, not for storage. Option C (EMR) is for batch processing, not streaming. Option E (Redshift) is for data warehousing, not immediate S3 querying.

Answer

Amazon EMR

Answer

Amazon Redshift

Answer

Amazon Kinesis Data Analytics

Question 14

A data engineer is designing a data lake on Amazon S3. The data is collected from IoT devices and is highly variable in volume. The engineer needs to ensure that the data is ingested reliably and can be processed in near real-time. Which AWS service should be used to ingest the data into the data lake?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is the best choice for reliably ingesting streaming data into S3 with near real-time delivery, automatic scaling, and optional transformations.

Answer

AWS Glue

Answer

Amazon Kinesis Data Streams

Answer

Amazon Simple Queue Service (SQS)

Question 15

A machine learning engineer is using Amazon SageMaker to train a model. The training dataset is 2 TB and is stored in Amazon S3. The engineer wants to reduce the training time by improving data loading performance. Which data ingestion mode should be used?

Accepted Answer

Pipe mode. SageMaker Pipe mode streams data directly from S3 to the training algorithm, which can reduce training time by overlapping data loading and training, especially for large datasets.

Answer

Incremental mode

Answer

File mode

Answer

Fast file mode