AWS Certified Machine Learning Specialty MLS-C01 practice test

A company is using Amazon Kinesis Data Streams to ingest real-time clickstream data. The data is consumed by a Lambda function that writes to an S3 bucket. Recently, the Lambda function started failing with 'ProvisionedThroughputExceededException' errors. What is the MOST likely cause?

Trap 1: The data retention period of the stream is too short.

Retention period does not affect throughput.

Trap 2: The S3 bucket has insufficient write capacity.

S3 scales automatically.

Trap 3: The Lambda function's reserved concurrency is set too high.

High concurrency would not cause throttling.

A
The data retention period of the stream is too short.
Why wrong: Retention period does not affect throughput.
B
The S3 bucket has insufficient write capacity.
Why wrong: S3 scales automatically.
C
The Kinesis stream has too few shards for the data volume.
Insufficient shards cause ProvisionedThroughputExceededException.
D
The Lambda function's reserved concurrency is set too high.
Why wrong: High concurrency would not cause throttling.

Question 2hardmultiple choice

A team is building a data pipeline to process terabytes of log data daily using Amazon EMR. The data arrives in 5-minute windows and must be available for querying within 30 minutes. The data is originally in gzip-compressed CSV files. Which approach will minimize processing time and cost?

Trap 1: Use Amazon EMR with Spark to convert data to Parquet and use…

On-demand instances are more expensive.

Trap 2: Use AWS Glue to convert data to gzip-compressed CSV and query with…

CSV is not optimal for Athena performance.

Trap 3: Use Amazon EMR with Hive to transform data to compressed CSV and…

CSV still incurs full scan costs.

A
Use Amazon EMR with Spark to convert data to Parquet and use on-demand instances.
Why wrong: On-demand instances are more expensive.
B
Use Amazon EMR with Spark to convert data to Parquet and store in S3, using spot instances for task nodes.
Parquet reduces scan size, spot instances reduce cost.
C
Use AWS Glue to convert data to gzip-compressed CSV and query with Athena.
Why wrong: CSV is not optimal for Athena performance.
D
Use Amazon EMR with Hive to transform data to compressed CSV and store in S3.
Why wrong: CSV still incurs full scan costs.

Question 3mediummultiple choice

A data science team is building a real-time fraud detection system. Transactions are streamed via Amazon Kinesis Data Streams, and a Lambda function performs feature engineering and invokes an Amazon SageMaker endpoint for predictions. The team notices that the Lambda function is timing out and causing data loss. Which solution should the team implement to process the stream reliably and at low latency?

Trap 1: Use the Kinesis Client Library (KCL) to process the stream in an…

KCL-based processing on EC2 adds operational overhead and does not directly address Lambda timeout issues; DynamoDB storage is not necessary for the pipeline.

Trap 2: Increase the Lambda function timeout to 15 minutes and allocate…

Lambda has a maximum timeout of 15 minutes, but this increases cost and still risks data loss if failures occur; it does not provide checkpointing.

Trap 3: Configure Amazon Kinesis Firehose to deliver the stream to an…

Kinesis Firehose introduces minutes of delay, which is unsuitable for real-time fraud detection.

A
Use Amazon Kinesis Data Analytics for Apache Flink to consume the stream, perform feature engineering, and invoke the SageMaker endpoint with exactly-once processing.
Kinesis Data Analytics provides stateful stream processing with checkpointing, ensuring no data loss and low-latency integration with SageMaker.
B
Use the Kinesis Client Library (KCL) to process the stream in an Amazon EC2 instance, and store the predictions in Amazon DynamoDB.
Why wrong: KCL-based processing on EC2 adds operational overhead and does not directly address Lambda timeout issues; DynamoDB storage is not necessary for the pipeline.
C
Increase the Lambda function timeout to 15 minutes and allocate more memory to reduce processing time.
Why wrong: Lambda has a maximum timeout of 15 minutes, but this increases cost and still risks data loss if failures occur; it does not provide checkpointing.
D
Configure Amazon Kinesis Firehose to deliver the stream to an Amazon S3 bucket, then trigger a Lambda function to process the data in batches.
Why wrong: Kinesis Firehose introduces minutes of delay, which is unsuitable for real-time fraud detection.

Question 4hardmultiple choice

A company uses Amazon SageMaker to train and deploy machine learning models. The training data is stored in Amazon S3 (Parquet format, 10 TB). The data scientists have been running training jobs using the File mode input, but the jobs are taking too long due to data download time. They want to reduce the training start-up time and overall training time. Which solution is MOST cost-effective and efficient?

Trap 1: Use S3 Transfer Acceleration to speed up the data transfer from S3…

S3 Transfer Acceleration optimizes uploads over long distances, but training jobs download data; it does not apply to SageMaker training jobs.

Trap 2: Use larger EC2 instances with more vCPUs and memory to speed up the…

Larger instances may speed up computation but do not reduce the data download time; they increase cost unnecessarily.

Trap 3: Enable Elastic Fabric Adapter (EFA) on the training instances to…

EFA is designed for inter-node communication, not for S3 data loading; it does not address the download bottleneck.

A
Configure the SageMaker training job to use Pipe mode, which streams data directly from S3 without downloading to the instance's local storage.
Pipe mode reduces start-up time by streaming data, and it is cost-effective as it avoids EBS volume costs associated with File mode.
B
Use S3 Transfer Acceleration to speed up the data transfer from S3 to the training instance.
Why wrong: S3 Transfer Acceleration optimizes uploads over long distances, but training jobs download data; it does not apply to SageMaker training jobs.
C
Use larger EC2 instances with more vCPUs and memory to speed up the training process.
Why wrong: Larger instances may speed up computation but do not reduce the data download time; they increase cost unnecessarily.
D
Enable Elastic Fabric Adapter (EFA) on the training instances to improve network throughput.
Why wrong: EFA is designed for inter-node communication, not for S3 data loading; it does not address the download bottleneck.

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Question 5easymultiple choice

A data engineer is building a data pipeline to process user clickstream data. The data arrives as JSON files in an S3 bucket. The pipeline must transform the JSON into Parquet format and partition by date and event type, then make the data available for Amazon Athena queries. The engineer needs a fully managed, serverless solution with minimal operational overhead. Which combination of AWS services should the engineer use?

Trap 1: Use Amazon EMR with Spark to read JSON, convert to Parquet, and…

EMR requires cluster management and is not serverless, increasing operational overhead.

Trap 2: Use AWS Glue ETL jobs to read JSON from S3, transform to Parquet,…

Glue ETL is serverless but has overhead; for simple transformations, Lambda is more efficient and cheaper.

Trap 3: Use Amazon Kinesis Firehose to ingest data and convert to Parquet,…

Kinesis Firehose is for streaming data, not batch files; it cannot handle existing S3 objects.

A
Use Amazon EMR with Spark to read JSON, convert to Parquet, and partition, then query with Athena.
Why wrong: EMR requires cluster management and is not serverless, increasing operational overhead.
B
Use AWS Glue ETL jobs to read JSON from S3, transform to Parquet, and write to a partitioned S3 location, then use Athena.
Why wrong: Glue ETL is serverless but has overhead; for simple transformations, Lambda is more efficient and cheaper.
C
Use S3 Event Notifications to trigger an AWS Lambda function that converts the JSON to Parquet and writes to a partitioned S3 location, then query with Athena.
Lambda is serverless, cost-effective for per-file processing, and can partition output easily.
D
Use Amazon Kinesis Firehose to ingest data and convert to Parquet, then write to S3, and query with Athena.
Why wrong: Kinesis Firehose is for streaming data, not batch files; it cannot handle existing S3 objects.

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Question 6mediummulti select

A data engineering team is designing a data lake on AWS for machine learning workloads. The data includes structured, semi-structured, and unstructured data. The team needs to ensure that the data is cataloged, easily discoverable, and can be queried by Amazon Athena and Amazon EMR. The team also wants to enforce fine-grained access control at the column and row level for sensitive data. Which combination of AWS services should the team use? (Select TWO.)

Trap 1: AWS Identity and Access Management (IAM)

IAM provides identity and access management but not column/row-level granularity.

Trap 2: Amazon RDS for PostgreSQL

RDS is a relational database, not a data catalog for the data lake.

Trap 3: Amazon DynamoDB

DynamoDB is a key-value store and not used for data lake cataloging.

A
AWS Lake Formation
Lake Formation provides fine-grained access control and integrates with Glue Catalog.
B
AWS Identity and Access Management (IAM)
Why wrong: IAM provides identity and access management but not column/row-level granularity.
C
AWS Glue Data Catalog
Glue Data Catalog is the central metadata repository for Athena, EMR, and other services.
D
Amazon RDS for PostgreSQL
Why wrong: RDS is a relational database, not a data catalog for the data lake.
E
Amazon DynamoDB
Why wrong: DynamoDB is a key-value store and not used for data lake cataloging.