MLS-C01 Practice Test 24 — 15 Questions

Question 1

A data science team uses Amazon SageMaker to train models on a dataset stored in Amazon S3. The dataset is 2 TB and is accessed by multiple training jobs. The team notices that training jobs are slow due to high S3 GET request latency. Which solution would provide the fastest and most cost-effective data access?

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

Accepted Answer

Mount an Amazon FSx for Lustre file system integrated with the S3 bucket. Amazon FSx for Lustre provides a high-performance, POSIX-compliant file system that can be directly linked to an S3 bucket, allowing training instances to access data with sub-millisecond latency instead of S3 GET request latency. This integration enables data to be read from the Lustre file system at up to hundreds of gigabytes per second of throughput, which is significantly faster than reading directly from S3, and it is cost-effective because you only pay for the storage and throughput you provision during training.

Answer

Place all training instances in a Cluster Placement Group

Answer

Enable S3 Transfer Acceleration on the bucket

Answer

Use Elastic Fabric Adapter (EFA) for training instances

Question 2

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.. AWS Lambda functions can be triggered directly by S3 events (e.g., ObjectCreated) to run a custom Python transformation script on the incoming data, and then invoke a SageMaker endpoint for real-time inference. This combination meets the serverless infrastructure requirement and provides automatic, event-driven processing without managing any servers.

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 3

Refer to the exhibit. A CloudFormation template creates an S3 bucket. The data engineering team stores daily log files in this bucket and queries them using Amazon Athena. After 30 days, queries on logs older than 30 days start failing with 'Access Denied' errors. What is the MOST likely reason?

Accepted Answer

The lifecycle rule transitions objects to GLACIER after 30 days, making them inaccessible to Athena.. Amazon Athena reads data directly from S3 and does not support querying objects stored in the GLACIER storage class because GLACIER objects are not retrievable in real time. The lifecycle rule transitions objects to GLACIER after 30 days, so when Athena attempts to read those older objects, it receives 'Access Denied' errors because the objects are no longer in a queryable storage class.

Answer

The bucket uses default encryption with SSE-S3, which Athena does not support.

Answer

The lifecycle rule deletes objects after 30 days.

Answer

The bucket policy denies access to objects older than 30 days.

Question 4

A company uses Amazon Kinesis Data Analytics for Apache Flink to process real-time clickstream data. The application uses event time and watermarks for windowed aggregations. The team notices that the output from tumbling windows is delayed, and many late records are being dropped. What is the MOST likely cause?

Accepted Answer

The allowed lateness is set too low, causing late records to be discarded. The described symptoms—delayed output and dropped late records—are classic indicators that the `allowedLateness` parameter is set too low. In Apache Flink, event-time processing relies on watermarks to determine when a window is complete; if `allowedLateness` is too short, any record arriving after the watermark passes the window's end time is discarded as late. The team's observation that many late records are being dropped directly points to this configuration issue.

Answer

The checkpointing interval is too long, causing state to be lost

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The parallelism is too low, causing backpressure

Answer

The source is marking itself as idle, causing watermarks to stall

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. Amazon Kinesis Data Analytics (B) can process streaming data from Kinesis Data Streams in real-time using SQL or Apache Flink, and AWS Lambda (E) can be used as a consumer to write the processed results to DynamoDB. This combination provides a fully managed, serverless pipeline for real-time ingestion, processing, and storage.

Answer

Amazon Athena

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AWS Glue

Answer

Amazon S3

Question 6

A company uses AWS Glue Data Catalog to manage metadata for its data lake on Amazon S3. The data lake contains terabytes of data in CSV format. The data engineering team wants to improve query performance in Amazon Athena and reduce costs. Which actions should the team take? (Select THREE.)

Accepted Answer

Compress the data using Snappy or GZIP.. Compressing CSV data with Snappy or GZIP reduces the amount of data scanned by Athena, directly lowering query costs (Athena charges per TB scanned). Snappy offers faster decompression for better query performance, while GZIP provides higher compression ratios. Both formats are natively supported by Athena and reduce I/O from S3.

Answer

Create views in Athena to simplify queries.

Answer

Convert the data to JSON format.

Question 7

An ML engineer is using Amazon SageMaker to train a model on a dataset that contains personal identifiable information (PII). The data must be encrypted at rest and in transit. The company uses AWS KMS for key management. How should the engineer configure the SageMaker training job to meet these encryption requirements?

Accepted Answer

Specify a KMS key for the training job's VolumeKmsKeyId and enable inter-container traffic encryption. It addresses both encryption at rest and in transit for the SageMaker training job. Specifying a KMS key via VolumeKmsKeyId encrypts the ML storage volume (EBS) used by the training instances at rest, while enabling inter-container traffic encryption ensures data exchanged between distributed training containers is encrypted in transit using TLS. This combination meets the PII encryption requirements using AWS KMS.

Answer

Enable S3 Server-Side Encryption (SSE-S3) on the input data bucket

Answer

Use a custom Docker image with built-in encryption and disable inter-container traffic encryption for performance

Answer

Use a VPC with an S3 VPC Endpoint and enable SSL for the endpoint

Question 8

A company is running a machine learning training job on Amazon SageMaker that reads training data from an S3 bucket. The job fails intermittently with an S3 throttling error. The data is partitioned across thousands of small files (average 100 KB). Which strategy is MOST effective to resolve the throttling issue?

Accepted Answer

Combine the small files into larger files (e.g., 100 MB) using a preprocessing step. S3 throttling errors (HTTP 503) occur when many small files cause a high request rate per prefix. By combining thousands of 100 KB files into fewer 100 MB files, you drastically reduce the number of GET requests, staying within S3's 5,500 GET requests per second per prefix limit. This preprocessing step directly addresses the root cause of the throttling without changing the training infrastructure.

Answer

Use Amazon Athena to query the data and output results to a new S3 location

Answer

Enable S3 Transfer Acceleration on the bucket

Answer

Increase the number of SageMaker training instances to distribute the load

Question 9

A data engineer is building a data pipeline using AWS Glue. The pipeline reads data from Amazon S3, transforms it, and writes it back to S3 in a different format. The engineer needs to handle schema evolution (new columns added over time). Which TWO features of AWS Glue can help manage schema evolution?

Accepted Answer

AWS Glue Data Catalog. AWS Glue Data Catalog is correct because it stores schema metadata and can be updated automatically or manually to reflect new columns added to source data, enabling schema evolution tracking. AWS Glue DynamicFrame is correct because it provides a flexible, schema-on-read structure that can accommodate varying schemas across records, allowing transformations to handle new columns without breaking the pipeline.

Answer

AWS Lake Formation

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Amazon Athena

Answer

Amazon S3 object tags

Question 10

A data engineer needs to set up a data pipeline that ingests data from an Amazon RDS MySQL database into Amazon S3. The pipeline should run daily and capture incremental changes (inserts, updates, deletes) from the source database. Which AWS service should be used as the data ingestion tool?

Accepted Answer

AWS Database Migration Service (DMS) with continuous change data capture (CDC).. AWS DMS with continuous CDC is the correct choice because it is specifically designed to capture incremental changes (inserts, updates, deletes) from a relational database like Amazon RDS MySQL and replicate them to Amazon S3. DMS uses the MySQL binary log (binlog) to track row-level changes in near real-time, making it ideal for daily incremental pipelines. Other services either lack native CDC support or are not optimized for database-to-object-store incremental ingestion.

Answer

Amazon Kinesis Data Streams with a Lambda function.

Answer

AWS Data Pipeline with a SQL activity.

Answer

AWS Glue with a scheduled crawler.

Question 11

Match each data format to its typical use in AWS ML.

Accepted Answer

CSV: Tabular data for built-in algorithms. Common data formats in AWS ML: CSV for tabular data, JSON for semi-structured inference I/O, Parquet for columnar analytics, and TFRecord for TensorFlow training. Distractors confuse CSV with Parquet and JSON with binary formats.

Answer

CSV: Columnar storage for analytics

Answer

JSON: Binary format for efficient I/O

Question 12

Refer to the exhibit. A data scientist wants to update the endpoint to use a new model image. The scientist updates the endpoint configuration with the new image and calls UpdateEndpoint. After the update, the endpoint status is 'Updating' but remains in that state for a long time. What is the most likely cause?

Accepted Answer

The new model image is failing health checks. Blue/green deployment requires the new model to be healthy before traffic is shifted. If the new model fails health checks, the update may hang. Option B is wrong because the endpoint is updating, not failed. Option C is wrong because the old model is still running. Option D is wrong because there is no indication of insufficient capacity.

Answer

The endpoint is already InService, so it cannot be updated

Answer

The old model image is no longer available

Answer

The instance count is too low to deploy both variants

Question 13

A company has an AWS Glue ETL job that reads data from an Amazon RDS for MySQL table and writes to Amazon S3 in Parquet format. The job runs daily and processes 500 GB of data. Recently, the job has been failing with memory errors during the write phase. The data schema is wide (200 columns). Which change should a data engineer make to the Glue job to resolve the memory issue?

Accepted Answer

Configure the write operation with 'groupSize' to limit records per file.. The memory error occurs because the wide schema (200 columns) and large data volume (500 GB) cause the Spark executors to run out of memory when writing Parquet files, as each executor attempts to buffer entire partitions. Configuring 'groupSize' limits the number of records written per file, reducing the per-executor memory footprint and preventing out-of-memory errors during the write phase.

Answer

Increase the number of DPUs for the Glue job.

Answer

Change the output format from Parquet to CSV.

Answer

Use the JDBC connection with fetchSize parameter.

Question 14

A data scientist is using Amazon SageMaker built-in XGBoost algorithm to train a regression model. The training job completes successfully but the model performance on the test set is poor, with high bias. Which hyperparameter adjustment is most likely to help reduce bias?

Accepted Answer

Increase the max_depth parameter.. High bias (underfitting) can be reduced by increasing the model complexity. Increasing max_depth allows more complex trees. Decreasing max_depth would increase bias. Increasing gamma increases regularization and bias. Reducing num_round (number of trees) reduces complexity.

Answer

Reduce the num_round parameter.

Answer

Increase the gamma parameter.

Answer

Decrease the max_depth parameter.

Question 15

A data engineer is designing a streaming pipeline using Amazon Kinesis Data Analytics for Apache Flink. The pipeline reads from a Kinesis data stream and writes to a S3 bucket. The job must recover quickly from failures without reprocessing large amounts of data. Which TWO configurations should be used? (Choose TWO)

Accepted Answer

Enable checkpointing with a state backend like RocksDB.. Enabling checkpointing with a state backend like RocksDB allows Apache Flink to periodically save the state of the streaming application to durable storage. In the event of a failure, Flink can restart from the last completed checkpoint, avoiding the need to reprocess large amounts of data from the beginning of the stream. RocksDB is specifically designed for large state and provides fast recovery by storing state on disk with memory caching, making it ideal for production streaming pipelines.

Answer

Use in-memory state backend for low latency.

Answer

Set the parallelism to the maximum number of shards.

Answer

Increase the retention period of the Kinesis stream to 365 days.