MLS-C01 Practice Test 24 — 15 Questions

Question 1

A team is training a large language model using PyTorch on multiple GPUs. The training is taking too long due to inefficient data loading. Which AWS service can help accelerate data loading by caching data close to the GPU instances?

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

Accepted Answer

Amazon FSx for Lustre. Amazon FSx for Lustre is a high-performance file system optimized for machine learning workloads. It provides sub-millisecond latencies and high throughput by caching training data on local NVMe SSDs attached to the Lustre servers, which are co-located with GPU instances in the same AWS Availability Zone. This eliminates the I/O bottleneck from remote object storage, directly accelerating data loading for PyTorch DataLoader workers.

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Amazon EBS Snapshots for fast restore

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Amazon S3 Transfer Acceleration

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Amazon CloudFront

Question 2

A company wants to use Amazon Rekognition to detect objects in images stored in an S3 bucket. The images are uploaded by users. Which IAM policy statement is necessary to allow Rekognition to read from the bucket?

Accepted Answer

s3:GetObject. Amazon Rekognition needs to read the image files from the S3 bucket to perform object detection. The s3:GetObject permission grants read access to the objects stored in the bucket, which is the minimum required action for Rekognition to retrieve and analyze the images.

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s3:PutObject

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s3:DeleteObject

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s3:ListBucket

Question 3

Refer to the exhibit. A custom training job using Pipe input mode fails. The logs indicate the algorithm cannot read the data. What is the most likely issue?

Accepted Answer

The algorithm expects File mode but Pipe mode is specified. The most likely issue is that the algorithm expects File mode but Pipe mode is specified. In SageMaker, Pipe mode streams data directly from S3 to the training container, but some algorithms are not designed to read from a pipe and require files to be present on the local file system. This mismatch causes the algorithm to fail to read the data.

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The instance type is too small for the data

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The training data is compressed

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The training image is not accessible

Question 4

A company is using AWS Glue to run ETL jobs that process data from Amazon RDS to Amazon S3. The ETL jobs are failing intermittently with write timeout errors when writing to S3. The company wants to implement a retry mechanism for transient errors. What should the company do?

Accepted Answer

Configure the AWS Glue job to retry on failure by setting the 'Max retries' parameter. AWS Glue jobs support a 'Max retries' parameter (set to 0–10) that automatically retries the entire job run when it fails. For intermittent S3 write timeouts caused by transient network or service issues, configuring Max retries is the native, low-effort way to handle transient failures without manual intervention.

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Increase the size of the Amazon EBS volumes attached to the Glue job

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Use Amazon CloudWatch to monitor the job and manually restart on failure

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Place the failed job messages in an Amazon SQS queue and reprocess them

Question 5

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 is the correct choice because it is designed to efficiently transfer large volumes of data from on-premises storage systems, including HDFS, to AWS services like Amazon S3. It uses a purpose-built network protocol and parallel multi-threading to optimize transfer speed over the internet or AWS Direct Connect, and it can handle the 10 TB volume without requiring physical appliances or complex streaming setups.

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S3 Transfer Acceleration

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Amazon Kinesis Data Streams

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AWS Snowball Edge

Question 6

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.

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Amazon Kinesis Data Streams with a Lambda function.

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AWS Data Pipeline with a SQL activity.

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AWS Glue with a scheduled crawler.

Question 7

A machine learning engineer needs to deploy a real-time inference endpoint for a model that requires GPU acceleration for low latency. Which AWS service should be used?

Accepted Answer

Amazon SageMaker real-time endpoint. Amazon SageMaker real-time endpoints are designed for low-latency, synchronous inference and support GPU instances for accelerated compute. They provide a persistent HTTPS endpoint that can be integrated with applications. Batch transform is for asynchronous, large-batch processing and does not provide a real-time endpoint. EC2 with auto scaling requires manual setup of inference server and scaling, lacking the managed experience. AWS Lambda does not support GPU acceleration.

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Amazon SageMaker batch transform

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Amazon EC2 with auto scaling

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AWS Lambda with GPU

Question 8

A company uses Amazon Kinesis Data Streams to ingest clickstream data from a website. The data is consumed by a custom application that runs on Amazon EC2 instances. The company notices that the consumer application is falling behind the producer, causing data to be throttled. Which action should the company take to improve the consumer's throughput?

Accepted Answer

Increase the number of shards in the Kinesis data stream. Increasing the number of shards in the Kinesis data stream increases the total read throughput available to consumers, because each shard supports up to 2 MB/sec read and 5 transactions per second. If the consumer application is falling behind and data is being throttled, adding shards provides more parallelism and higher aggregate read capacity. The consumer application must also scale its processing (e.g., more EC2 instances or threads) to read from the additional shards.

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Reduce the data retention period of the stream

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Increase the maximum concurrency of the AWS Lambda function that processes the stream

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Use Amazon Kinesis Data Firehose to deliver data to Amazon S3

Question 9

A company uses Amazon Redshift as a data warehouse. They need to load 50 TB of clickstream data from S3 into Redshift daily. The data arrives in 5-minute intervals as gzipped CSV files. The target table has a sort key and a distribution key. The load must complete within 2 hours. Which approach is MOST efficient?

Accepted Answer

Use the COPY command with a manifest file and gzip compression.. The COPY command is the most efficient way to load large volumes of data into Amazon Redshift because it uses the cluster's massively parallel processing (MPP) architecture to read data directly from S3 in parallel across all nodes. With a manifest file, you can specify multiple gzipped CSV files, and the gzip compression reduces network I/O and storage overhead. This approach can easily load 50 TB within 2 hours, especially when the target table has a sort key and distribution key, as COPY automatically leverages these for optimal data distribution and sorting during the load.

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Use AWS Glue to transform the data and write to Redshift using JDBC.

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Use a staging table and then merge using a stored procedure.

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Use a series of INSERT statements from a Lambda function.

Question 10

A data scientist needs to implement a recommendation system for an e-commerce website. Which Amazon service is specifically designed for building and deploying recommendation models?

Accepted Answer

Amazon Personalize. Amazon Personalize is a fully managed machine learning service that provides real-time personalized recommendations. It is purpose-built for recommendation systems. SageMaker is a general-purpose ML platform, but Personalize is specialized for recommendations.

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Amazon SageMaker

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Amazon Rekognition

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Amazon Forecast

Question 11

A data scientist is tuning a linear regression model and observes that the model has high bias and low variance. Which action is most likely to improve model performance?

Accepted Answer

Add more features. High bias and low variance indicate underfitting, meaning the model is too simple to capture the underlying patterns in the data. Adding more features increases model complexity, allowing it to learn more relevant relationships and reduce bias. This directly addresses the core issue of underfitting in linear regression.

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Reduce the number of features

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Increase regularization

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Reduce the amount of training data

Question 12

Which TWO services can be used to serve machine learning models for real-time inference? (Select TWO.)

Accepted Answer

Amazon SageMaker. Amazon SageMaker (C) is correct because it provides fully managed endpoints (real-time inference endpoints) that host trained models and serve low-latency predictions over HTTPS, which is exactly what real-time ML inference requires. AWS Lambda (E) is also correct because it can run inference code in response to events or API Gateway requests, invoking a model (e.g., from S3 or a container image) and returning predictions synchronously with low latency. Amazon Rekognition (A) and Amazon Comprehend (D) are pre-built AI services for specific tasks like image analysis and NLP, not general-purpose services for serving your own ML models for real-time inference. Amazon Athena (B) is an interactive query service for analyzing data in S3 with SQL, not a model-serving service.

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Amazon Rekognition

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Amazon Athena

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Amazon Comprehend

Question 13

A data scientist is training a classification model on a dataset with missing values in several features. The data scientist wants to use SageMaker to train the model. Which TWO approaches can the data scientist use to handle missing data within the SageMaker training pipeline? (Choose two.)

Accepted Answer

Use the SageMaker built-in XGBoost algorithm, which can handle missing values by default.. Option A is correct because the SageMaker built-in XGBoost algorithm natively handles missing values by learning a default direction for missing data at each tree split, so no explicit imputation is required before training. Option D is correct because SageMaker Processing lets you run a custom Python script (for example, using pandas fillna or scikit-learn SimpleImputer) as a preprocessing step to impute missing values before the training job consumes the data. Option B is incorrect because BlazingText is a text-classification/word-embedding algorithm and does not perform automatic imputation of missing feature values. Option C is incorrect because an Inference Pipeline addresses preprocessing at inference time, not handling missing data during the training pipeline. Option E is incorrect because SageMaker PCA does not automatically impute missing values and generally requires complete numeric input.

Answer

Use the SageMaker BlazingText algorithm, which automatically imputes missing values.

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Use SageMaker Inference Pipeline to handle missing values at inference time.

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Use SageMaker PCA algorithm, which automatically handles missing values.

Question 14

A team is building a model to predict customer churn. They have 50 features, including categorical variables with high cardinality (e.g., zip code with 10,000 unique values). Which feature engineering technique is most appropriate?

Accepted Answer

Target encoding. Target encoding replaces each high-cardinality category (e.g., each of the 10,000 zip codes) with a statistic derived from the target variable, such as the mean churn rate for that zip code. This compresses 10,000 unique values into a single numeric column, preserving predictive signal without exploding dimensionality. It is the standard technique for high-cardinality categoricals in tabular ML.

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Binning zip codes into regions

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Label encoding

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One-hot encoding

Question 15

A data scientist is analyzing a dataset with 1 million rows and 50 features. The scientist wants to detect outliers in a numerical feature 'transaction_amount' which has a long right tail. The scientist suspects that outliers are due to data entry errors and should be removed. Which outlier detection method is MOST robust for this scenario?

Accepted Answer

Interquartile range (IQR) with multiplier 1.5. The Interquartile Range (IQR) method with a multiplier of 1.5 is robust to skewed distributions because it uses quartiles, which are less affected by extreme values than mean and standard deviation. For a feature with a long right tail, IQR effectively identifies outliers beyond the upper fence (Q3 + 1.5*IQR) without assuming normality. This makes it the most robust choice for detecting data entry errors in a skewed numerical feature.

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Mahalanobis distance

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Z-score with threshold 3

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DBSCAN clustering