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Hard Difficulty Questions

Practise AWS Certified Machine Learning Specialty MLS-C01 practice questions — original exam-style scenarios covering every exam domain, with detailed explanations, wrong-answer analysis, and common exam traps.

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MLS-C01
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Amazon Web Services
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Scenario guide

How to approach hard difficulty questions

These are the questions most candidates get wrong. They require connecting multiple concepts, reading tricky output, or knowing edge-case behaviour that isn't on most study cards. Practising them trains you to operate under uncertainty — a necessary skill on the real exam.

Quick answer

Hard Difficulty Questions questions test whether you can apply the concept in context, not just recognise a definition.

How the topic appears in realistic exam-style scenarios.

Which detail in the question changes the correct answer.

How to eliminate plausible but wrong options.

How to connect the question back to the wider exam objective.

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Practice set

Practice scenarios

Question 1hardmulti select
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A data engineering team is designing a streaming data pipeline that ingests 10,000 events per second. Each event is 2 KB. The pipeline must process events with a latency of less than 1 second. The team is considering using Amazon Kinesis Data Streams with 10 shards. Which TWO additional configurations should the team implement to meet the latency requirement? (Choose two.)

Question 2hardmultiple choice
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An ML team is deploying a model to a SageMaker endpoint for real-time inference. The model is large (2 GB) and requires GPU for low-latency inference. The team wants to minimize cost while maintaining a response time of under 200 ms. Which instance configuration and SageMaker feature would be best?

Question 3hardmultiple choice
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A company uses Amazon SageMaker to deploy a model for real-time predictions. The model is updated weekly. The company wants to ensure that the new model version is gradually rolled out to a small percentage of traffic before full deployment, and that it can be rolled back quickly if issues are detected. Which deployment strategy should be used?

Question 4hardmultiple choice
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A team wants to automate the retraining of a model weekly using new data that arrives in S3. Which combination of services should they use?

Question 5hardmulti select
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A data engineer needs to set up a data lake on S3 that supports both batch and streaming ingestion. The data must be queryable by Athena, Redshift Spectrum, and EMR. Which TWO configurations are essential? (Choose two.)

Question 6hardmulti select
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A company is using Amazon SageMaker to build a custom model. The training job is failing with a 'ResourceLimitExceeded' error. Which TWO actions should the company take to resolve this issue?

Question 7hardmultiple choice
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A data scientist is performing EDA on a dataset with 1,000 features and 10,000 rows. The target variable is binary. After checking for multicollinearity, the scientist finds many pairs of features with correlation > 0.95. Which action should be taken to prepare the data for modeling?

Question 8hardmultiple choice
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A data scientist is training a model using Amazon SageMaker. The training dataset is 500 GB and is stored in S3. The data scientist wants to use Pipe input mode to stream data directly from S3 to the training container. However, the training job fails with an error indicating that the container cannot read the data. What is the most likely cause?

Question 9hardmultiple choice
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A company uses Amazon SageMaker to train a deep learning model for image classification. The training dataset consists of 500,000 images, each 256x256 pixels, stored in S3. The team uses a single ml.p3.2xlarge instance for training. The training time is unacceptably long (over 48 hours). The team wants to reduce training time without sacrificing model accuracy. They have already optimized the data pipeline by using SageMaker Pipe mode and sharding the S3 dataset. The model is a ResNet-50 implemented in TensorFlow. The team is considering the following options: A) Switch to a ml.p3.16xlarge instance which has 8 GPUs and more memory. B) Implement distributed data parallelism using Horovod across multiple instances. C) Use SageMaker's built-in Hyperparameter Tuning to find optimal hyperparameters. D) Reduce the image resolution to 128x128 to speed up training. Which option will MOST effectively reduce training time while maintaining accuracy?

Question 10hardmultiple choice
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A company uses Amazon EMR to run Spark jobs on a transient cluster that processes data from S3. The jobs are failing with 'OutOfMemory' errors. The data engineer has already increased the executor memory. Which additional configuration change would MOST likely resolve the issue?

Question 11hardmulti select
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A data scientist is using Amazon SageMaker to train a model using a custom Docker container. The training job fails with an error message indicating that the container exited with a non-zero code. Which THREE steps should the data scientist take to diagnose the issue? (Choose THREE.)

Question 12hardmultiple choice
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A data pipeline uses AWS Lambda to process small files (10-50 MB) from an S3 bucket and write results to DynamoDB. The Lambda function times out after 15 seconds for larger files. The team wants to handle files up to 100 MB without changing the Lambda code. Which solution is MOST cost-effective?

Question 13hardmulti select
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A data scientist is analyzing a dataset with high multicollinearity. Which TWO techniques can help identify and address multicollinearity?

Question 14hardmultiple choice
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A training job log shows this error. The training instance is an ml.m5.large with 8 GB EBS storage. The training data is 500 MB, and the model size is expected to be 200 MB. What is the most likely cause?

Exhibit

Refer to the exhibit.

```
2024-01-15 10:23:45,123 - sagemaker - INFO - Training job created
2024-01-15 10:23:46,456 - sagemaker - INFO - Starting training...
2024-01-15 10:23:50,789 - root - ERROR - OSError: [Errno 28] No space left on device
```
Question 15hardmultiple choice
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A company wants to build a machine learning model to predict customer churn. The dataset includes customer demographics, usage patterns, and support interactions. The data is stored in Amazon S3. The data scientist needs to perform feature engineering, including creating aggregate features from support interactions and encoding categorical variables. Which AWS service is most suitable for building the feature engineering pipeline?

Question 16hardmulti select
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A data scientist is analyzing a dataset with a continuous target variable and suspects that the relationship between a predictor and the target is non-linear. Which THREE techniques can the scientist use to explore and model this non-linearity?

Question 17hardmultiple choice
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A company is building a near-real-time dashboard using data from multiple sources. They need to aggregate millions of events per second with sub-second latency. The architecture must be fully managed and minimize operational overhead. Which service should they use for the aggregation layer?

Question 18hardmultiple choice
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An e-commerce company uses Amazon Kinesis Data Firehose to deliver clickstream data to Amazon S3. The data arrives at unpredictable rates, with occasional bursts. The company needs to ensure data is delivered within 60 seconds of ingestion, and the data must be partitioned by year/month/day/hour. Which configuration meets these requirements?

Question 19hardmultiple choice
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A data engineer is designing a data pipeline that transforms raw JSON files (each 50-200 KB) in Amazon S3 into Parquet format using AWS Glue. The pipeline must minimize data processing costs and handle a high volume of small files (millions per day). The engineer configures a Glue ETL job with Spark, but the job is slow and expensive due to overhead of reading many small files. Which optimization should the engineer implement to reduce cost and improve performance?

Question 20hardmultiple choice
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A company runs an e-commerce platform that generates clickstream data in real-time. The data is ingested into Amazon Kinesis Data Streams (100 shards) and processed by AWS Lambda functions, which aggregate data in 1-minute windows and write the results to Amazon S3. The Lambda functions are triggered by the Kinesis stream using the event source mapping. Recently, the company noticed that some records are being processed multiple times, leading to duplicate data in S3. The Lambda function is idempotent, but the duplicates are causing downstream issues. The Lambda function's concurrency limit is 1000, and the batch size is 100. The average processing time per record is 200 ms. What is the most likely cause of the duplicates, and how should it be fixed?

These MLS-C01 practice questions are part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style MLS-C01 questions with detailed explanations, topic-based practice, mock exams, readiness tracking, and study analytics.