MLS-C01 Practice Exam 2 — 20 Questions

Question 1

A data scientist is tuning a neural network on a small dataset and observes that the training loss decreases but validation loss increases after a few epochs. Which technique should be applied to mitigate this issue?

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

Accepted Answer

Add dropout layers to the model.. The observed behavior—training loss decreasing while validation loss increasing—is a classic sign of overfitting. Adding dropout layers randomly drops a fraction of neurons during training, which forces the network to learn more robust features and reduces co-adaptation, thereby mitigating overfitting. This technique is specifically designed to improve generalization on small datasets.

Answer

Increase the learning rate.

Answer

Remove regularization terms from the loss function.

Answer

Increase the number of epochs.

Question 2

A data scientist is tuning a gradient boosting model using Amazon SageMaker Automatic Model Tuning. The objective metric is AUC. The training job converges quickly but the final model has low AUC on the validation set. Which hyperparameter should the data scientist adjust to improve validation AUC?

Accepted Answer

Decrease the learning rate and increase the number of rounds. Decreasing the learning rate and increasing the number of rounds is the correct approach because a low learning rate forces the model to take smaller steps toward the optimum, reducing overfitting and allowing more trees to contribute to the ensemble. This combination often improves generalization and validation AUC when the training job converges too quickly, indicating that the model is overfitting or underfitting due to aggressive learning.

Answer

Increase the subsample ratio of training data

Answer

Increase the learning rate

Answer

Increase the maximum depth of trees

Question 3

A data scientist is building a time series forecasting model for monthly sales. The data shows strong seasonality with a yearly pattern. They plan to use Amazon Forecast. Which algorithm should they choose?

Accepted Answer

DeepAR+. DeepAR+ is purpose-built for time series forecasting with strong seasonality, as it uses recurrent neural networks (RNNs) to capture complex temporal dependencies and automatically models seasonal patterns like yearly cycles. Amazon Forecast natively supports DeepAR+ for such use cases, making it the optimal choice over general-purpose or non-forecasting algorithms.

Answer

XGBoost

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K-means clustering

Answer

Linear regression

Question 4

A data scientist is training a neural network for image classification. The dataset has 50,000 images across 100 classes. The model uses a ResNet-50 architecture pre-trained on ImageNet. The training loss decreases rapidly, but validation loss starts to increase after 5 epochs. Which of the following is the most effective technique to address this?

Accepted Answer

Use data augmentation to increase the diversity of the training set. The rapid decrease in training loss followed by an increase in validation loss after only 5 epochs is a classic sign of overfitting. Data augmentation artificially expands the training set by applying random transformations (e.g., rotations, flips, crops) to existing images, which improves the model's generalization and reduces overfitting. This is the most effective technique among the options because it directly addresses the lack of diverse training examples without changing the model architecture or training hyperparameters in a way that could destabilize learning.

Answer

Increase the learning rate

Answer

Add more layers to the network

Answer

Use a smaller batch size

Question 5

Refer to the exhibit. A data scientist runs the AWS CLI command to create a SageMaker training job. The training job fails because the input data is not accessible. Which step should the data scientist take to fix the issue?

Accepted Answer

Attach an IAM policy to SageMakerRole that grants s3:GetObject on the bucket. The training job fails because the SageMaker execution role (SageMakerRole) lacks the necessary IAM permissions to read the input data from the S3 bucket. By attaching an IAM policy that grants s3:GetObject on the specific bucket, the role gains the required access. SageMaker uses the execution role to access S3 data, so the role's permissions must explicitly allow the GetObject action.

Answer

Add a VpcConfig to the training job

Answer

Modify the bucket policy to allow s3:GetObject for any principal

Answer

Increase VolumeSizeInGB to 50

Question 6

A financial services company is deploying a machine learning model for credit risk assessment. The model must have an inference latency under 200ms and must be able to handle up to 1000 transactions per second (TPS). The company wants to minimize costs. The model is a gradient boosting model implemented in XGBoost. Which SageMaker deployment option should the team choose?

Accepted Answer

Deploy the model on a SageMaker real-time endpoint with multiple instances behind a load balancer.. A SageMaker real-time endpoint with multiple instances behind a load balancer provides the sub-200ms inference latency and 1000 TPS throughput required for synchronous, low-latency predictions. XGBoost models are well-suited for real-time endpoints, and horizontal scaling with multiple instances ensures high availability and cost efficiency by matching capacity to demand.

Answer

Use SageMaker Batch Transform to process transactions in batches.

Answer

Use SageMaker asynchronous inference for queued requests.

Answer

Use SageMaker Serverless Inference for automatic scaling.

Question 7

A machine learning team is using Amazon SageMaker to train a model with a custom algorithm packaged in a Docker container. The training job fails with the error 'Error: Unable to locate sagemaker-training toolkit.' What is the MOST likely cause?

Accepted Answer

The container does not include the SageMaker Training Toolkit. The SageMaker Training Toolkit is a required library that provides the interface between SageMaker and custom Docker containers for training jobs. When a custom container does not include this toolkit, SageMaker cannot execute the training script or communicate with the container, resulting in the 'Unable to locate sagemaker-training toolkit' error. This is a mandatory dependency for any custom training container used with SageMaker.

Answer

The container does not have internet access to download dependencies

Answer

The instance type is incompatible with the container

Answer

The training role does not have permissions to access the container repository

Question 8

Which THREE AWS services can be used together to build a serverless data pipeline that ingests streaming data, transforms it, and loads it into Amazon Redshift for analysis?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose (C) is correct because it is a fully managed, serverless service that can ingest streaming data and deliver it directly into Amazon Redshift (via S3 staging and the Redshift COPY command), making it ideal for the load stage of the pipeline. Amazon Kinesis Data Streams (D) is correct because it provides the serverless, scalable ingestion layer for capturing and buffering streaming data before it is consumed or transformed downstream. AWS Lambda (E) is correct because it is a serverless compute service that can be triggered by Kinesis to transform records in flight before they are loaded into Redshift, satisfying the transform requirement without managing servers. Amazon EMR (A) is not correct here because it is a managed big-data cluster service (EC2-based) rather than a serverless component, and it is not needed for this streaming ingest-transform-load pattern. Amazon SQS (B) is not correct because it is a message queue for decoupling applications, not a streaming data ingestion or delivery service for Redshift pipelines.

Answer

Amazon EMR

Answer

Amazon SQS

Question 9

A company uses Amazon Redshift for its data warehouse. The data engineering team needs to load 10 TB of data from Amazon S3 into Redshift every night. The team wants to minimize the load time and use the fewest number of COPY commands. The data is in CSV format and is partitioned by date in S3. Which approach should the team take?

Accepted Answer

Use a manifest file with a single COPY command.. Using a manifest file with a single COPY command is the most efficient approach because it allows Redshift to load data from multiple S3 objects (partitioned by date) in parallel, automatically splitting the workload across cluster nodes. This minimizes load time by leveraging Redshift's parallel processing without requiring multiple COPY commands or manual concatenation, and it avoids the overhead of additional services like AWS Glue for a straightforward bulk load.

Answer

Use multiple COPY commands, one per partition.

Answer

Concatenate all data into a single large file before loading.

Answer

Use AWS Glue to transform the data and then load into Redshift.

Question 10

A data scientist is building a regression model to predict house prices. The dataset contains features like 'number_of_rooms' (integer), 'sqft' (float), 'location' (categorical with 1000 unique values). Which feature engineering approach is BEST for the 'location' feature?

Accepted Answer

Target encoding. Target encoding is the best approach for the 'location' feature because it has 1,000 unique categories, making one-hot encoding infeasible (would create 1,000 dummy columns) and label encoding inappropriate (imposes arbitrary ordinal relationships). Target encoding replaces each category with the mean of the target variable (house price) for that category, capturing the predictive signal of location while keeping the feature as a single numeric column. This balances model performance with dimensionality and avoids overfitting when regularized (e.g., with smoothing or cross-validation).

Answer

Remove the feature

Answer

One-hot encoding

Answer

Label encoding

Question 11

Which TWO of the following are common techniques for handling missing values in a dataset during exploratory data analysis? (Select TWO.)

Accepted Answer

Remove rows or columns with missing values if they are few.. Option B is correct because listwise deletion (dropping rows) or dropping entire columns is a standard, simple technique when the proportion of missing data is small, minimizing information loss and bias. Option E is correct because mean or median imputation replaces missing entries with a central tendency statistic of the column, preserving sample size and being especially suitable for numerical features (median being robust to outliers). Option A is not a missing-value technique; feature scaling (e.g., Min-Max or StandardScaler) only rescales existing values and cannot handle NaNs. Option C, PCA, is a dimensionality-reduction method and does not address missing values directly (it typically requires complete data or prior imputation). Option D is incorrect because one-hot encoding converts categorical variables into binary vectors and does not fill or handle missing values.

Answer

Apply feature scaling to normalize the data.

Answer

Use Principal Component Analysis (PCA) to reduce dimensionality.

Answer

Apply one-hot encoding to the missing values.

Question 12

A data scientist uses SageMaker to train a model. The training job takes 10 hours, but the team needs to reduce costs. Which approach is MOST cost-effective?

Accepted Answer

Enable Managed Spot Training. Managed Spot Training on SageMaker uses spare EC2 capacity at up to 90% discount compared to on-demand pricing, making it the most cost-effective option for a 10-hour training job that can tolerate interruption. SageMaker automatically checkpoints and resumes training if the spot instance is reclaimed, so long jobs remain viable.

Answer

Use SageMaker Automatic Model Tuning

Answer

Use a larger instance type to finish faster

Answer

Use SageMaker Distributed Training with more instances

Question 13

A data scientist is training a neural network for image classification. The training loss is decreasing steadily, but the validation loss starts increasing after a few epochs. What is the MOST likely cause?

Accepted Answer

The model is overfitting to the training data. The validation loss increasing while the training loss continues to decrease is the classic signature of overfitting. The model is memorizing the training data (including noise) rather than learning generalizable patterns, causing it to perform poorly on unseen validation data.

Answer

The learning rate is too high

Answer

The gradients are vanishing

Answer

The model is underfitting

Question 14

A data scientist needs to run a one-time SQL query on a large dataset in Amazon S3. The dataset is stored in Parquet format and is about 500 GB. The query requires complex aggregations and joins. Which AWS service should be used to minimize cost and setup time?

Accepted Answer

Amazon Athena. Amazon Athena is the correct choice because it is a serverless query service that allows you to run SQL directly on data stored in S3 without provisioning any infrastructure. For a one-time query on 500 GB of Parquet data, Athena minimizes cost (pay-per-query, no idle cluster costs) and setup time (no cluster creation or data loading). Its ability to handle complex aggregations and joins on columnar formats like Parquet makes it ideal for this ad-hoc use case.

Answer

Amazon Redshift

Answer

Amazon RDS for MySQL

Answer

Amazon EMR with Spark SQL

Question 15

During EDA, a data scientist finds that a feature has a skewed distribution. They want to apply a log transformation to make it more Gaussian-like. Which Amazon SageMaker feature is most appropriate for this transformation?

Accepted Answer

Amazon SageMaker Data Wrangler. Amazon SageMaker Data Wrangler is designed for data preparation and feature engineering, including built-in transforms like log transformation to reduce skewness and make distributions more Gaussian-like. It provides a visual interface to apply, preview, and export these transformations directly into a SageMaker pipeline.

Answer

Amazon SageMaker Clarify

Answer

Amazon SageMaker JumpStart

Answer

Amazon SageMaker Ground Truth

Question 16

A company is using SageMaker to train a large NLP model. The training job is taking too long due to high I/O wait time. The data is stored as CSV files in S3. Which optimization should the company implement to reduce I/O wait time?

Accepted Answer

Use SageMaker Pipe mode to stream data directly from S3. SageMaker Pipe mode streams data directly from S3 into the training algorithm without first writing it to the local disk, eliminating the I/O wait time caused by downloading and decompressing CSV files. This is the most effective optimization for high I/O wait during training because it bypasses the bottleneck of writing large datasets to the instance's local storage.

Answer

Convert CSV files to RecordIO format

Answer

Use SageMaker batch transform before training

Answer

Use SageMaker File mode with larger instance storage

Answer

Use SageMaker ShardedByS3Key data distribution

Question 17

A machine learning engineer is building a pipeline to preprocess data and train a model using Amazon SageMaker. The data is stored in Amazon S3 and the preprocessing step is computationally intensive. The engineer wants to minimize costs while ensuring that the preprocessing step does not fail due to instance termination. Which instance type should be used for the preprocessing step?

Accepted Answer

On-demand instances. On-demand instances (Option B) are the correct choice because they provide reliable, non-interruptible compute capacity for the preprocessing step. Spot instances can be terminated at any time (with a 2-minute warning) when AWS reclaims capacity, which would cause the computationally intensive preprocessing to fail. Reserved instances require a 1- or 3-year commitment and are not cost-effective for a single preprocessing job that may not run continuously.

Answer

Reserved instances

Answer

A larger instance type to speed up processing

Answer

Spot instances

Question 18

Which THREE techniques can help reduce overfitting in a neural network trained on a small dataset?

Accepted Answer

Apply L2 weight regularization. L2 weight regularization (also known as weight decay) penalizes large weights by adding a term to the loss function proportional to the sum of squared weights. This forces the network to learn simpler patterns and reduces sensitivity to noise in the training data, which is especially helpful when the dataset is small and prone to overfitting.

Answer

Increase the number of hidden layers

Answer

Train for more epochs

Question 19

Which THREE factors should be considered when choosing an instance type for a SageMaker training job?

Accepted Answer

The number of vCPUs needed for parallel processing. Option A is correct because the number of vCPUs directly determines how many parallel data-processing threads or worker processes the training job can run, which affects throughput for CPU-bound workloads such as data loading, augmentation, and traditional ML algorithms. Option B is correct because the instance's RAM must accommodate the model parameters, gradients, optimizer states, and the training batch in memory; insufficient memory causes out-of-memory failures or forces smaller batch sizes. Option E is correct because GPU requirements (type, count, and GPU memory) are a primary driver for deep learning training, since frameworks like TensorFlow and PyTorch rely on CUDA-enabled GPUs such as the P3 or G4dn families for accelerated matrix operations. Option C is not relevant because endpoint latency concerns real-time inference hosting, not the training job itself. Option D is not a factor in instance-type selection because the AWS region determines availability and pricing, but the instance type is chosen based on compute, memory, and accelerator needs regardless of region.

Answer

The endpoint latency requirement

Answer

The AWS region where the instance is launched

Question 20

A data scientist wants to understand the statistical relationship between two categorical variables in a dataset. Which test is most appropriate?

Accepted Answer

Chi-squared test. The chi-squared test is used to determine if there is a significant association between two categorical variables, which is exactly what the data scientist wants to understand. Option B (Pearson correlation coefficient) is incorrect because it measures linear relationship between two continuous variables. Option C (Student's t-test) is used to compare means of two groups, typically for continuous data. Option D (ANOVA) is used to compare means across three or more groups, also for continuous data.

Answer

Pearson correlation coefficient

Answer

Student's t-test

Answer

ANOVA test