MLS-C01 Practice Test 17 — 15 Questions

Question 1

A data scientist is using SageMaker to train a deep learning model with a large dataset stored in S3. The training is taking a long time. Which action would most likely reduce training time without sacrificing accuracy?

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

Accepted Answer

Use SageMaker Pipe Input mode. SageMaker Pipe Input mode streams training data directly from S3 into the algorithm without first downloading it to the local EBS volume. This eliminates the I/O bottleneck caused by large dataset downloads, significantly reducing training time while preserving accuracy because the model sees the same data.

Answer

Increase the batch size

Answer

Use a smaller instance type

Answer

Reduce the number of epochs

Question 2

A data scientist loads a large dataset from Amazon S3 into a pandas DataFrame using a SageMaker notebook. The dataset contains a mix of numeric and categorical features. The data scientist wants to quickly check for missing values. Which pandas function is most appropriate?

Accepted Answer

df.isnull().sum(). Df.isnull().sum() returns the count of missing values per column. Option A is wrong because df.info() provides column data types and non-null counts, but not missing value counts directly. Option B is wrong because df.describe() only summarizes numeric columns. Option C is wrong because df.shape returns the dimensions, not missing values.

Answer

df.info()

Answer

df.describe()

Answer

df.shape

Question 3

A company is using Amazon SageMaker to train a model using a custom Docker container. The training script writes model artifacts to the `/opt/ml/model` directory. The training job completes successfully, but the model artifacts are not uploaded to the S3 output path specified in the training job. The company has verified that the SageMaker execution role has the necessary S3 permissions. The Docker container is built using a base image that is not one of the official SageMaker Docker images. What is the MOST likely reason for the failure to upload model artifacts?

Accepted Answer

The custom container does not include the SageMaker training toolkit, which handles artifact uploads.. The SageMaker training toolkit is required in custom containers to handle the automatic upload of model artifacts to S3. Without it, even if the script writes to /opt/ml/model and has correct permissions, SageMaker cannot perform the upload. Option B is correct because the container lacks the toolkit. Option A is incorrect because the entry point is unrelated to the upload failure. Option C is incorrect as the output path formatting would cause a different error. Option D is incorrect because permissions were already verified.

Answer

The training script's entry point is not correctly specified in the container.

Answer

The output path in the training job configuration is incorrectly formatted.

Answer

The SageMaker execution role does not have s3:PutObject permission on the output bucket.

Question 4

Which TWO of the following are valid Amazon SageMaker built-in algorithms for regression tasks? (Select TWO.)

Accepted Answer

XGBoost. XGBoost (B) is a valid SageMaker built-in algorithm that supports regression via the reg:squarederror (and related) objectives, making it appropriate for predicting continuous numeric targets. Linear Learner (E) is also a SageMaker built-in algorithm explicitly designed for both classification and regression, learning a linear function to predict continuous values. BlazingText (A) is a built-in algorithm for text classification and word embeddings, not regression. Image Classification (C) is a supervised vision algorithm for assigning discrete labels to images, so it is not a regression algorithm. Object Detection (D) is a vision algorithm that predicts bounding boxes and classes, which is a detection task rather than regression.

Answer

BlazingText

Answer

Image Classification

Answer

Object Detection

Question 5

A DevOps engineer created a SageMaker notebook instance using the Terraform configuration shown. The notebook instance is in a VPC with a public subnet. However, the notebook instance cannot access the internet. What is the most likely cause?

Accepted Answer

The subnet does not have a route to an internet gateway.. A SageMaker notebook instance in a VPC with a public subnet requires a route to an internet gateway (IGW) in the subnet's route table to access the internet. Without that route, traffic from the notebook cannot reach the internet, even if `direct_internet_access` is enabled. The Terraform configuration likely omitted the route to the IGW, causing the connectivity failure.

Answer

The role_arn is incorrect or missing permissions.

Answer

The instance type ml.t2.medium does not support internet access.

Answer

The direct_internet_access parameter is set to 'Enabled' but should be 'Disabled'.

Question 6

A data engineer is configuring an IAM policy to allow users to upload objects to an S3 bucket only if the objects are encrypted using SSE-S3. However, users are getting AccessDenied errors when uploading objects without specifying encryption. What is the most likely cause?

Accepted Answer

The condition requires encryption to be specified, but the upload does not specify it. The most likely cause of AccessDenied errors is that the IAM policy includes a condition that requires encryption to be specified (e.g., 's3:x-amz-server-side-encryption' equals 'AES256'), but the upload request does not include the encryption header. When users upload without specifying encryption, the condition evaluates to false, and the policy denies the action.

Answer

The condition should check for aws:SourceIp instead of encryption

Answer

The policy is attached to the wrong IAM user

Answer

The bucket policy denies all PutObject without encryption

Question 7

Which TWO actions are best practices for securing a SageMaker notebook instance? (Select TWO.)

Accepted Answer

Disable direct internet access for the notebook instance.. Disabling direct internet access for a SageMaker notebook instance (Option A) is a best practice because it prevents the instance from reaching the public internet, reducing the attack surface. This forces all outbound traffic through a VPC, allowing you to control egress via NAT gateways or VPC endpoints, and ensures data does not traverse the public internet. It is a fundamental security hardening step for sensitive workloads.

Answer

Enable root access for users to install packages.

Answer

Store data in the notebook's local storage for performance.

Answer

Use a shared IAM user for all data scientists.

Question 8

Which TWO of the following are appropriate techniques for detecting outliers in a univariate continuous feature?

Accepted Answer

Use Z-score and flag values with absolute Z-score > 3.. Option B is correct because the Z-score measures how many standard deviations a value lies from the mean, and in a univariate continuous feature a common rule flags observations with |Z| > 3 as outliers (roughly 0.3% of a normal distribution). Option E is correct because the IQR method is a standard univariate technique: it computes Q1 and Q3, defines the fences Q1 - 1.5*IQR and Q3 + 1.5*IQR, and flags values outside them as outliers. Option A is not appropriate because Random Forest is a supervised classifier requiring labeled outliers, which is not a univariate detection technique. Option C is wrong because one standard deviation is far too narrow (it would flag about 32% of normally distributed data), not a valid outlier threshold. Option D is not appropriate because DBSCAN is a multivariate density-based clustering algorithm, and its default parameters are not designed for univariate outlier detection.

Answer

Apply a Random Forest classifier to predict outliers.

Answer

Remove any value that is more than one standard deviation from the mean.

Answer

Use DBSCAN clustering with default parameters.

Question 9

A data scientist is using Amazon SageMaker to deploy a custom model container. The model is a large transformer that requires 16 GB of memory. The scientist wants to minimize inference latency. Which SageMaker hosting option should they choose?

Accepted Answer

Use a real-time endpoint with an instance that has sufficient memory.. A real-time endpoint is the only SageMaker hosting option designed for low-latency, synchronous inference where the client waits for the response. The scientist can select an instance type (e.g., ml.g4dn.2xlarge with 32 GB memory) that meets the 16 GB requirement, ensuring the model loads and serves predictions with minimal delay. Asynchronous inference, serverless inference, and batch transform all introduce additional queuing, cold-start, or job-scheduling overhead that increases latency.

Answer

Use an asynchronous inference endpoint.

Answer

Use SageMaker Serverless Inference.

Answer

Use a batch transform job.

Question 10

A data scientist is building a classification model and wants to evaluate its performance. Which TWO metrics are appropriate for a multi-class classification problem? (Choose 2)

Accepted Answer

Recall. Recall (B) is correct because it measures the proportion of actual instances of each class that the model correctly identified, and in multi-class settings it can be computed per class and then averaged (macro, micro, or weighted), making it a standard classification metric. Precision (C) is also correct because it measures the proportion of predicted positives for a class that are truly that class, and like recall it extends naturally to multi-class problems through per-class and averaged variants. MAE (A), R-squared (D), and RMSE (E) are regression metrics that quantify the magnitude of numeric prediction errors or explained variance, so they are not appropriate for evaluating a categorical multi-class classifier.

Answer

Mean Absolute Error (MAE)

Answer

R-squared

Answer

Root Mean Square Error (RMSE)

Question 11

A data engineer needs to transfer 50 TB of historical data from an on-premises Hadoop cluster to Amazon S3. The on-premises network has a 1 Gbps connection to AWS. The transfer must be completed within 10 days. What is the MOST efficient approach?

Accepted Answer

Use AWS Snowball Edge to physically ship the data.. Transferring 50 TB over a 1 Gbps connection would take approximately 5.6 days under ideal conditions (50 TB × 8 bits/byte / 1 Gbps / 86400 seconds/day ≈ 4.63 days), but real-world factors like network congestion, TCP overhead, and protocol inefficiencies typically reduce throughput to 50-70% of line rate, pushing the transfer beyond the 10-day window. AWS Snowball Edge provides a physical shipping alternative that bypasses network limitations entirely, making it the most efficient and reliable method for this volume and deadline.

Answer

Use Amazon S3 Transfer Acceleration to speed up the upload.

Answer

Use AWS DataSync over the existing network connection.

Answer

Set up a VPN connection and use multi-part upload directly to S3.

Question 12

A company uses Amazon SageMaker to train a model using a custom Docker container. The training job fails with an error: "Unable to write to /opt/ml/output/data". The data scientist checks the container and finds that the /opt/ml directory is not writable. What is the MOST likely cause?

Accepted Answer

The container runs as a non-root user that lacks write permissions to /opt/ml.. The error 'Unable to write to /opt/ml/output/data' indicates a permission issue. By default, SageMaker training containers run as a non-root user (uid 1000) for security reasons. If the Docker image is built with /opt/ml owned by root and without world-writable permissions, the non-root user cannot write to that directory, causing the failure.

Answer

The Docker image is built from a base image that does not have the required libraries.

Answer

The SageMaker training job is configured with insufficient memory.

Answer

The training script is not copying the model to /opt/ml/model.

Question 13

A data scientist is performing EDA on a time series dataset of daily sales. The data scientist observes a pattern that repeats every 7 days. Which characteristic of the time series is being observed?

Accepted Answer

Seasonality. Seasonality refers to periodic fluctuations in a time series that repeat at fixed intervals, such as daily, weekly, or yearly. In this case, the pattern repeats every 7 days, which is a classic example of weekly seasonality. This is distinct from other time series characteristics like trend (long-term increase/decrease) or autocorrelation (correlation with lagged values).

Answer

Stationarity

Answer

Autocorrelation

Answer

Trend

Question 14

A company is deploying a machine learning model using AWS Lambda for real-time inference. The model is a large ensemble model that takes approximately 500 MB of memory. The Lambda function is configured with 1024 MB of memory and a timeout of 15 seconds. The company observes that the function frequently times out during inference. The company wants to keep using Lambda for its serverless benefits. Which solution should the company implement to reduce inference time?

Accepted Answer

Increase the Lambda function memory to 3008 MB to provide more CPU resources.. Lambda has a maximum memory of 10,240 MB and a maximum timeout of 15 minutes. Increasing memory to 3008 MB gives more CPU power and reduces inference time. Option A is correct. Option B (SageMaker) moves away from serverless, which the company wants to keep. Option C (Step Functions) adds orchestration overhead and does not directly reduce inference time. Option D (ElastiCache) adds latency and cost and does not address the timeout issue.

Answer

Deploy the model on Amazon SageMaker hosting instead of Lambda.

Answer

Use AWS Step Functions to invoke the Lambda function asynchronously.

Answer

Use Amazon ElastiCache to cache model predictions and reduce computation.

Question 15

A machine learning team is using Amazon SageMaker to build a regression model. The target variable is heavily right-skewed with a long tail. Which data transformation should the team apply to the target variable before training?

Accepted Answer

Log transformation. A heavily right-skewed target variable with a long tail violates the normality assumption of many regression models and can cause the model to be dominated by extreme values. A log transformation compresses the tail and makes the distribution more symmetric, which stabilizes variance and improves linear model performance. This is the standard transformation for right-skewed targets in regression.

Answer

One-hot encoding

Answer

Min-max scaling

Answer

Standardization (z-score)