MLS-C01 Practice Test 42 — 15 Questions

Question 1

A data scientist is performing EDA on a dataset with 1 million rows and 50 features. The dataset includes a column 'user_id' with unique identifiers, a column 'event_date' with timestamps, and other columns. Which TWO actions should the data scientist take to understand data quality issues?

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

Accepted Answer

Analyze missing value patterns across columns. Option A is correct because analyzing missing value patterns across columns is a core EDA step for data quality: it reveals null counts, co-occurrence of missingness, and whether missingness is random or systematic, which directly informs imputation or exclusion decisions. Option B is correct because checking for duplicate rows based on 'user_id' and 'event_date' detects redundant or improperly joined records; since 'user_id' is a unique identifier and 'event_date' a timestamp, a composite key check exposes duplicate events that would distort counts and aggregations. Option C is not appropriate because dropping 'user_id' is a dimensionality-reduction or modeling choice, not a data quality diagnostic, and it discards the key needed for duplicate detection. Option D is not appropriate because PCA is a dimensionality-reduction and visualization technique that does not surface data quality issues like missingness or duplicates. Option E is not appropriate because training a random forest for feature importance is a modeling task, not an EDA step for assessing data quality.

Answer

Drop the 'user_id' column to reduce dimensionality

Answer

Use PCA to reduce dimensions and visualize

Answer

Train a random forest model to identify feature importance

Question 2

A machine learning team is using Amazon SageMaker to train a model. The training data is stored in an S3 bucket. The team wants to ensure that the training job can access the data securely without using long-lived AWS credentials. Which approach should the team use?

Accepted Answer

Specify an IAM role in the SageMaker training job configuration. SageMaker training jobs can assume an IAM role specified in the job configuration to obtain temporary security credentials via AWS Security Token Service (STS). This allows the training job to access the S3 bucket securely without embedding long-lived AWS access keys in code or configuration files.

Answer

Store AWS access keys in the training script

Answer

Use an S3 bucket policy that allows public access

Answer

Create a new IAM user for each training job

Question 3

Which TWO of the following are valid techniques to handle missing data in a dataset?

Accepted Answer

Mean imputation. Mean imputation (C) is a valid missing-data handling technique because it replaces each missing value in a numeric column with the mean of the observed values in that column, preserving the dataset size and avoiding loss of records. Deleting rows with missing values (E), often called listwise deletion, is also a standard valid technique because it removes incomplete records so that analyses run only on fully observed cases. Normalizing the data (A) is a scaling technique that rescales numeric features (for example to 0–1 or z-scores) and does not address missing values. Adding a constant value of 0 (B) is not a recognized missing-data imputation method; it would distort distributions and is not equivalent to principled zero imputation. SMOTE (D) is an oversampling technique for class imbalance that generates synthetic minority-class samples, not a method for handling missing data.

Answer

Normalizing the data

Answer

Adding a constant value of 0

Answer

Synthetic Minority Over-sampling (SMOTE)

Question 4

A data scientist is deploying a model to a SageMaker endpoint and needs to optimize for cost while maintaining low latency. Which TWO actions should the data scientist take?

Accepted Answer

Use SageMaker Serverless Inference. SageMaker Serverless Inference (D) is correct because it automatically provisions and scales compute capacity based on traffic, charging only for the compute time used during request processing, which directly reduces cost for intermittent or unpredictable workloads while still providing low-latency real-time responses. Enabling Auto Scaling on the endpoint (E) is correct because it dynamically adjusts the number of instances to match demand, avoiding over-provisioning during low-traffic periods and maintaining low latency during spikes, thereby optimizing cost without sacrificing responsiveness. A larger instance type (A) increases hourly cost and does not inherently improve cost efficiency, so it does not belong. Deploying to a single instance (B) may reduce cost but eliminates redundancy and cannot scale to maintain low latency under load, so it is not a recommended optimization. Switching to batch transform (C) is for offline, non-real-time inference and cannot serve a live endpoint with low latency, so it is inappropriate here.

Answer

Use a larger instance type

Answer

Deploy to a single instance

Answer

Switch to batch transform

Question 5

A data scientist is reviewing the training logs from a SageMaker training job. The logs show training and validation loss per epoch. Based on the exhibited logs, which statement is correct?

Accepted Answer

The model is overfitting because training loss decreases while validation loss does not. The training logs show a classic overfitting pattern: training loss consistently decreases across epochs, indicating the model is memorizing the training data, while validation loss does not decrease (or may even increase), indicating poor generalization to unseen data. In SageMaker, monitoring both losses during training is critical to detect overfitting early, often prompting regularization or early stopping.

Answer

The model is not learning because the loss is not decreasing

Answer

The model is underfitting because both losses are high

Answer

The model is performing well because validation loss is stable

Question 6

A company wants to deploy a machine learning model on Amazon SageMaker and needs to monitor the model's performance in production. Which TWO AWS services can be used to set up monitoring?

Accepted Answer

Amazon CloudWatch. Amazon CloudWatch (A) is correct because SageMaker automatically publishes model and endpoint metrics such as Invocations, ModelLatency, and 4XX/5XX errors to CloudWatch, and you can create alarms and dashboards to detect performance degradation in production. Amazon SageMaker Model Monitor (D) is correct because it is the purpose-built service that continuously monitors deployed endpoints for data drift, model quality drift, bias drift, and feature attribution drift, emitting violations to CloudWatch and Amazon S3. AWS X-Ray (B) is not correct here because it traces requests through distributed applications for latency and error debugging, not model performance or drift monitoring. Amazon Inspector (C) is not correct because it is a vulnerability management service for EC2, container images, and Lambda, not ML model monitoring. AWS Config (E) is not correct because it records and evaluates resource configuration changes for compliance, not model inference performance.

Answer

AWS X-Ray

Answer

Amazon Inspector

Answer

AWS Config

Question 7

A data engineer runs the CLI command to download an object from S3. The bucket owner is 123456789012, and the engineer's IAM user has s3:GetObject permission on the bucket. The object was uploaded by a different AWS account. What is the MOST likely reason for the AccessDenied error?

Accepted Answer

The object is owned by a different AWS account, and the bucket owner has not been granted access. When an object is uploaded to S3 by a different AWS account, the object is owned by the uploading account, not the bucket owner. By default, the bucket owner does not have access to objects uploaded by other accounts, even if the bucket owner has a policy granting s3:GetObject to their IAM users. The engineer's IAM user has permission on the bucket, but the object itself is not owned by the bucket owner, so the bucket owner cannot delegate access to it unless the object owner explicitly grants read access via an object ACL or a bucket policy that the object owner accepts.

Answer

The --expected-bucket-owner parameter is incorrect

Answer

The bucket policy denies access to the engineer's IAM user

Answer

The IAM policy does not allow s3:GetObject for that specific key

Question 8

A company is using SageMaker Autopilot to automatically build a binary classification model. After the AutoML job completes, the data scientist wants to understand which features are most important for the best candidate model. How can the scientist get feature importance?

Accepted Answer

Open the SageMaker Autopilot job details and view the 'Explainability' tab. SageMaker Autopilot automatically generates a 'Explainability' tab within the job details for the best candidate model. This tab uses SHAP (SHapley Additive exPlanations) values to provide feature importance, showing which features most influence the model's predictions. The data scientist can directly access this information without any additional configuration or re-running the model.

Answer

Re-run the best model using SageMaker built-in XGBoost with the 'feature_importance' hyperparameter

Answer

Check the CloudWatch Logs for the training job

Answer

Use SageMaker Ground Truth to label a new dataset

Question 9

A machine learning team is analyzing a dataset with numerical features. They compute the pairwise correlation matrix and find that two features, 'X1' and 'X2', have a correlation coefficient of 0.98. The team plans to train a linear regression model. Which of the following actions should the team take to avoid multicollinearity issues?

Accepted Answer

Remove one of the two highly correlated features.. Removing one of the highly correlated features reduces multicollinearity. Option A is wrong because PCA creates new uncorrelated features but is not necessary for just two correlated features. Option B is wrong because adding an interaction term between X1 and X2 would actually increase multicollinearity. Option C is wrong because standard scaling does not address correlation between features.

Answer

Perform PCA on the dataset to reduce dimensionality.

Answer

Add an interaction term between X1 and X2 to the model.

Answer

Standardize both features using Z-score normalization.

Question 10

An S3 event notification is configured to trigger a Lambda function when new objects are created. The Lambda function processes the event JSON shown. Which field should the function use to read the new object from S3?

Accepted Answer

s3.bucket.arn and s3.object.key. The Lambda function needs the bucket name and object key to retrieve the new object from S3. The `s3.bucket.arn` provides the bucket identifier, and `s3.object.key` provides the object path. Together, they allow the function to call `GetObject` on the S3 API.

Answer

s3.s3SchemaVersion

Answer

awsRegion

Answer

eventName

Question 11

A data scientist is performing EDA on a dataset with 1,000 features. The goal is to select the most important features for a regression model. Which technique can be used to rank feature importance quickly?

Accepted Answer

Calculate the correlation coefficient of each feature with the target. Correlation analysis with the target variable is a quick way to rank features. Option B (t-SNE) is used for visualization, not feature ranking. Option C (k-means clustering) is an unsupervised clustering method and does not provide feature importance. Option D (PCA) component loadings show variance contribution but are not a direct ranking of feature importance to the target.

Answer

Use t-SNE to visualize feature relationships

Answer

Run k-means clustering and use cluster centroids

Answer

Apply Principal Component Analysis (PCA) and examine component loadings

Question 12

A machine learning team is using SageMaker to train a model. They want to ensure that the training data is encrypted at rest in the S3 bucket and that the data is also encrypted during transit. Which configuration should they use?

Accepted Answer

Use SSE-S3 encryption on the S3 bucket and enforce HTTPS. SSE-S3 provides server-side encryption at rest for objects in S3, and enforcing HTTPS ensures data is encrypted in transit using TLS. This combination meets the requirements for both encryption at rest and in transit without additional client-side complexity.

Answer

Use client-side encryption and transfer data via HTTP

Answer

Use SSE-KMS encryption on the S3 bucket and disable HTTP

Answer

Use SSE-C encryption on the S3 bucket and HTTPS

Answer

Use no encryption on S3 but use HTTPS

Question 13

A company is using Amazon SageMaker to train a linear learner model for predicting customer lifetime value. The target variable is right-skewed with a long tail. The data scientist applies a log transformation to the target variable and trains the model. The model achieves a low root mean squared error (RMSE) on the log scale. However, when the predictions are exponentiated back to the original scale, the RMSE is much higher. Which step should the data scientist take to improve the model's performance on the original scale?

Accepted Answer

Use a loss function that models the original distribution, such as Poisson or Tweedie. Log-transforming a right-skewed target and then exponentiating predictions back to the original scale introduces bias because the model minimizes squared error on the log scale, which corresponds to relative error, not absolute error, on the original scale. Using a loss/distribution that natively models the original skewed target — such as Poisson or Tweedie in SageMaker Linear Learner — aligns the objective with the original-scale distribution and typically yields better original-scale RMSE. This addresses the mismatch directly rather than tuning unrelated hyperparameters.

Answer

Increase the regularization strength

Answer

Remove outliers from the training data

Answer

Use a deep learning model instead of linear learner

Question 14

A data scientist is working on a project to predict customer churn for a telecom company. The dataset includes 50,000 records with 20 features, including customer demographics, account information, and service usage. The data scientist uses Amazon SageMaker Studio and loads the data into a pandas DataFrame. During EDA, the data scientist notices that the target variable 'churn' has only 10% positive cases. Additionally, several features have missing values: 'income' has 5% missing, 'age' has 2% missing, and 'total_charges' has 1% missing. The data scientist also observes that 'income' is highly skewed with a long right tail, and 'age' is moderately skewed. The data scientist wants to handle missing values and prepare the data for modeling. Which course of action is most appropriate?

Accepted Answer

Impute 'income' with median, 'age' with median, 'total_charges' with median, and use SMOTE to handle class imbalance after splitting the data.. Median imputation is robust to skewness (particularly for income and age), and SMOTE is applied after splitting to avoid data leakage and handle class imbalance. Option B is wrong because removing rows with missing values would discard roughly 8% of the data (5%+2%+1% with potential overlap), which is a significant loss of information; additionally, random oversampling may lead to overfitting. Option C is wrong because mode imputation is appropriate for categorical data, not for continuous features like income, age, and total_charges. Option D is wrong because mean imputation is sensitive to outliers and skewness, especially for income with a long right tail; also, stratified sampling only ensures proportional representation in train/test splits, it does not generate synthetic samples to address imbalance.

Answer

Remove all rows with any missing values, and use random oversampling to handle class imbalance.

Answer

Impute 'income' with mode, 'age' with mode, 'total_charges' with mode, and use SMOTE after splitting.

Answer

Impute all missing values with the mean of each column, and use stratified sampling to handle class imbalance.

Question 15

A data scientist is using Amazon SageMaker Data Wrangler for exploratory data analysis. The dataset contains a column with missing values that are encoded as 'NA' strings. The data scientist wants to treat these as missing values during the import. Which step should the data scientist take?

Accepted Answer

Configure a custom missing value symbol 'NA' in the import settings of Data Wrangler.. In SageMaker Data Wrangler, missing value symbols like 'NA' can be configured during import so they are treated as nulls from the start. This ensures subsequent transforms and analyses correctly recognize them as missing rather than as valid string values.

Answer

Use the 'Impute' transform to fill 'NA' with the mean of the column.

Answer

Use the 'Replace missing' transform to replace 'NA' with null after import.

Answer

Use the 'Drop missing' transform to remove rows containing 'NA'.