MLS-C01 Practice Test 4 — 25 Questions

Question 1

A company uses SageMaker to deploy a model for predicting customer churn. The model was trained on historical data and achieves 85% accuracy on the test set. After deployment, the model's predictions are significantly worse on new data due to changes in customer behavior. What is the MOST likely cause?

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

Accepted Answer

Concept drift in the underlying data distribution. The model's performance degradation on new data, despite high accuracy on the test set, is a classic symptom of concept drift. Concept drift occurs when the statistical properties of the target variable (customer churn) change over time due to shifts in customer behavior, making the trained model's decision boundary obsolete. SageMaker deployed the model as a persistent endpoint, but the underlying data distribution has evolved, so the model no longer generalizes to the current environment.

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Data leakage during training

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The training dataset was too small

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The model is overfitting to the training data

Question 2

The Glue job my-glue-job fails after a few successful runs. The error log shows 'Job run exceeds max concurrent runs limit'. The CloudFormation template is shown in the exhibit. What change should be made to allow multiple runs to execute concurrently?

Accepted Answer

Set the MaxConcurrentRuns property to 3. The error 'Job run exceeds max concurrent runs limit' indicates that the AWS Glue job is configured to allow fewer concurrent runs than are being attempted. The MaxConcurrentRuns property in the job definition controls how many runs of the same job can execute simultaneously. Setting it to 3 would allow up to three concurrent runs, resolving the error.

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Change the IAM role to one with more permissions

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Increase the MaxRetries property to 3

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Remove the --job-bookmark-option argument

Question 3

A company wants to use SageMaker to host multiple models behind a single endpoint to reduce costs. Which SageMaker feature should they use?

Accepted Answer

SageMaker Multi-Model Endpoints. SageMaker Multi-Model Endpoints allow you to deploy multiple models behind a single endpoint, each loaded dynamically from Amazon S3 based on the inference request. This reduces hosting costs by sharing a single instance across many models, as only the models that are actively invoked consume memory. The correct answer is E because this feature is specifically designed for cost-efficient multi-model hosting.

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SageMaker Elastic Inference

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SageMaker inference pipeline

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SageMaker batch transform

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SageMaker multi-container endpoints

Question 4

Which THREE of the following are valid approaches for deploying a machine learning model to an Amazon SageMaker endpoint for real-time inference?

Accepted Answer

Use a SageMaker Inference Pipeline with multiple containers. Option A is correct because a SageMaker Inference Pipeline lets you chain multiple containers (for example, a preprocessing container plus a model container) behind a single real-time endpoint, which is a supported deployment approach for real-time inference. Option B is correct because SageMaker provides pre-built containers for built-in algorithms, and you can deploy those directly to a real-time endpoint without building custom images. Option E is correct because SageMaker supports bringing your own Docker container that conforms to the SageMaker inference container contract, allowing custom models to be hosted on a real-time endpoint. Option C is not correct because Amazon EMR is a big data processing service for running frameworks like Spark and Hadoop, not a service for hosting SageMaker real-time inference endpoints. Option D is not correct because AWS Lambda runs function code in response to events and is not the mechanism for deploying a model to a SageMaker endpoint, even though Lambda can invoke a SageMaker endpoint.

Answer

Use Amazon EMR to host the model

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Deploy the model as an AWS Lambda function

Question 5

A data scientist builds a Random Forest model using SageMaker. The model performs well on training data but poorly on test data. Which step is most likely to reduce overfitting?

Accepted Answer

Reduce the maximum depth of each tree. Reducing the maximum depth of each tree limits the complexity of individual decision trees, preventing them from memorizing noise and specific patterns in the training data. This directly addresses overfitting by enforcing simpler, more generalized splits, which improves performance on unseen test data.

Answer

Increase the number of trees

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Switch to a linear model

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Increase the number of features considered at each split

Question 6

A data scientist is performing exploratory data analysis on a dataset with mixed data types (numerical, categorical, text). The goal is to identify clusters of similar records. Which technique is most appropriate?

Accepted Answer

K-prototypes clustering. K-prototypes clustering is specifically designed to handle mixed data types (numerical and categorical) by combining K-means for numerical attributes and K-modes for categorical attributes. It uses a distance measure that integrates Euclidean distance for numerical features and Hamming distance for categorical features, making it suitable for datasets with mixed types. The other algorithms are not inherently designed for mixed data types without preprocessing.

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DBSCAN

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Hierarchical clustering

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K-means clustering

Question 7

A data engineer needs to transform large CSV files stored in S3 into Parquet format and load them into a data warehouse for analysis. The transformation must be cost-effective and serverless. Which AWS service should be used?

Accepted Answer

AWS Glue. AWS Glue is the correct choice because it provides a fully managed, serverless ETL service that can automatically convert CSV files from S3 into Parquet format using its built-in Spark engine. It is cost-effective as you only pay for the resources consumed during the job execution, and it integrates directly with data warehouses like Amazon Redshift for loading transformed data.

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Amazon Athena

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Amazon EMR with Spark

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AWS Data Pipeline

Question 8

A data scientist needs to understand the distribution of a numeric feature in a dataset stored in Amazon S3. Which AWS service can be used to run a quick exploratory query without setting up a server?

Accepted Answer

Amazon Athena. Amazon Athena is a serverless, interactive query service that allows you to analyze data directly in Amazon S3 using standard SQL. It requires no infrastructure setup or management, making it ideal for quick exploratory queries on data stored in S3. You simply point Athena to your S3 data, define a table in the AWS Glue Data Catalog, and run SQL queries to understand the distribution of a numeric feature. This aligns perfectly with the requirement to avoid server setup.

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Amazon Redshift

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Amazon EMR

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AWS Glue

Question 9

A team is performing exploratory data analysis on a dataset containing 10 million records stored in Amazon S3. They want to sample the data efficiently to build a representative subset for initial modeling. Which sampling method should they use to minimize bias and ensure the sample reflects the population distribution?

Accepted Answer

Stratified random sampling. Stratified random sampling divides the population into homogeneous subgroups (strata) based on a relevant characteristic and then samples proportionally from each stratum. This ensures the sample mirrors the population's distribution across key dimensions, minimizing bias when the dataset has heterogeneous subgroups. For 10 million records with potentially skewed classes or categories, stratified sampling produces a more representative subset than simple random sampling.

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Simple random sampling

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Systematic sampling

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Reservoir sampling

Question 10

A data pipeline uses AWS Glue to transform data from Amazon RDS to Amazon S3. The team wants to ensure that only new or updated records are processed in each run, minimizing cost and time. Which AWS Glue feature should be used?

Accepted Answer

Enable Glue Job Bookmarks.. Glue Job Bookmarks track processed data and persist state between job runs, enabling incremental processing of new or updated records from a source like Amazon RDS. This minimizes cost and time by avoiding full table scans and reprocessing unchanged data.

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Use Glue triggers to run the job on a schedule.

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Use Glue partition pruning to filter data.

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Use Glue crawlers to detect new data.

Question 11

A data scientist is training a linear regression model on a dataset with 50 features. After training, they notice that the model performs well on training data but poorly on test data. They suspect overfitting. Which action should they take to reduce overfitting?

Accepted Answer

Add L2 regularization (Ridge regression). L2 regularization (Ridge regression) adds a penalty term proportional to the square of the magnitude of the coefficients to the loss function. This discourages the model from fitting the noise in the training data by shrinking the weights, which reduces variance and mitigates overfitting, improving generalization to the test data.

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Use a larger learning rate

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Add more features to the model

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Increase the number of training epochs

Question 12

Which THREE factors should be considered when choosing between a parametric and a non-parametric machine learning model?

Accepted Answer

Non-parametric models are generally more flexible. Option A is correct because non-parametric models, such as k-NN, decision trees, or kernel SVMs, do not assume a fixed functional form and can adapt their complexity to the data, making them generally more flexible. Option C is correct because parametric models like linear or logistic regression estimate a fixed, small set of parameters, so training is typically faster than for non-parametric models that must retain or process much of the training data. Option E is correct because parametric models have strong structural assumptions and a fixed number of parameters, so they usually need less training data to estimate those parameters reliably. Option B is incorrect because non-parametric models typically have lower bias and higher variance, not the reverse. Option D is incorrect because non-parametric models are generally more prone to overfitting, not less, due to their higher flexibility.

Answer

Parametric models have lower bias than non-parametric models

Answer

Non-parametric models are less prone to overfitting

Question 13

A data scientist is training a deep learning model on Amazon SageMaker and wants to reduce the training time. Which TWO actions would help achieve this?

Accepted Answer

Use distributed training across multiple instances.. Option B is correct because distributed training across multiple instances (e.g., using SageMaker's distributed data parallel or model parallel libraries) splits the compute workload across nodes, directly reducing wall-clock training time for large deep learning models. Option D is correct because GPU-based instance types (e.g., ml.p3, ml.p4d, ml.g4dn) provide massively parallel floating-point throughput and high-bandwidth memory that accelerate the matrix operations dominating deep learning training compared to CPU instances. Option A is not correct because data augmentation increases the volume and variety of training data, which typically adds preprocessing and training overhead rather than reducing training time. Option C is not correct because SageMaker Automatic Model Tuning (hyperparameter tuning) runs many training jobs to find optimal hyperparameters, which increases overall time and cost rather than reducing a single training run's duration. Option E is not correct because SageMaker Managed Spot Training reduces cost by using spare EC2 capacity, not training time, and can even introduce interruptions and checkpoint-restart overhead.

Answer

Enable data augmentation.

Answer

Use SageMaker Automatic Model Tuning.

Answer

Use SageMaker Managed Spot Training.

Question 14

During EDA, a data scientist finds that two features have a Pearson correlation coefficient of 0.95. What is the primary concern when using these features together in a linear regression model?

Accepted Answer

Multicollinearity will make coefficient estimates unstable. A Pearson correlation coefficient of 0.95 indicates strong multicollinearity between the two features. Multicollinearity inflates the variance of coefficient estimates, making them unstable and difficult to interpret. Option A is wrong because redundant information leads to multicollinearity, not underfitting; underfitting occurs when the model is too simple. Option B is wrong because heteroscedasticity refers to non-constant variance of errors, not correlation between features. Option C is wrong because overfitting is more associated with model complexity and variance, not directly with redundant features; in fact, redundant features can cause numerical instability but not necessarily overfitting.

Answer

The model will underfit because of redundant information

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Heteroscedasticity will be introduced

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The model will overfit due to redundant features

Question 15

A data scientist is training a neural network on a dataset with 1 million images. The training loss decreases steadily but the validation loss starts to increase after 10 epochs. Which action should the scientist take to improve generalization?

Accepted Answer

Implement early stopping. Increasing validation loss while training loss decreases indicates overfitting. Early stopping (Option A) halts training when validation loss stops improving, directly preventing overfitting. Option B (adding more layers) increases model capacity and typically worsens overfitting. Option C (reducing learning rate) might slow training but does not directly stop overfitting. Option D (increasing epochs) would continue training and likely worsen overfitting.

Answer

Add more layers to the network

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Reduce the learning rate

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Increase the number of epochs

Question 16

A data scientist is using Amazon Athena to query a CSV file stored in S3. The query fails with the error: 'HIVE_CANNOT_OPEN_SPLIT: Number of fields in line 1502 does not match number of fields in the first line.' What is the most likely cause?

Accepted Answer

The CSV file has inconsistent number of columns in some rows.. Athena uses a schema-on-read model where the table definition specifies a fixed number of columns. When a CSV row contains more or fewer comma-separated fields than the header/first line, the Hive SerDe cannot map the fields to columns and throws HIVE_CANNOT_OPEN_SPLIT with the line number. The fix is to clean the CSV (ensure every row has the same column count) or use OpenCSVSerDe with tolerance settings.

Answer

The CSV file uses a different delimiter than comma.

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The CSV file is missing a header row.

Answer

The CSV file is too large for Athena to process.

Question 17

A company is building a data lake on Amazon S3. Data arrives from multiple sources in different formats (CSV, JSON, Parquet). The engineering team wants to query this data using Amazon Athena with minimal transformation. Which approach minimizes query cost and improves performance?

Accepted Answer

Use AWS Glue to convert all data to Parquet format, partition by date, and store in a separate S3 bucket. Converting data to Parquet format (a columnar storage format) significantly reduces the amount of data scanned by Athena, which directly lowers query cost (Athena charges per TB scanned). Partitioning by date further limits scanned data by pruning irrelevant partitions. AWS Glue provides a serverless ETL service to perform this conversion efficiently, and storing the output in a separate S3 bucket avoids polluting the raw data lake.

Answer

Use Amazon Redshift Spectrum to query the data directly without transformation

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Use Amazon EMR to convert data to CSV format and repartition

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Store data as-is in S3 and create external tables in Athena for each format

Question 18

A data scientist is using Amazon SageMaker to train a model. The training dataset contains missing values in several features. The data scientist wants to impute missing values using the median of each feature. Which approach is most appropriate?

Accepted Answer

Compute the median of each feature on the training set only, then impute both training and test sets using that median. Computing the median on the training set only avoids data leakage, and applying that median to both training and test sets ensures consistent imputation without using test set information. Option A is incorrect because dropping rows with missing values discards potentially useful data and is not imputation. Option B is incorrect because computing the median on the entire dataset before splitting introduces data leakage, as the test set influences the imputation values. Option C is incorrect because imputing with zero is arbitrary and does not use the median; also, doing it before splitting would use the entire dataset, causing leakage.

Answer

Drop all rows that contain missing values

Answer

Compute the median on the entire dataset, then split into training and test sets

Answer

Impute missing values with zero for all features before splitting

Question 19

A data engineer needs to transform raw clickstream data (JSON files) stored in S3 into a partitioned Parquet dataset for querying with Athena. The transformation includes cleaning, deduplication, and enrichment. The pipeline should run daily. Which solution is MOST cost-effective and requires the least operational overhead?

Accepted Answer

Use an AWS Glue ETL job with a schedule trigger to perform the transformation and write to S3.. AWS Glue ETL jobs are serverless, require no cluster management, and can be easily scheduled for daily runs. Glue also integrates with the Data Catalog for partitioning. Option A (Amazon EMR) is not the most cost-effective or least operational overhead because it requires managing a cluster, even if it can be terminated after completion. Option C (AWS Lambda) is not suitable for large-scale clickstream data due to execution time limits and lack of built-in support for complex transformations like deduplication. Option D (Amazon Athena CTAS) is not appropriate because Athena is primarily a query engine, not a transformation tool; CTAS queries are good for converting data formats but lack the flexibility for cleaning and enrichment.

Answer

Launch an Amazon EMR cluster with Spark, transform the data, and terminate the cluster after completion.

Answer

Use AWS Lambda functions triggered by S3 events to transform each file incrementally.

Answer

Use Amazon Athena to run CTAS queries to convert and partition the data daily.

Question 20

A data analyst is using Amazon SageMaker Studio to perform exploratory data analysis on a dataset stored in S3. The analyst wants to generate summary statistics and visualizations quickly. Which built-in feature of SageMaker Studio should the analyst use?

Accepted Answer

SageMaker Data Wrangler. SageMaker Data Wrangler is the built-in Studio feature for exploratory data analysis, offering 300+ prebuilt transforms, quick summary statistics, histograms, scatter plots, and a visual data flow — all without writing code. It imports directly from S3, Athena, Redshift, and Snowflake, making it the correct tool for fast EDA on an S3 dataset. The other services serve labeling, AutoML, and bias detection, not exploratory visualization.

Answer

SageMaker Ground Truth

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SageMaker Autopilot

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SageMaker Clarify