MLS-C01 Practice Test 41 — 15 Questions

Question 1

A data scientist is performing EDA on a dataset of customer churn. The dataset includes a categorical feature 'Region' with 100 unique values. What is the best way to encode this feature for a tree-based model?

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

Accepted Answer

Use the feature as a categorical variable directly in the tree-based model. Many tree-based model implementations (e.g., LightGBM, CatBoost) support categorical features natively, handling high cardinality without encoding. Option A is wrong because frequency encoding can introduce target leakage if applied without proper cross-validation. Option C is wrong because label encoding imposes an ordinal relationship that the tree might misinterpret. Option D is wrong because one-hot encoding with 100 categories creates many sparse columns, leading to inefficiency and potential overfitting.

Answer

Replace each category with its frequency in the dataset

Answer

Label encode the feature (assign integers 0-99)

Answer

One-hot encode the feature

Question 2

A company uses SageMaker to train a time-series forecasting model using Amazon Forecast. The dataset contains historical sales data for 10,000 products over 2 years. Which data format is required for the target time series?

Accepted Answer

A CSV file with columns: timestamp, target_value, item_id. Amazon Forecast requires the target time series data to be in a CSV format with specific columns: timestamp, target_value, and item_id. This structured format allows the service to correctly identify the time series for each product and the target metric to forecast. The CSV format is the standard input for Forecast's built-in algorithms and ensures compatibility with the dataset import process.

Answer

A single JSON file with nested arrays

Answer

A text file with one value per line

Answer

A Parquet file partitioned by date

Question 3

A machine learning engineer is analyzing a text classification dataset with 50,000 documents. Which EDA step is most important to understand the vocabulary size and frequency distribution?

Accepted Answer

Plot frequency of each word in a bar chart. Plotting the frequency of each word in a bar chart directly shows the vocabulary size and the frequency distribution (e.g., Zipfian distribution). This EDA step helps decide vocabulary cutoff by identifying very rare words that can be removed. Option A is wrong because TF-IDF is a feature transformation, not an exploratory step. Option C is wrong because bigram collocations are for detecting phrases, not for basic word frequency. Option D is wrong because document length distribution pertains to the number of words per document, not vocabulary size or word frequency.

Answer

Compute TF-IDF matrix

Answer

Generate bigram collocations

Answer

Plot histogram of document lengths

Question 4

During EDA, a data scientist notices that a numeric feature 'age' has outliers beyond 3 standard deviations. What is the most appropriate first step?

Accepted Answer

Investigate the source of the outliers. The most appropriate first step when encountering outliers is to investigate their source (Option D). Outliers could indicate data entry errors, measurement issues, or genuine rare events. Blindly removing them (Option C) or transforming them (Option B) without understanding the cause may distort analysis. Using the feature as-is (Option A) can bias models if outliers are erroneous. Investigation should precede any action.

Answer

Use the feature as-is in the model

Answer

Apply a log transformation to the feature

Answer

Remove all rows with outlier values

Question 5

A machine learning engineer is building a pipeline using Amazon SageMaker Pipelines. The pipeline has multiple steps including data preprocessing, training, and evaluation. Which statement about SageMaker Pipelines is correct?

Accepted Answer

Pipelines support caching of step outputs.. SageMaker Pipelines supports output caching, which allows step outputs to be reused when the step configuration and inputs remain unchanged. This caching mechanism reduces execution time and cost by skipping redundant computations for steps like data preprocessing or training when their parameters have not changed.

Answer

Steps in a pipeline must run sequentially.

Answer

Pipelines can only use built-in algorithms.

Answer

Pipelines cannot have conditional branches.

Question 6

A company wants to perform automated hyperparameter tuning for a model. Which Amazon SageMaker feature should be used?

Accepted Answer

Amazon SageMaker automatic model tuning. Amazon SageMaker automatic model tuning (also known as hyperparameter tuning) is the correct feature because it automates the process of searching for the optimal combination of hyperparameters for a machine learning model. It uses algorithms like Bayesian optimization, random search, or Hyperband to efficiently explore the hyperparameter space and find the best-performing configuration based on a specified objective metric.

Answer

Amazon SageMaker Clarify

Answer

Amazon SageMaker Ground Truth

Answer

Amazon SageMaker Debugger

Question 7

During EDA, a data scientist plots the distribution of a feature and sees a bimodal pattern. What does this likely indicate?

Accepted Answer

The data may contain two distinct groups.. A bimodal distribution has two distinct peaks, which typically indicates that the data contains two different subpopulations or clusters. This is a common finding in exploratory data analysis (EDA) when the feature is influenced by a categorical variable with two categories. For example, in a dataset of customer purchases, transaction amounts may be bimodal if there are two types of customers (e.g., individuals and businesses). Therefore, option A is correct. Option B is incorrect because missing values usually appear as a separate bar or a spike at a specific value, not as a second peak. Option C is incorrect because outliers typically appear as extreme values far from the main distribution, not as a second mode. Option D is incorrect because standardization (scaling to zero mean and unit variance) does not change the shape of the distribution; it only changes the scale.

Answer

The feature has missing values.

Answer

The feature contains outliers.

Answer

The feature needs to be standardized.

Question 8

A data analyst needs to visualize the distribution of a numerical feature in a dataset. Which AWS service can be used to create a histogram directly from data stored in S3 without writing code?

Accepted Answer

Amazon QuickSight. Mazon QuickSight (Option C) because it is a business intelligence (BI) service that can connect directly to data stored in Amazon S3 and create visualizations such as histograms without requiring any coding. Amazon Athena (Option A) is an interactive query service that returns raw query results, not visualizations. Amazon SageMaker Studio (Option B) is a machine learning IDE that typically requires writing code or using notebooks to generate plots. AWS Glue (Option D) is a serverless data integration service for ETL operations, not for visualization.

Answer

Amazon Athena

Answer

Amazon SageMaker Studio

Answer

AWS Glue

Question 9

The exhibit shows an IAM policy for a SageMaker notebook. A data scientist wants to use the notebook to run an Athena query and then load the results into a pandas DataFrame. Which action is NOT possible with this policy?

Accepted Answer

Read the Athena query results from the output S3 location. The policy grants s3:GetObject on the 'my-training-data' bucket, so reading a specific object from that bucket (Option C) is possible. It also grants s3:ListBucket on that bucket, so listing objects (Option D) is possible. The policy includes Athena permissions, so starting a query execution (Option B) is allowed. However, to read Athena query results, the user needs s3:GetObject permission on the S3 location where Athena writes the results, which is typically a different bucket (the query output location). The policy does not grant s3:GetObject on that output bucket, so reading the results (Option A) is not possible.

Answer

Start an Athena query execution

Answer

Read a specific object from the my-training-data bucket

Answer

List objects in the my-training-data bucket

Question 10

A data scientist is using SageMaker to train a model with a custom algorithm. The training script uses TensorFlow and runs on GPU instances. The training job fails with 'CUDA_ERROR_OUT_OF_MEMORY'. What is the most likely cause?

Accepted Answer

The batch size is too large for the GPU memory. The error 'CUDA_ERROR_OUT_OF_MEMORY' indicates that the GPU memory has been exhausted during training. In TensorFlow, the batch size directly determines how many samples are processed simultaneously on the GPU; a batch size that is too large will exceed the available GPU memory, causing this specific CUDA error. Reducing the batch size is the standard fix for this issue.

Answer

The S3 bucket is in a different region

Answer

The GPU driver is outdated

Answer

The training script has a memory leak on CPU

Answer

The instance type does not have enough CPU cores

Question 11

A company has deployed a model on SageMaker for real-time inference. The endpoint is experiencing high latency during traffic spikes. Which action should the company take to reduce latency?

Accepted Answer

Enable SageMaker endpoint auto-scaling. Enabling SageMaker endpoint auto-scaling allows the endpoint to dynamically adjust the number of instances based on incoming traffic, which directly reduces latency during spikes by ensuring sufficient compute capacity is available. Auto-scaling uses CloudWatch metrics (e.g., InvocationsPerInstance or latency) to trigger scale-out events, preventing queue buildup and response time degradation.

Answer

Use a larger instance type for the endpoint

Answer

Attach SageMaker Elastic Inference to the endpoint

Answer

Use SageMaker Neo to compile the model

Answer

Switch to SageMaker batch transform

Question 12

A company is using Amazon SageMaker to deploy a model for real-time inference. The endpoint receives variable traffic and the company wants to optimize cost while maintaining responsiveness. Which scaling policy should be used?

Accepted Answer

Target tracking scaling based on invocation count. Target tracking scaling based on invocation count is the correct choice because it automatically adjusts the number of instances in real-time based on a predefined metric (e.g., InvocationsPerInstance), maintaining responsiveness during variable traffic while optimizing cost by scaling down during low demand. This policy uses a target value (e.g., 1000 invocations per instance) and SageMaker Application Auto Scaling continuously monitors CloudWatch metrics to add or remove instances as needed, eliminating manual intervention.

Answer

Simple scaling with a cooldown period

Answer

Scheduled scaling

Answer

Manual scaling

Question 13

A data analyst is using Amazon QuickSight to explore a dataset with 10 million rows. The analyst wants to create a histogram of a numerical column. However, the query is taking too long. Which action should the analyst take to improve performance without losing accuracy?

Accepted Answer

Import the dataset into SPICE (Super-fast, Parallel, In-memory Calculation Engine).. SPICE (Super-fast, Parallel, In-memory Calculation Engine) is Amazon QuickSight's in-memory engine that caches data, enabling fast query performance without losing accuracy. Importing the dataset into SPICE speeds up histogram computation while preserving full data. Option A is incorrect because using Athena with a limit clause reduces the number of rows, losing accuracy. Option B is incorrect because reducing bins changes histogram granularity, not necessarily improving performance and potentially losing detail. Option C is incorrect because sampling reduces accuracy by excluding data points.

Answer

Change the data source to Amazon Athena directly with a limit clause.

Answer

Reduce the number of bins in the histogram.

Answer

Use a sample of the data (e.g., 1 million rows) for the histogram.

Question 14

A data scientist is analyzing a dataset with missing values in several columns. The dataset is stored in an S3 bucket. What is the most efficient method to identify the percentage of missing values per column using AWS services?

Accepted Answer

Use Amazon Athena to query the data with SQL using COUNT(*) and CASE statements to compute missing percentage per column.. Amazon Athena allows running SQL queries directly on data in S3, and the COUNT and CASE statements can compute missing value percentages efficiently without moving data. Option A is wrong because Amazon SageMaker Notebook requires manual coding and is less efficient for quick checks. Option B is wrong because Amazon QuickSight is a visualization tool, not for direct SQL-based analysis. Option D is wrong because AWS Glue Crawler only catalogs metadata, not performing data analysis.

Answer

Use Amazon SageMaker Notebook with pandas to load the dataset and compute missing percentages.

Answer

Use Amazon QuickSight to connect to S3 and calculate missing value percentages via calculated fields.

Answer

Use AWS Glue Crawler to infer schema and view missing values statistics in the AWS Glue Data Catalog.

Question 15

A data scientist is training a random forest model for regression. The model shows high variance on the validation set. Which TWO actions are most likely to reduce variance? (Choose 2.)

Accepted Answer

Decrease the maximum depth of trees. Both decreasing the maximum depth of trees (B) and increasing the minimum samples per leaf (C) reduce the complexity of individual trees. Decreasing max depth limits tree growth, preventing overfitting to noise. Increasing min samples per leaf forces leaves to contain more samples, smoothing predictions and reducing variance. Together, these regularization techniques directly combat high variance in random forest models.

Answer

Use bootstrap sampling with replacement

Answer

Increase the number of trees in the forest

Answer

Increase the number of features considered at each split