MLS-C01 Practice Test 40 — 15 Questions

Question 1

An ML team is using SageMaker Autopilot to automatically build a binary classification model. The dataset has 500,000 rows and 200 columns, with a severe class imbalance (1% positive). Which configuration should the team set to address the imbalance?

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

Accepted Answer

Specify the 'objective' as 'F1' or 'AUC' to optimize for imbalanced data.. SageMaker Autopilot allows specifying the objective metric for optimization. For imbalanced datasets, metrics like F1 score or AUC are more appropriate than accuracy because they account for precision and recall or the trade-off between true positive and false positive rates. By setting the objective to 'F1' or 'AUC', Autopilot will optimize the model for these metrics, which better handle class imbalance. Option B (MulticlassClassification) is for multi-class problems, not binary imbalance; Option C (SMOTE) is not supported by Autopilot; Option D (stratified splitting) helps ensure representative validation splits but does not directly address the imbalance in model optimization.

Answer

Set the 'problem_type' to 'MulticlassClassification' to handle imbalance.

Answer

Use the 'AutoML' job with 'EnsembleMode' and 'SMOTE' sampling.

Answer

Configure the data split to use stratified sampling based on the target.

Question 2

A company uses Amazon Redshift for data warehousing. The data engineering team needs to load data from multiple S3 buckets into Redshift daily. Each bucket contains files in different formats (CSV, JSON, Parquet). Which AWS service is BEST suited to automate this ingestion process?

Accepted Answer

AWS Glue. AWS Glue is a fully managed ETL service that can crawl S3 buckets to discover schema, handle various formats (CSV, JSON, Parquet), and load data into Redshift. It automates the ingestion process without the need for manual infrastructure management. Amazon EMR with Spark requires more setup and management, AWS Data Pipeline is less flexible and older, and AWS Database Migration Service is designed for migrating entire databases, not for loading from S3.

Answer

Amazon EMR with Apache Spark

Answer

AWS Data Pipeline

Answer

AWS Database Migration Service (DMS)

Question 3

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) is correct because it divides the dataset into homogeneous subgroups (strata) and samples proportionally from each, ensuring all subgroups are represented and reducing bias. This is especially important when the data is imbalanced. Option B (Simple random sampling) is incorrect because it may underrepresent or miss rare subgroups, leading to biased samples. Option C (Systematic sampling) is incorrect because it can introduce bias if the data has periodic patterns. Option D (Reservoir sampling) is incorrect because it is designed for streaming data where the total size is unknown, not for a static dataset of 10 million records in Amazon S3.

Answer

Simple random sampling

Answer

Systematic sampling

Answer

Reservoir sampling

Question 4

Refer to the exhibit. A data engineer runs the AWS CLI command to check an object in an S3 bucket. The bucket is part of a data lake and is configured with versioning enabled. However, the output shows "VersionId": null. What is the most likely reason for this?

Accepted Answer

The object was uploaded before versioning was enabled. The most likely reason is that the object was uploaded before versioning was enabled on the bucket. When versioning is enabled, objects uploaded afterward receive a unique version ID, while objects that existed before versioning was enabled have a null version ID. Option B is correct. Option A is incorrect because SSE-S3 encryption does not affect version IDs; version IDs are metadata independent of encryption. Option C is incorrect because the `head-object` command automatically returns the version ID of the latest version; no `--version-id` parameter is needed to display it. Option D is incorrect because the bucket is explicitly stated to have versioning enabled.

Answer

The object is encrypted using SSE-S3, which hides the version ID

Answer

The command must include the --version-id parameter to display the version ID

Answer

Versioning is not enabled on the bucket

Question 5

Which THREE factors should be considered when choosing between Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose for a real-time data ingestion pipeline? (Choose 3.)

Accepted Answer

Need for custom data processing using AWS Lambda. Kinesis Data Streams supports custom processing via AWS Lambda consumers (using the Kinesis Client Library or direct integration), enabling real-time transformations, filtering, or enrichment. Kinesis Data Firehose does not natively support custom Lambda processing for transformation; it only allows optional Lambda functions for data format conversion or transformation before delivery, but not for arbitrary real-time processing logic.

Answer

Ability to compress data before delivery

Answer

Ability to encrypt data at rest

Question 6

A data engineer needs to ingest data from an on-premises Apache Kafka cluster into Amazon S3 with minimal latency (under 5 minutes) for real-time analytics. The data volume is approximately 10 MB per second. Which solution is MOST cost-effective and meets the latency requirement?

Accepted Answer

Use Amazon MSK to mirror the on-premises Kafka cluster, then use Kinesis Firehose to write to S3. Amazon MSK (Managed Streaming for Apache Kafka) can mirror the on-premises Kafka topics to the cloud with near-real-time replication (typically under 1 minute), and then Kinesis Firehose can be configured with a 60-second buffer interval to deliver data to Amazon S3, meeting the under-5-minute latency requirement. This solution is cost-effective because MSK eliminates the need to manage Kafka infrastructure, and Firehose provides serverless, pay-per-use data delivery without provisioning servers.

Option B (S3 Transfer Acceleration) is designed for accelerating file uploads over long distances, not for continuous streaming of data from Kafka; it would require additional application changes and is not suitable for real-time streaming.

Option C (Kinesis Data Streams with Direct Connect) would require re-architecting the data pipeline to send data directly to Kinesis, adding complexity and cost (Direct Connect bandwidth). Kinesis Data Streams also requires managing shards and does not natively integrate with Kafka replication.

Option D (VPN + Lambda) introduces additional latency due to VPN overhead and Lambda cold starts, and Lambda is not optimized for high-throughput streaming of 10 MB/s; it would likely cause throttling or increased costs. Therefore, option A is the most cost-effective and meets the latency requirement.

Answer

Use Amazon S3 Transfer Acceleration for direct uploads from on-premises

Answer

Use Amazon Kinesis Data Streams with a Direct Connect connection from on-premises

Answer

Set up a VPN connection and use AWS Lambda to consume from Kafka and write to S3

Question 7

A data scientist is analyzing a dataset with a target variable that is binary (0/1). Which visualization is most appropriate to explore the relationship between a continuous feature and the target?

Accepted Answer

Box plot of the feature grouped by target.. The most appropriate visualization to explore the relationship between a continuous feature and a binary target is a box plot of the feature grouped by the target. This allows comparing the distribution of the continuous feature across the two target classes (0 and 1), revealing differences in central tendency, spread, and outliers. Option A (scatter plot) is unsuitable because scatter plots are for two continuous variables, not a binary target. Option B (bar chart) is typically used for categorical features, not continuous ones. Option D (histogram) shows the distribution of a single continuous variable but does not separate by target class, so it cannot reveal the relationship with the binary target. Therefore, option C is correct.

Answer

Scatter plot of the feature vs. the target.

Answer

Bar chart of the feature.

Answer

Histogram of the feature.

Question 8

A data scientist is analyzing a dataset and notices that the distribution of a continuous feature is heavily right-skewed. Which transformation is most likely to make the distribution more symmetric?

Accepted Answer

Log transformation (natural log). Log transformation is commonly used to reduce right skewness by compressing the range of large values. Option B is wrong because Min-Max scaling only rescales the data to a fixed range and does not alter the distribution shape, so it cannot reduce skewness. Option C is wrong because one-hot encoding is designed for categorical features and does not apply to continuous features. Option D is wrong because a square transformation (power >1) amplifies larger values more than smaller ones, which would increase right skewness rather than reduce it.

Answer

Min-Max scaling

Answer

One-hot encoding

Answer

Square transformation

Question 9

A data scientist is analyzing a dataset with a binary target variable. The dataset is highly imbalanced (99% negative class). Which metric is most appropriate for evaluating the model's performance during exploratory data analysis?

Accepted Answer

Area Under the ROC Curve (AUC-ROC). In highly imbalanced datasets (99% negative class), accuracy is misleading because a model that predicts the majority class always achieves 99% accuracy. Precision focuses on false positives and is threshold-dependent. F1 score balances precision and recall but is sensitive to the chosen threshold and may not reflect overall performance. AUC-ROC evaluates the model's ability to distinguish between classes across all thresholds and is robust to class imbalance, making it the most appropriate metric for initial model evaluation.

Answer

Accuracy

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Precision

Answer

F1 Score

Question 10

A company uses Amazon SageMaker to train a model. The training job runs successfully but the model artifacts are not saved to the specified S3 output path. What is a likely cause?

Accepted Answer

The training script does not save the model to /opt/ml/model.. Amazon SageMaker expects the training script to save the model artifacts to the `/opt/ml/model` directory. After the training job completes, SageMaker automatically copies the contents of this directory to the specified S3 output path. If the script saves the model elsewhere (e.g., `/tmp` or a custom path), no artifacts will be uploaded, resulting in an empty or missing S3 output.

Answer

The model size exceeds the S3 bucket limit.

Answer

The training job used spot instances.

Answer

The S3 bucket is in a different AWS Region.

Question 11

A data scientist is exploring a dataset and finds that the variance of a feature is 0. What should be done with this feature?

Accepted Answer

Remove the feature from the dataset. When a feature has zero variance, it means all values are identical (constant). Such a feature provides no discriminative information for machine learning models and can be safely removed. Removing it reduces dimensionality without losing any information. Option A is correct. Option B (creating interaction terms) is incorrect because interacting a constant with any feature yields a constant. Option C (min-max scaling) does not change the fact that the feature is constant; it remains constant after scaling. Option D (imputing missing values) is irrelevant since zero variance does not imply missing values.

Answer

Create interaction terms with other features

Answer

Apply Min-Max scaling to normalize the feature

Answer

Impute missing values using the mean

Question 12

A team is exploring a dataset with missing values in multiple columns. They want to decide whether to drop rows or impute values. Which approach is most appropriate for exploratory data analysis?

Accepted Answer

Analyze the missing data pattern using visualizations and summary statistics. During EDA, the first step is to understand the pattern and extent of missing data using visualizations and summary statistics. This helps determine whether missingness is random or systematic, and guides the choice of imputation or deletion. Option A is wrong because imputing with the mean without understanding the missing mechanism can introduce bias. Option C is wrong because dropping rows may discard valuable data and reduce sample size unnecessarily. Option D is wrong because using SageMaker Data Wrangler is a specific tool and may not be necessary; EDA focuses on understanding data, not automated imputation.

Answer

Impute missing values with the mean of each column

Answer

Drop all rows with missing values to ensure data quality

Answer

Use Amazon SageMaker Data Wrangler to automatically impute missing values

Question 13

An ML engineer is performing EDA on a dataset of customer transactions. The dataset has 1 million rows and 20 columns, including a 'transaction_amount' column. The engineer notices that 5% of the transaction amounts are negative, which are data entry errors. The rest are positive. Which approach is most appropriate for handling these negative values during EDA?

Accepted Answer

Remove rows with negative transaction amounts from the dataset.. Removing rows with negative transaction amounts is the most appropriate approach during EDA. The negative values are data entry errors, not legitimate transactions. Removing them cleans the dataset without introducing bias from imputation or transformation. Option A is incorrect because imputing negative values with the median would treat the errors as missing data, but they are not missing; they are erroneous. This could distort the distribution. Option C is incorrect because taking absolute values would convert errors into positive values, adding noise and misrepresenting the data (e.g., a negative $100 error becomes a legitimate $100 transaction). Option D is incorrect because capping negative values at zero would create a spike at zero and distort the distribution, treating errors as valid zero amounts. Therefore, removal is the cleanest approach for erroneous data.

Answer

Impute the negative values with the median of positive transaction amounts.

Answer

Take the absolute value of the negative transaction amounts.

Answer

Cap the negative values at zero.

Question 14

A data scientist is performing EDA on a dataset with both numerical and categorical features. Which technique is best for detecting multicollinearity among numerical features?

Accepted Answer

Correlation matrix with heatmap. A correlation matrix quantifies linear relationships between numerical features, and a heatmap visualizes these correlations, making it effective for detecting multicollinearity. Option A is wrong because the chi-square test of independence is used for categorical variables, not numerical features. Option B is wrong because box plots show distributions and outliers, not relationships between features. Option D is wrong because pair plots provide a visual scatter plot matrix but do not offer a quantitative measure of multicollinearity like a correlation matrix does.

Answer

Chi-square test of independence

Answer

Box plots for each numerical feature

Answer

Pair plot

Question 15

During exploratory data analysis, a data scientist observes a strong correlation (r=0.95) between two numeric features. The model to be trained is a linear regression. What is the most appropriate action?

Accepted Answer

Remove one of the correlated features.. High correlation (r=0.95) between two features indicates severe multicollinearity in linear regression, which can cause unstable coefficient estimates and inflated standard errors. The most straightforward solution is to remove one of the correlated features (Option B), as it directly eliminates the redundancy. Option A (standardization) does not affect correlation. Option C (L2 regularization) can help but is not the first choice because removal is simpler and preserves interpretability; regularization only shrinks coefficients but does not remove the linear dependence. Option D (interaction term) would increase multicollinearity, making the problem worse.

Answer

Apply standardization to both features.

Answer

Use L2 regularization (Ridge regression) without removing features.

Answer

Create an interaction term between the two features.