MLS-C01 Practice Test 4 — 25 Questions

Question 1

A data scientist is working on a multi-class classification problem with 10 classes. The model outputs probabilities and the scientist wants to evaluate the model's ability to rank classes correctly. Which metric is most appropriate?

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

Accepted Answer

Area Under the ROC Curve (AUC-ROC). The most appropriate metric for evaluating a multi-class classifier's ability to rank classes (i.e., order classes by predicted probability) is the Area Under the ROC Curve (AUC-ROC). AUC-ROC measures the model's ability to distinguish between classes across all thresholds, and for multi-class problems it can be extended using one-vs-rest or macro/micro averaging. Log loss (Option D) measures probability calibration, not ranking. F1 score (Option A) is a threshold-dependent metric suitable for binary or per-class evaluation, not overall ranking. Accuracy (Option B) is also threshold-dependent and does not consider probability ranking. Therefore, Option C (AUC-ROC) is correct.

Answer

F1 score

Answer

Accuracy

Answer

Log loss

Question 2

A machine learning engineer is analyzing a dataset and observes that the distribution of a continuous feature is heavily right-skewed. Which transformation is most likely to make the distribution approximately normal?

Accepted Answer

Log transformation. A log transformation (C) is most appropriate for heavily right-skewed continuous data because it compresses the long right tail and can make the distribution approximately normal. Square root (A) is less effective for severe skewness. Exponential (B) would amplify the skewness. Box-Cox with lambda = 0 (D) is equivalent to log, but since log is explicitly given and commonly known, option C is the direct and correct choice.

Answer

Square root transformation

Answer

Exponential transformation

Answer

Box-Cox transformation with lambda = 0

Question 3

A data scientist is performing EDA on a dataset with a binary target variable. Which THREE techniques can help assess the relationship between a continuous feature and the target?

Accepted Answer

KDE plot grouped by target. Box plots (comparing distributions for each class), histograms (overlay or side-by-side), and KDE plots (probability density) are all effective for visualizing the relationship between a continuous feature and a binary target. Option A (scatter plot) requires two continuous variables. Option D (bar chart) is for categorical features.

Answer

Scatter plot against another continuous feature

Answer

Bar chart of feature values

Question 4

A data engineering team is designing a data pipeline that processes streaming data from Amazon Kinesis Data Streams using AWS Lambda. The team notices that some records are being processed multiple times (duplicates). Which TWO steps should the team take to ensure exactly-once processing?

Accepted Answer

Design the Lambda function to be idempotent.. Options A and B are correct. Making the Lambda function idempotent ensures that processing the same record multiple times does not cause duplicates downstream. Using a unique identifier per record and storing processed IDs in an external store like DynamoDB allows deduplication by checking if a record has already been processed. Option C is incorrect because increasing batch size does not prevent duplicates and may increase the chance of processing failures. Option D is incorrect because KPL provides exactly-once delivery to Kinesis Data Streams, not from the stream to Lambda, so deduplication is still needed. Option E is incorrect because disabling retries can lead to data loss without guaranteeing exactly-once processing.

Answer

Increase the batch size to reduce the number of invocations.

Answer

Use Kinesis Producer Library (KPL) to guarantee exactly-once delivery.

Answer

Disable retries on the Lambda function.

Question 5

Refer to the exhibit. A data scientist is unable to run an Amazon Athena query on data in `my-bucket`. The IAM policy shown is attached to the user. What is the most likely reason for the failure?

Accepted Answer

Athena needs s3:PutObject permission to write results.. Athena requires `s3:PutObject` permission to write query results to an output location. Without this permission, the query fails. Option A is not the issue because the bucket exists and is accessible; Option C is not necessarily relevant; Option D is incorrect because the bucket does exist (otherwise the user couldn't even attempt the query).

Answer

The ListBucket action is not granted.

Answer

The data is encrypted with SSE-C.

Answer

The bucket does not exist.

Question 6

A data scientist is analyzing a dataset with 1 million records and 20 features. The target variable is continuous. The scientist wants to identify non-linear relationships between features and the target. Which technique is MOST suitable for this purpose during exploratory data analysis?

Accepted Answer

Calculate mutual information scores between each feature and the target.. Mutual information captures any kind of dependency, including non-linear. Option A is wrong because a correlation matrix heatmap only shows pairwise linear correlations among features, not feature-target relationships. Option B is wrong because PCA is for dimensionality reduction and does not directly assess feature-target relationships. Option D is wrong because Pearson correlation only measures linear relationships, missing non-linear ones.

Answer

Visualize the correlation matrix heatmap of all features.

Answer

Apply Principal Component Analysis (PCA) and examine the loadings.

Answer

Compute Pearson correlation coefficients between each feature and the target.

Question 7

A company uses Amazon SageMaker Data Wrangler to perform exploratory data analysis. They want to detect outliers in a numerical column using the Interquartile Range (IQR) method. Which transformation should they apply in Data Wrangler?

Accepted Answer

Handle outliers. Amazon SageMaker Data Wrangler provides a 'Handle outliers' transform that supports IQR-based outlier detection. Option A (Impute) is used to fill missing values, not detect outliers. Option B (Normalize) scales data to a standard range. Option D (Binning) groups continuous values into intervals. Therefore, the correct transform to apply for IQR outlier detection is 'Handle outliers'.

Answer

Impute

Answer

Normalize

Answer

Binning

Question 8

Refer to the exhibit. A data scientist is running an Amazon EMR Spark job for exploratory data analysis on a large dataset. The job fails with the error shown. What is the most appropriate action to resolve this?

Accepted Answer

Increase the executor memory in Spark configuration.. The error message indicates an OutOfMemoryError in the Spark executors. Increasing executor memory (option C) directly addresses this by providing more heap space for data processing. Option A (fewer nodes) reduces total cluster memory, worsening the problem. Option B (Parquet format) can improve I/O performance but does not resolve insufficient memory allocation. Option D (increase driver memory) only helps the driver process, not the executors.

Answer

Reduce the number of worker nodes.

Answer

Convert the input data to Parquet format.

Answer

Increase the driver memory.

Question 9

A data scientist is exploring a dataset with many missing values. They want to understand the pattern of missingness before deciding on imputation. Which approach is most appropriate?

Accepted Answer

Visualize the missingness using a heatmap or bar chart.. Visualizing missingness with a heatmap or bar chart (using libraries like missingno) reveals patterns such as MCAR, MAR, or MNAR. Option A (correlation matrix) does not directly show missingness patterns. Option B (dropping rows) may remove valuable data and assumes MCAR. Option C (mean imputation) also assumes MCAR and can bias results if missingness is not random.

Answer

Compute the correlation matrix of the features with missing values.

Answer

Drop all rows with any missing values.

Answer

Impute all missing values with the mean of each column.

Question 10

Which TWO metrics are appropriate for evaluating a binary classification model trained on imbalanced data? (Select TWO.)

Accepted Answer

F1 score. The F1 score is appropriate for imbalanced binary classification because it balances precision and recall, making it robust when the positive class is rare. Unlike accuracy, it does not get inflated by a majority negative class, and it directly penalizes models that predict the majority class for all instances.

Answer

Log loss

Answer

Accuracy

Answer

ROC-AUC

Question 11

A data scientist is analyzing a dataset with 100 features and wants to identify which features are most correlated with the target variable. Which AWS service is most appropriate for this task?

Accepted Answer

Amazon SageMaker Data Wrangler. Amazon SageMaker Data Wrangler provides built-in data analysis and visualization capabilities, including correlation analysis, making it suitable for this task. Amazon QuickSight is a BI tool for dashboards, not for feature correlation analysis. Amazon Athena is a query service for data in S3, not for embedded data wrangling. AWS Glue DataBrew is a visual data preparation tool, but SageMaker Data Wrangler is more directly suited for correlation analysis.

Answer

Amazon QuickSight

Answer

Amazon Athena

Answer

AWS Glue DataBrew

Question 12

A data engineer needs to transfer 50 TB of historical data from an on-premises Hadoop cluster to Amazon S3. The company has a 100 Mbps internet connection and a tight deadline of two weeks. Which AWS service should the engineer use to transfer the data most efficiently?

Accepted Answer

AWS Snowball Edge. B is correct because transferring 50 TB over a 100 Mbps connection would take approximately 48 days (50 TB * 8 / 100 Mbps / 86400 seconds/day), far exceeding the two-week deadline. AWS Snowball Edge is a physical data transport device that can securely transfer petabytes of data offline, bypassing network bandwidth constraints entirely. For large datasets and tight deadlines, Snowball Edge is the most efficient AWS service.

Answer

AWS Storage Gateway (Volume Gateway)

Answer

Amazon S3 Transfer Acceleration

Answer

AWS DataSync over the internet

Question 13

A data scientist is performing EDA on a dataset with 500,000 rows and 10 columns. The dataset is stored in an S3 bucket as CSV files. The scientist wants to generate summary statistics (mean, median, min, max) for all numeric columns. Which service allows the quickest ad-hoc analysis without provisioning any infrastructure?

Accepted Answer

Amazon Athena. Amazon Athena can query data in S3 directly using SQL. Option A is wrong because AWS Glue ETL requires job setup. Option C is wrong because Amazon SageMaker Data Wrangler requires a notebook instance. Option D is wrong because QuickSight is for visualization, not direct summary statistics.

Answer

AWS Glue ETL

Answer

Amazon SageMaker Data Wrangler

Answer

Amazon QuickSight

Question 14

A company uses Amazon SageMaker to train a model for fraud detection. The dataset has 1 million transactions, with 0.1% fraud. The data scientist trains a random forest model and achieves 99.9% accuracy but 0% recall on the fraud class. Which technique is most likely to improve recall without significantly reducing precision?

Accepted Answer

Apply SMOTE to generate synthetic fraud samples. SMOTE (Synthetic Minority Oversampling Technique) generates synthetic fraud samples by interpolating between existing minority class instances, which directly addresses the extreme class imbalance (0.1% fraud). This increases the representation of the fraud class in the training data, allowing the random forest model to learn decision boundaries that capture fraud patterns, thereby improving recall without introducing the noise or information loss associated with other methods.

Answer

Tune the classification threshold

Answer

Use cost-sensitive learning with a high cost for fraud misclassification

Answer

Undersample the majority class

Question 15

A data scientist is analyzing a dataset with a target variable that is highly imbalanced (only 1% positive class). The goal is to build a binary classifier. During exploratory data analysis, which metric is MOST appropriate to evaluate the performance of different sampling strategies before model training?

Accepted Answer

Area Under the Receiver Operating Characteristic Curve (AUC ROC). The most appropriate metric during exploratory data analysis for evaluating sampling strategies with imbalanced data is AUC ROC, as it is independent of the class distribution and measures the model's ability to distinguish between positive and negative classes regardless of the threshold. Option A (RMSE) is used for regression tasks, not classification. Option C (F1 score) depends on a specific threshold and can be affected by sampling changes. Option D (Accuracy) is misleading for imbalanced datasets because a high accuracy can be achieved by predicting the majority class.

Answer

Root Mean Squared Error (RMSE)

Answer

F1 score

Answer

Accuracy

Question 16

A Glue job fails with an AccessDenied error when trying to write to the S3 bucket my-data-lake. The IAM policy attached to the job role is shown in the exhibit. What is the MOST likely reason for the failure?

Accepted Answer

The job role does not have permissions to decrypt the KMS key used for server-side encryption. The policy allows s3:PutObject on the bucket, so write access seems granted. However, if the bucket is encrypted with SSE-KMS, the job also needs kms:Decrypt and kms:GenerateDataKey permissions. The policy does not include KMS actions. The bucket policy might also deny, but the most common issue is KMS encryption.

Answer

The s3:ListBucket action is missing on the bucket level

Answer

The s3:PutObject action is not sufficient; the job needs s3:PutObjectAcl

Answer

The resource ARN for s3:PutObject should include a specific prefix

Question 17

A data scientist runs the AWS CLI command shown in the exhibit to list objects larger than 100 KB in an S3 bucket. The data scientist wants to understand the size distribution of these files. What is the most significant limitation of this approach for EDA?

Accepted Answer

The command may return incomplete results if there are more than 1000 objects.. The AWS CLI `list-objects` command returns a maximum of 1000 objects by default. If the bucket contains more than 1000 objects larger than 100 KB, the command will only return the first 1000, leading to incomplete results for EDA. Option A is incorrect because the command uses `> 100000` which excludes objects exactly 100 KB, but that is not the most significant limitation. Option C is incorrect because the query syntax is valid. Option D is incorrect because the command does return the object keys (file names) as well as sizes.

Answer

The command only returns objects larger than 100 KB, not equal to.

Answer

The command uses the wrong query syntax and will fail.

Answer

The command does not return the file names, only sizes.

Question 18

During exploratory data analysis, a data scientist notices that a feature has a highly skewed distribution. Which transformation is most likely to make the distribution approximately normal?

Accepted Answer

Log transformation. Log transformation is commonly used to reduce right skewness and make the distribution approximately normal. Option B (min-max scaling) is incorrect because it does not change the shape of the distribution. Option C (one-hot encoding) is incorrect because it is used for categorical variables, not for transforming continuous skewed data. Option D (standardization) is incorrect because it does not change the shape of the distribution.

Answer

Min-max scaling

Answer

One-hot encoding

Answer

Standardization (z-score)

Question 19

A data scientist is performing EDA on a dataset stored in Amazon S3 using Amazon Athena. The dataset is partitioned by date, and each partition contains CSV files. The data scientist notices that some queries return zero rows for partitions that should have data. Which THREE steps should the data scientist take to troubleshoot? (Choose 3.)

Accepted Answer

Verify that the CSV files exist in the S3 bucket for the specific partition.. Verifying that the CSV files actually exist in the S3 bucket for the specific partition confirms whether data is present. Option B is correct because MSCK REPAIR TABLE adds new partitions to the Glue Data Catalog that may not have been registered automatically. Option D is correct because incorrect data types in the query's WHERE clause can cause filters to exclude rows, resulting in zero returned rows. Option C is incorrect because converting to Parquet is not a troubleshooting step for this issue—it is an optimization. Option E is incorrect because adding a LIMIT clause does not force partition discovery; it only limits the number of rows returned.

Answer

Convert the CSV files to Parquet format.

Answer

Re-run the query with a LIMIT clause to force partition discovery.

Question 20

Refer to the exhibit. A data scientist is assigned an IAM policy to deploy a SageMaker model. When the scientist tries to create an endpoint, the action fails with an authorization error. What is the missing permission?

Accepted Answer

iam:PassRole. The error occurs because the IAM policy does not include the `iam:PassRole` permission. When creating a SageMaker endpoint, the service must assume an IAM role to access resources (e.g., S3 buckets, CloudWatch). The `iam:PassRole` permission allows the user to pass that role to SageMaker. The other actions listed are either for inference (`InvokeEndpoint`), listing endpoints (`ListEndpoints`), or updating endpoints (`UpdateEndpoint`), which are not relevant to the creation process. Therefore, the missing permission is `iam:PassRole` (Option A).

Answer

sagemaker:ListEndpoints

Answer

sagemaker:InvokeEndpoint

Answer

sagemaker:UpdateEndpoint