MLS-C01 Practice Test 39 — 15 Questions

Question 1

A data scientist is reviewing a dataset and notices that the distribution of a numerical feature is heavily right-skewed with a long tail. Which visualization is most appropriate to assess the distribution?

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

Accepted Answer

Histogram with a logarithmic scale on the x-axis. A histogram with a logarithmic scale on the x-axis can effectively display the distribution of a heavily right-skewed numerical feature by compressing the long tail and making the shape more interpretable. Option A (box plot) is less suitable because it shows quartiles and outliers but not the full distribution shape. Option B (line chart) is used for time series or trends, not for distribution assessment. Option C (scatter plot) is for visualizing relationships between two variables, not a single variable's distribution.

Answer

Box plot

Answer

Line chart

Answer

Scatter plot

Question 2

A data scientist is working with a dataset that includes a 'timestamp' column. They want to create features that capture seasonality. Which feature engineering approach is most appropriate?

Accepted Answer

Extract hour of day and apply sine/cosine transformation.. Extracting hour of day and applying sine and cosine transformations captures the cyclic nature of time (e.g., midnight wrapping around to the next day). Option A (binning into fixed intervals) loses granularity and does not preserve cyclicity. Option B (converting to Unix epoch seconds) loses the cyclic pattern. Option D (one-hot encoding) creates many sparse features and does not capture order or cycles.

Answer

Bin timestamps into fixed intervals.

Answer

Convert timestamp to Unix epoch seconds.

Answer

One-hot encode the timestamp column.

Question 3

A data scientist is building a recommender system using Amazon SageMaker. The dataset contains user-item interactions with implicit feedback (clicks). Which THREE evaluation metrics are appropriate for this use case?

Accepted Answer

Precision@k. For implicit feedback (e.g., clicks) in recommender systems, evaluation metrics should focus on ranking quality without requiring explicit negative labels. Precision@k measures the proportion of relevant items among top-k recommendations, Recall@k measures the proportion of relevant items retrieved in top-k, and Mean Average Precision (MAP) averages precision across multiple recall levels, all suitable for top-k recommendation tasks. RMSE is for explicit rating prediction and requires ground truth ratings, not applicable to implicit feedback. AUC-ROC is for binary classification with both positive and negative samples, but implicit feedback lacks explicit negatives, making it unsuitable.

Answer

Root Mean Squared Error (RMSE)

Answer

Area Under the ROC Curve (AUC-ROC)

Question 4

A data scientist is trying to create a SageMaker training job but receives an access denied error. The IAM policy attached to the role is shown in the exhibit. What is the most likely cause of the error?

Accepted Answer

The policy does not allow s3:PutObject for the output location. The IAM policy attached to the role must include the s3:PutObject action to allow SageMaker to write the training output to the specified S3 bucket. Without this permission, the training job fails with an access denied error. Option B is incorrect because the policy likely includes sagemaker:CreateTrainingJob permission, which is necessary to start the job. Option C is incorrect because there is no explicit deny statement in the policy. Option D is incorrect because the training job needs to write output, not read from the output bucket; s3:GetObject is not required for the output location.

Answer

The policy does not allow sagemaker:CreateTrainingJob

Answer

The policy has an explicit deny on s3:PutObject

Answer

The policy does not allow s3:GetObject on the output bucket

Question 5

A data scientist is training a linear regression model and observes that the training loss is low but validation loss is high. Which step should the data scientist take to address this issue?

Accepted Answer

Apply L2 regularization to the model. The model is overfitting (low training loss, high validation loss). L2 regularization adds a penalty on the magnitude of coefficients, which discourages complexity and reduces overfitting. Increasing training epochs (B) would likely worsen overfitting by allowing the model to memorize more. Reducing the training dataset size (C) would provide less data, making overfitting worse. Adding more features (D) increases model complexity and typically exacerbates overfitting.

Answer

Increase the number of training epochs

Answer

Reduce the size of the training dataset

Answer

Add more features to the model

Question 6

During exploratory data analysis, a data scientist discovers that a feature has a variance of 0.01, while other features have variances around 1.0. Which action should be taken?

Accepted Answer

Consider removing the feature or applying variance threshold.. A feature with near-zero variance (0.01) compared to others (~1.0) likely has little predictive power and can cause numerical instability in models. Variance thresholding is a standard preprocessing step to remove low-variance features. Option A is wrong: scaling to unit variance does not address the fundamental issue of low information content; it merely changes the scale. Option B is wrong: log transformation changes distribution shape but does not meaningfully increase variance; the variance will remain low. Option C is wrong: imputation is for missing values, which is unrelated to low variance.

Answer

Scale the feature to have unit variance.

Answer

Apply a log transformation to the feature.

Answer

Impute missing values in the feature.

Question 7

A data scientist is analyzing a dataset and finds that the target variable has a bimodal distribution. Which preprocessing step is most appropriate before modeling?

Accepted Answer

Consider clustering to separate the two modes and model them separately.. Bimodal distributions indicate two distinct underlying groups. Clustering can separate the modes, allowing separate models for each cluster, which often improves performance. Option A is incorrect because standardizing does not change distribution shape. Option B is incorrect because removing outliers would not address bimodality; outliers are extreme values, not necessarily related to modes. Option D is incorrect because log transformation is for skewed unimodal distributions, not bimodal.

Answer

Standardize the target variable to have mean 0 and variance 1.

Answer

Remove outliers from the target variable.

Answer

Apply a log transformation to the target variable.

Question 8

A data scientist is working with a dataset containing geospatial coordinates (latitude and longitude) of customer locations. The scientist wants to engineer features such as distance to the nearest store, and cluster customers into regions. Which AWS service is best suited for performing geospatial analysis and clustering during exploratory data analysis?

Accepted Answer

Amazon SageMaker with custom Python scripts using scikit-learn and Geopy. Amazon SageMaker notebooks allow custom Python scripts using libraries like scikit-learn for clustering (e.g., K-Means) and Geopy for distance calculations, making it ideal for geospatial feature engineering and clustering during EDA. Option B is incorrect: Amazon Athena with PostGIS is for querying geospatial data, not for iterative analysis or clustering. Option C is incorrect: AWS Glue is an ETL service, not suited for interactive exploration and clustering. Option D is incorrect: Amazon Location Service provides maps and location tracking APIs, not a platform for analytical clustering.

Answer

Amazon Athena with PostGIS extensions

Answer

AWS Glue with geospatial transforms

Answer

Amazon Location Service

Question 9

You are a data engineer at a fintech company. The company processes real-time stock market data from multiple exchanges. The data is ingested via Amazon Kinesis Data Streams with 50 shards. Each record is about 1 KB, and the ingestion rate is 5,000 records per second. The data is consumed by a Java application running on Amazon ECS that performs real-time analytics and stores results in Amazon DynamoDB. Recently, the application has been experiencing high latency, and some records are stuck in the shards for minutes before being consumed. The CloudWatch metrics show that the application's CPU utilization is low, but the iterator age is increasing. The application uses the Kinesis Client Library (KCL) with a single worker. What is the most likely cause and how should it be fixed?

Accepted Answer

Scale the number of KCL workers to match the number of shards (e.g., 50 workers) to process shards in parallel.. A single KCL worker processes all shards sequentially, causing high iterator age with 50 shards. Scaling to 50 workers (one per shard) enables parallel processing, reducing latency. Option A is incorrect because 50 shards provide up to 50 MB/s write capacity, far exceeding the actual ~5 MB/s (5000 records/sec * 1 KB). Option B is incorrect because CPU utilization is low, indicating the bottleneck is not compute but parallelization. Option C is incorrect because DynamoDB write latency is not the issue; the problem is ingestion-side processing delay.

Answer

Increase the number of shards to 200 to provide more throughput.

Answer

Increase the CPU capacity of the ECS task by moving to a larger instance type.

Answer

Move the destination from DynamoDB to Amazon RDS to reduce write latency.

Question 10

A data scientist is analyzing a dataset with 10,000 rows and 50 columns. The target variable is binary. Which technique is most appropriate for identifying the most important features for predicting the target?

Accepted Answer

Train a Random Forest classifier and use feature_importances_. The most appropriate technique for identifying the most important features for predicting a binary target is to train a Random Forest classifier and use the built-in feature_importances_ attribute (Option C). Random Forest is a supervised ensemble method that provides a ranking of feature importance based on how much each feature reduces impurity (e.g., Gini impurity) across all trees. Option A (t-SNE) is a nonlinear dimensionality reduction technique primarily used for visualization in 2D/3D; it does not provide feature importance. Option B (K-means clustering) is an unsupervised clustering algorithm that does not use the target variable and cannot identify predictive features. Option D (PCA) is an unsupervised dimensionality reduction method that finds principal components maximizing variance, but these components are not directly interpretable as feature importance for a specific target variable.

Answer

Use t-SNE to reduce dimensionality and inspect clusters

Answer

Run K-means clustering and examine cluster centroids

Answer

Apply PCA and select components with highest variance

Question 11

A data analyst is exploring a dataset and wants to identify outliers in a numerical feature. Which visualization technique is most effective for detecting outliers?

Accepted Answer

Box plot. Box plot. A box plot is specifically designed to show quartiles and highlight outliers as points beyond the whiskers. A line chart (A) is for trends over time, a histogram (B) shows distribution but does not isolate outliers, and a scatter plot (C) is for bivariate relationships.

Answer

Line chart

Answer

Histogram

Answer

Scatter plot

Question 12

A retail company uses Amazon Redshift for its data warehouse. The data engineering team runs ETL jobs that load data from multiple sources into Redshift daily. They notice that the load performance is slow and the cluster CPU utilization is high during the ETL window. The team wants to improve load performance without changing the cluster configuration. They currently load data using INSERT statements from a staging table. What should they do?

Accepted Answer

Use the COPY command to load data from S3 in parallel. The COPY command is the most efficient way to load large amounts of data into Amazon Redshift because it uses the cluster's nodes in parallel to read data from Amazon S3, maximizing throughput. Option A (VACUUM and ANALYZE) are maintenance operations that reclaim space and update statistics, but they do not improve load performance. Option C (increasing node count) contradicts the requirement to not change cluster configuration. Option D (compression encoding) can reduce storage and improve scan performance but does not significantly speed up the initial load.

Answer

Run VACUUM and ANALYZE before loading

Answer

Increase the number of nodes in the Redshift cluster

Answer

Apply compression encoding on the staging table

Question 13

A company uses Amazon S3 to store log files from various applications. The logs are in JSON format and are appended to existing files every few minutes. A data analyst wants to run SQL queries on the logs using Amazon Athena. However, queries return incomplete results because Athena does not support modifying data. The team needs to enable querying of the latest log data with minimal changes to the existing ingestion process. Which solution should the team implement?

Accepted Answer

Create an Athena table using the Hive JSON SerDe that reads the logs directly from the existing S3 bucket.. Athena supports reading JSON data with the Hive JSON SerDe. By creating a table with the appropriate SerDe, the analyst can query the JSON logs directly without modifying the ingestion process. Option A is incorrect because converting to Parquet would require changing the ingestion process. Option B is incorrect because using Kinesis Data Firehose would require altering the ingestion pipeline. Option D is incorrect because loading into Redshift adds complexity and latency.

Answer

Convert the logs to Parquet format using a scheduled AWS Glue job and store them in a separate S3 bucket.

Answer

Stream the logs to Amazon Kinesis Data Firehose, which writes the data to S3 in Parquet format.

Answer

Use AWS Glue to load the JSON logs into Amazon Redshift and query using Redshift.

Question 14

A data scientist trains a model using Amazon SageMaker's built-in XGBoost algorithm. The model overfits on the training data. Which hyperparameter adjustment is MOST likely to reduce overfitting?

Accepted Answer

Increase the value of the lambda (L2 regularization) hyperparameter.. Increasing the lambda (L2 regularization) hyperparameter adds a penalty on the squared magnitude of the model weights, which discourages the model from fitting noise in the training data. This directly reduces overfitting by shrinking the influence of individual features, a standard regularization technique in XGBoost.

Answer

Increase the value of the max_depth hyperparameter.

Answer

Increase the value of the subsample hyperparameter to 1.0.

Answer

Increase the value of the num_round hyperparameter.

Question 15

Which THREE of the following are valid ways to deploy a model using SageMaker? (Select THREE.)

Accepted Answer

Deploy to a SageMaker batch transform job. SageMaker batch transform jobs allow you to run inference on an entire dataset asynchronously, processing large batches of data without requiring a persistent endpoint. This is ideal for offline predictions where low latency is not needed, and the job automatically manages compute resources, scaling, and output storage.

Answer

Deploy to AWS Lambda

Answer

Deploy to Amazon EC2 directly