MLS-C01 Mock Exam 84 — 25 Questions

Question 1

A machine learning engineer is evaluating a dataset for building a fraud detection model. The dataset has 1 million transactions, but only 500 are fraudulent. The engineer wants to understand the distribution of fraudulent vs. non-fraudulent transactions over time. Which EDA visualization is most suitable?

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

Accepted Answer

Line plot of daily fraud rate and non-fraud rate. A time series line plot with two lines (fraud vs. non-fraud) shows temporal patterns. Option A is wrong because bar chart of counts per day is less effective for two categories. Option B is wrong because scatter plot with 1 million points is overwhelming. Option C is wrong because box plot shows distribution per time period but not temporal trend.

Answer

Bar chart of transaction count per day with colors for fraud status

Answer

Scatter plot of transactions over time colored by fraud status

Answer

Box plot of transaction amount per month grouped by fraud status

Question 2

A team is using Amazon SageMaker to train a linear regression model on a dataset with 10 features. After training, they notice the model has high bias. Which action is MOST likely to reduce bias?

Accepted Answer

Add polynomial features to capture non-linear relationships. High bias indicates underfitting, which can be reduced by adding more features or increasing model complexity. Option A reduces risk of overfitting, not bias. Option B increases regularization, which increases bias. Option C reduces data, potentially increasing bias.

Answer

Increase the regularization parameter lambda

Answer

Add L2 regularization

Answer

Use a smaller training dataset

Question 3

An IAM policy is attached to a group. A user in the group tries to read the object s3://data-lake-bucket/sensitive/file.txt from an IP address 192.168.1.1. What will happen?

Accepted Answer

The request is denied because of the Deny statement. The Deny statement explicitly denies any S3 action on the sensitive prefix when the source IP is not from 10.0.0.0/8. Since the IP 192.168.1.1 is not in that range, the Deny applies. Deny statements override Allow statements. So the user is denied access.

Answer

The request is allowed because the Allow statement grants s3:GetObject

Answer

The request is allowed because the Deny condition does not match

Answer

The request is denied because the policy has no explicit Allow for the sensitive prefix

Question 4

A data scientist needs to perform hyperparameter optimization for a gradient boosting model. Which built-in Amazon SageMaker feature should they use?

Accepted Answer

Amazon SageMaker Automatic Model Tuning. Amazon SageMaker Automatic Model Tuning (A) is the built-in feature specifically designed for hyperparameter optimization. It automates the search for the best combination of hyperparameters by launching multiple training jobs with different hyperparameter values, using strategies like Bayesian optimization, random search, or Hyperband. This directly addresses the data scientist's need to optimize a gradient boosting model's hyperparameters.

Answer

Amazon SageMaker Clarify

Answer

Amazon SageMaker Debugger

Answer

Amazon SageMaker Neo

Question 5

Which TWO of the following are valid configurations for SageMaker Training Job resource limits? (Select TWO.)

Accepted Answer

Maximum number of instances. SageMaker Training Jobs allow you to set a resource limit on the maximum number of instances that can be used for distributed training, which helps control costs and prevent accidental over-provisioning. Option C is correct because you can specify a maximum run time in seconds for a training job; if the job exceeds this limit, SageMaker automatically stops it, ensuring you don't incur unexpected charges.

Answer

Maximum wait time in seconds

Answer

Minimum number of instances

Answer

Maximum number of spot instances

Question 6

A company processes large streams of IoT sensor data using Amazon Kinesis Data Streams with 100 shards. Each sensor reading is about 1 KB. The data is consumed by an Amazon EMR cluster running Spark Streaming jobs. The team notices that the Spark Streaming job's processing time is gradually increasing, and the stream is falling behind. They suspect the issue is due to skewed data distribution across shards. Which approach should the team take to diagnose and resolve the issue?

Accepted Answer

Modify the producer to add a random prefix to the partition key, ensuring even distribution across all shards, and monitor the stream using CloudWatch.. Adding a random prefix to the partition key ensures that sensor data is evenly distributed across all 100 shards, eliminating hot shards that cause processing delays. This directly addresses the skewed data distribution issue without requiring infrastructure changes, and the team can monitor the improvement using CloudWatch metrics like IncomingBytes and ReadProvisionedThroughputExceeded.

Answer

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

Answer

Check Amazon CloudWatch metrics for Kinesis to identify hot shards, then manually redistribute the data by repartitioning in Spark.

Answer

Use the Kinesis Client Library (KCL) with a custom worker to rebalance the load across shards.

Question 7

During exploratory data analysis, a data scientist plots the distribution of a numerical feature and observes a heavy right skew. The feature has many outliers at the high end. Which transformation is most appropriate to reduce skewness?

Accepted Answer

Apply a log transformation to the feature.. A log transformation compresses the range of the data, reducing the impact of extreme values and pulling in the long tail of a right-skewed distribution. This makes the feature more normally distributed, which is often required for linear models and many statistical tests. It is the standard technique for handling positive-valued features with heavy right skew.

Answer

Apply z-score normalization.

Answer

Apply one-hot encoding.

Answer

Apply min-max scaling.

Question 8

A data engineer is tasked with building a data pipeline that moves data from an on-premises database to Amazon S3 for analytics. The database is a MySQL instance that is 2 TB in size. The company has a 1 Gbps dedicated network connection to AWS (AWS Direct Connect). The data must be transferred once daily. The engineer needs to choose the most efficient and reliable service for this task. Which service should they use?

Accepted Answer

AWS Database Migration Service (DMS). AWS Database Migration Service (DMS) is the correct choice because it is purpose-built for migrating databases to AWS, supporting continuous replication and one-time migrations from MySQL to Amazon S3. It can handle the 2 TB dataset efficiently over a 1 Gbps Direct Connect link by using change data capture (CDC) for ongoing replication and parallel tasks for throughput, ensuring reliability with built-in monitoring and restart capabilities.

Answer

AWS DataSync

Answer

AWS Glue

Answer

Amazon S3 Transfer Acceleration

Question 9

A company is training a deep learning model on Amazon SageMaker. The training job is taking a long time and the data scientist suspects that the model is overfitting. Which of the following actions can help reduce overfitting and improve generalization?

Accepted Answer

Add dropout layers to the model architecture.. Adding dropout layers is a regularization technique that randomly drops neurons during training to prevent overfitting. Increasing the number of epochs (Option B) would likely worsen overfitting. Using a larger batch size (Option C) can sometimes help generalization but is not a direct regularization technique. Removing regularization (Option D) would increase overfitting.

Answer

Increase the batch size used during training.

Answer

Increase the number of training epochs.

Answer

Remove regularization terms from the loss function.

Question 10

A data scientist is training a binary classification model using Amazon SageMaker's built-in XGBoost algorithm. The dataset is highly imbalanced (95% negative class, 5% positive class). The model achieves high accuracy but poor recall on the positive class. Which TWO actions should the data scientist take to improve recall without significantly sacrificing precision?

Accepted Answer

Set scale_pos_weight to the ratio of negative to positive samples.. Options B and E are correct. Using scale_pos_weight adjusts the weight of the positive class, directly addressing imbalance. SMOTE oversamples the minority class to balance the dataset. Option A is wrong because subsampling the majority class may lose information. Option C is wrong because increasing max_depth may overfit. Option D is wrong because reducing eta may slow convergence but not directly help imbalance.

Answer

Perform random undersampling of the majority class.

Answer

Increase the max_depth hyperparameter.

Answer

Reduce the learning rate (eta) and increase num_round.

Question 11

A team is training a deep learning model using TensorFlow on a single GPU instance in SageMaker. The GPU utilization is below 30%. Which change will MOST improve GPU utilization?

Accepted Answer

Increase the batch size. Increasing the batch size makes more efficient use of GPU memory and parallel processing, improving utilization.

Answer

Reduce the number of epochs

Answer

Use SageMaker Distributed Training with multiple GPUs

Answer

Switch to a CPU instance

Question 12

A data scientist is building a regression model to predict housing prices. The dataset includes numerical features such as square footage, number of bedrooms, and year built, as well as categorical features such as neighborhood and roof type. Which TWO preprocessing steps are most important to apply before training a linear regression model?

Accepted Answer

One-hot encode categorical features. Linear regression requires numerical features and is sensitive to feature scales. Encoding categorical variables as numerical is necessary, and scaling numerical features ensures that no single feature dominates the model.

Answer

Apply principal component analysis (PCA) for dimensionality reduction

Answer

Remove outliers using IQR

Answer

Add interaction terms between all features

Question 13

A company runs a critical data pipeline using Apache Spark on Amazon EMR. The pipeline reads data from Amazon S3, performs complex transformations, and writes results back to S3. The job runs every hour and must complete within 30 minutes. Recently, the job has been taking longer and occasionally failing due to executor losses. The team suspects memory pressure. Which action should the team take to improve stability and performance without increasing cost?

Accepted Answer

Enable Spark dynamic allocation to adjust executors based on workload.. Enabling Spark dynamic allocation allows the cluster to automatically scale the number of executors up and down based on the workload. This helps alleviate memory pressure by releasing idle executors and requesting additional executors only when needed, improving resource utilization without increasing overall cluster cost. Option A is incorrect because simply increasing spark.executor.memory may cause YARN container failures if the instance memory is exceeded, and does not address the root cause of executor losses. Option B is incorrect because adding core nodes increases cost and may not resolve memory pressure if the issue is inefficient resource allocation. Option C is incorrect because decreasing shuffle partitions reduces parallelism and can increase memory per task, potentially worsening memory pressure and prolonging job runtime.

Answer

Increase the spark.executor.memory setting to allocate more memory per executor.

Answer

Increase the number of core nodes in the EMR cluster.

Answer

Decrease the number of shuffle partitions (spark.sql.shuffle.partitions) to reduce overhead.

Question 14

A machine learning engineer is monitoring a deployed model on SageMaker and notices that the prediction latency is increasing over time. The model is a linear regression with a small number of features. Which is the MOST likely cause?

Accepted Answer

The inference code has a memory leak. Memory leak or accumulation of model artifacts in inference code can cause latency growth over time.

Answer

The number of features is too large

Answer

The CPU utilization is too low

Answer

The model is overfitting to recent data

Question 15

A company is using Amazon SageMaker to deploy a model that predicts customer churn. The model was trained using a linear learner algorithm. During inference, the endpoint returns predictions that are always 0.5 (the probability of churn). What is the most likely cause?

Accepted Answer

The learning rate is set too high, causing the model to converge to the mean prediction. If the model always outputs 0.5, it suggests that the model is not learning and is stuck at the prior probability. This often happens when the learning rate is too high (causing divergence) or too low (causing slow convergence) so that the model does not update weights. The other options would cause different symptoms: data imbalance might bias towards 0 or 1, not exactly 0.5; feature scaling issues typically cause NaN or poor convergence; insufficient epochs might not converge but not necessarily give exactly 0.5.

Answer

The dataset is highly imbalanced, and the model is predicting the majority class

Answer

The model was trained with too few epochs

Answer

The input features are not normalized

Question 16

A data scientist is evaluating a regression model. The RMSE on the training set is 2.5, and on the test set is 2.7. The R² on the test set is 0.98. What does this indicate?

Accepted Answer

The model generalizes well with no severe overfitting. The model has low error and high R² on both sets, indicating good generalization without significant overfitting. The small difference between training and test RMSE suggests no severe overfitting.

Answer

The model has high bias

Answer

The model is underfitting because R² is too high

Answer

The model is overfitting because RMSE is lower on training data

Question 17

A data scientist is training a binary classification model on an imbalanced dataset where the positive class represents 5% of the data. Which metric is most appropriate for evaluating model performance?

Accepted Answer

AUC-ROC. AUC-ROC is robust to class imbalance and measures the trade-off between true positive rate and false positive rate. Option A is wrong because accuracy can be misleading with imbalanced data. Option C is wrong because RMSE is for regression. Option D is wrong because R-squared is for regression.

Answer

Accuracy

Answer

Root Mean Squared Error (RMSE)

Answer

R-squared

Question 18

A data scientist is training a k-means clustering model on a dataset with 1,000 points. The scientist uses the elbow method to choose the number of clusters. The elbow plot shows a clear bend at k=4. After running k-means with k=4, the scientist wants to evaluate the quality of the clustering. Which THREE of the following are suitable internal clustering validation metrics? (Choose THREE.)

Accepted Answer

Calinski-Harabasz index. Silhouette score, Davies-Bouldin index, and Calinski-Harabasz index are all internal validation metrics that do not require ground truth labels. They measure compactness and separation. Rand index and adjusted Rand index require ground truth labels (external validation).

Answer

Adjusted Rand index

Answer

Rand index

Question 19

A machine learning engineer is using Amazon SageMaker to train a deep learning model. The training job is taking longer than expected. The engineer notices that the GPU utilization is low (around 30%) while CPU utilization is high. Which action is most likely to improve training speed?

Accepted Answer

Increase the number of data loading workers. Low GPU utilization with high CPU utilization suggests a data loading bottleneck. Increasing the number of data loading workers keeps the GPU fed. Reducing batch size or using a smaller instance would not help. Using Pipe mode (streaming) might help but not as directly as increasing workers.

Answer

Use a smaller instance type with fewer GPUs

Answer

Decrease the number of data loading workers

Answer

Increase the batch size

Question 20

A data scientist is using Amazon SageMaker to perform hyperparameter tuning for a neural network. The tuning job uses the 'Random' search strategy. After 10 training jobs, the best objective metric has plateaued. The scientist wants to improve the results without increasing the total number of training jobs. Which approach should they take?

Accepted Answer

Switch the hyperparameter tuning strategy to 'Bayesian'. Switching to Bayesian search (e.g., 'Bayesian' strategy) is more efficient because it uses past results to choose the next hyperparameters, potentially finding better values in fewer jobs. Increasing the number of jobs would increase cost. Random search might get lucky but is less efficient. Changing the objective metric or scaling features would not directly improve the tuning process.

Answer

Use a different objective metric that is easier to optimize

Answer

Normalize the input features to have zero mean and unit variance

Answer

Increase the maximum number of training jobs