MLS-C01 Practice Test 25 — 15 Questions

Question 1

A data scientist is training a deep learning model on a GPU instance. The training loss is decreasing, but the validation loss starts increasing after a few epochs. Which action should the data scientist take to address this?

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

Accepted Answer

Implement early stopping. The scenario describes overfitting: training loss decreases while validation loss increases. Early stopping is a regularization technique that halts training when validation performance stops improving, preventing overfitting. It is the most direct and appropriate action to address this issue.

Answer

Reduce the batch size

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Increase the learning rate

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Add more layers to the model

Question 2

A company wants to monitor a deployed model for data drift. Which AWS service should they use?

Accepted Answer

Amazon SageMaker Model Monitor. Amazon SageMaker Model Monitor is the correct service because it is specifically designed to continuously monitor deployed machine learning models for data drift and quality issues. It automatically detects deviations in the input data distribution compared to a baseline, triggering alerts when drift exceeds defined thresholds, which directly addresses the company's requirement.

Answer

Amazon SageMaker Ground Truth

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Amazon CloudWatch Logs

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Amazon SageMaker Clarify

Question 3

A company is using Amazon SageMaker to deploy a model for real-time inference. The model requires 500 MB of memory and has a latency requirement of 100 ms. The endpoint is receiving 10 requests per second. Which instance type should be chosen for cost-effectiveness?

Accepted Answer

ml.m5.large. The ml.m5.large instance type provides a balance of compute, memory, and cost for this workload. It has 8 GiB of memory, which is more than enough for the 500 MB model, and sufficient vCPU to handle 10 requests per second with low latency. It is more cost-effective than the other options while meeting the requirements.

Answer

ml.c5.xlarge

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ml.t2.medium

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ml.p3.2xlarge

Question 4

A data engineer needs to set up a data lake on S3 that supports both batch and streaming ingestion. The data must be queryable by Athena, Redshift Spectrum, and EMR. Which TWO configurations are essential? (Choose two.)

Accepted Answer

Store data in columnar formats like Parquet or ORC.. Columnar formats like Parquet and ORC are optimized for analytical queries, reducing I/O by reading only the necessary columns. This is essential for Athena, Redshift Spectrum, and EMR, which all benefit from the efficient compression and predicate pushdown capabilities of these formats, enabling faster query performance and lower costs.

Answer

Enable S3 Select on the target buckets.

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Enable S3 versioning on all buckets.

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Set up Kinesis Data Firehose for streaming ingestion.

Question 5

A data scientist is using Amazon SageMaker to train a TensorFlow model on a dataset that includes sensitive personal information (PII). The data is stored in Amazon S3 with server-side encryption using AWS KMS (SSE-KMS). The training job fails with an Access Denied error when trying to read from S3. The data scientist has already verified that the SageMaker execution role has s3:GetObject permissions on the S3 bucket. What additional configuration is needed?

Accepted Answer

Add kms:Decrypt permission to the SageMaker execution role.. When S3 objects are encrypted with SSE-KMS, the SageMaker execution role must have the kms:Decrypt permission to decrypt the data during training. Even though the role has s3:GetObject access, the KMS key policy or the role's IAM policy must explicitly allow decryption of the KMS key used for server-side encryption. Without this, SageMaker cannot read the encrypted objects, resulting in an Access Denied error.

Answer

Add kms:Encrypt permission to the SageMaker execution role.

Answer

Add a bucket policy that grants s3:GetObject to the SageMaker role.

Answer

Configure a VPC endpoint for S3 and attach a policy.

Question 6

A data scientist is training a neural network using a custom loss function. The training process converges, but the model's performance on the validation set is poor. The data scientist suspects that the model is overfitting. Which action should the data scientist take to diagnose overfitting?

Accepted Answer

Plot the training and validation loss over epochs. Plotting the training and validation loss over epochs is the standard diagnostic technique for detecting overfitting. If the training loss continues to decrease while the validation loss plateaus or increases, it indicates that the model is memorizing the training data rather than generalizing. This visual comparison directly confirms overfitting, allowing the data scientist to take corrective action such as regularization or early stopping.

Answer

Add more layers to the network

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Increase the learning rate

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Compute the confusion matrix on the training set

Question 7

A data engineer is performing EDA on a time-series dataset of server metrics (CPU, memory, disk I/O) collected every minute. The dataset contains 2 years of data. The engineer suspects there are seasonal patterns and wants to decompose the time series for one metric. Which AWS service can be used to perform this decomposition natively?

Accepted Answer

Amazon SageMaker Canvas. Amazon SageMaker Canvas supports time-series forecasting with built-in decomposition. Option B is wrong because Amazon Athena with SQL window functions does not natively provide time-series decomposition. Option C is wrong because Amazon QuickSight does not decompose time series. Option D is wrong because AWS Glue DataBrew does not have decomposition functions.

Answer

Amazon Athena with SQL window functions

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Amazon QuickSight

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AWS Glue DataBrew

Question 8

A data engineer needs to move 50 TB of historical data from an on-premises Hadoop cluster to Amazon S3. The network bandwidth is limited to 100 Mbps. Which AWS service should be used to transfer the data most efficiently?

Accepted Answer

AWS Snowball Edge device to physically ship the data.. Given 50 TB of data and a 100 Mbps network link, the theoretical minimum transfer time over the network is over 46 days (50 TB * 8 / 100 Mbps ≈ 4,000,000 seconds ≈ 46.3 days), not accounting for protocol overhead, retransmissions, or contention. AWS Snowball Edge is a physical appliance that bypasses the network bottleneck entirely, allowing you to copy data locally and ship it to AWS, making it the most efficient option for this volume over a constrained link.

Answer

Amazon S3 Transfer Acceleration to speed up the transfer.

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AWS Direct Connect to establish a dedicated network connection.

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AWS Site-to-Site VPN to connect and copy data.

Question 9

Drag and drop the steps to use Amazon SageMaker Feature Store for feature engineering in the correct order.

Accepted Answer

1. Define feature group, 2. Ingest data, 3. Query features, 4. Train model, 5. Maintain feature store. Feature Store involves defining group, ingesting, querying, training, and maintaining.

Answer

1. Define feature group, 2. Query features, 3. Ingest data, 4. Train model, 5. Maintain feature store

Answer

1. Ingest data, 2. Define feature group, 3. Query features, 4. Train model, 5. Maintain feature store

Answer

1. Define feature group, 2. Ingest data, 3. Train model, 4. Query features, 5. Maintain feature store

Question 10

Drag and drop the steps to train a model using Amazon SageMaker built-in algorithm in the correct order.

Accepted Answer

Prepare training data and upload to S3 → Choose built-in algorithm → Create training job specifying algorithm, input/output S3 paths, and compute resources → Execute training job. Training involves data preparation, job creation, algorithm selection, input/output paths, and execution.

Answer

Choose built-in algorithm → Prepare training data and upload to S3 → Create training job specifying algorithm, input/output S3 paths, and compute resources → Execute training job

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Prepare training data and upload to S3 → Create training job specifying input/output S3 paths and compute resources → Choose built-in algorithm → Execute training job

Answer

Create training job specifying algorithm, input/output S3 paths, and compute resources → Prepare training data and upload to S3 → Execute training job → Choose built-in algorithm

Question 11

A data scientist is exploring a dataset stored in an Amazon S3 bucket. The dataset contains both numerical and categorical features. The scientist wants to compute summary statistics (mean, median, standard deviation) for all numerical features and count the distinct values for categorical features. Which AWS service is most appropriate for this task with minimal coding?

Accepted Answer

AWS Glue DataBrew. AWS Glue DataBrew is purpose-built for visual data profiling and transformation with minimal coding. It provides built-in profiling that automatically computes summary statistics (mean, median, standard deviation) for numerical columns and distinct value counts for categorical columns directly from S3 sources. DataBrew's profile view surfaces these statistics without writing any code, matching the 'minimal coding' requirement precisely.

Answer

Amazon Athena

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AWS Glue ETL jobs

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Amazon SageMaker Data Wrangler

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Amazon EMR

Question 12

A machine learning engineer is working on a customer churn prediction project. The dataset contains 100,000 records with 15 features, including customer demographics, account information, and usage patterns. The target variable 'churned' is binary with 15% positive examples. During EDA, the engineer notices that the feature 'tenure' (number of months the customer has been with the company) has a multimodal distribution with peaks at 1, 12, 24, and 36 months. Also, the feature 'monthly_charges' has a strong positive correlation with 'total_charges' (correlation coefficient = 0.95). The engineer wants to build a logistic regression model. Which preprocessing steps should the engineer take to address these issues? (Select TWO.)

Accepted Answer

Bin the 'tenure' feature into categorical groups (e.g., 0-6, 7-12, 13-24, 25-36, 36+) to capture the non-linear relationship.. Binning the 'tenure' feature into categorical groups (e.g., 0-6, 7-12, 13-24, 25-36, 36+) captures the multimodal distribution and non-linear relationship with churn, which logistic regression (a linear model) cannot model directly. This transforms the feature into a format that allows the model to learn different churn probabilities for each tenure segment without imposing a linear assumption. Option B is correct because 'monthly_charges' and 'total_charges' have a very high positive correlation (0.95), leading to multicollinearity. Removing one of these features (e.g., 'total_charges') reduces multicollinearity, which improves the stability and interpretability of logistic regression coefficients.

Answer

Apply log transformation to the 'tenure' feature to make it unimodal.

Answer

Create polynomial features up to degree 3 for 'tenure' to capture non-linearity.

Answer

Standardize all numerical features to have mean 0 and variance 1.

Question 13

A company uses Amazon Kinesis Data Firehose to deliver streaming data to an S3 bucket. The data is JSON and must be partitioned by year, month, and day. The delivery stream is configured with a buffer interval of 60 seconds and buffer size of 5 MB. The data producer sends about 1 MB per second. The data is arriving in S3 but the partitions are not being created as expected. What is the MOST likely reason?

Accepted Answer

The delivery stream does not have dynamic partitioning enabled with the appropriate custom prefix.. Kinesis Data Firehose requires dynamic partitioning to be explicitly enabled and configured with a custom prefix (e.g., 'year=!{timestamp:yyyy}/month=!{timestamp:MM}/day=!{timestamp:dd}/') to automatically partition data by year, month, and day. Without this setting, Firehose writes all data to a single S3 prefix, ignoring the desired partition structure.

Answer

The data is encrypted with AWS KMS and Firehose cannot write to encrypted buckets.

Answer

The buffer interval is too short for the data volume, causing incomplete records.

Answer

The S3 bucket has versioning enabled, which prevents partitioning.

Question 14

A company is building a recommendation system using matrix factorization. The dataset has 1 million users and 100,000 items. The data scientist trains a model using SageMaker's Factorization Machines algorithm. The model achieves a root mean squared error (RMSE) of 0.95 on the test set. However, the business requires RMSE below 0.90. The data scientist has already tuned hyperparameters like number of factors and learning rate. Which additional step should the data scientist take to improve RMSE?

Accepted Answer

Add side features such as user demographics and item categories. Adding side features such as user demographics and item categories allows the Factorization Machines model to capture additional interactions and context beyond user-item interactions, which can improve predictive accuracy and reduce RMSE. SageMaker's Factorization Machines algorithm supports side features, and incorporating them is a recommended step when hyperparameter tuning has plateaued.

Answer

Increase the number of training iterations

Answer

Increase the number of factors to 1000

Answer

Use a linear regression model instead

Question 15

A data scientist is performing exploratory data analysis on a dataset containing customer transactions. The dataset has a column 'transaction_date' with timestamps in string format. Which AWS service can be used to parse the timestamps and extract features like day of week and hour?

Accepted Answer

AWS Glue. AWS Glue is a serverless ETL service with built-in transforms and a Data Catalog, and it can parse string timestamps using Spark/Python date functions (e.g., to_timestamp, date_format) to extract features like day of week and hour. It is the natural choice for preparing and transforming transaction data at scale before analysis or ML.

Answer

Amazon Athena

Answer

Amazon SageMaker Studio

Answer

AWS Data Pipeline