MLS-C01 Practice Exam 69 — 20 Questions

Question 1

A team is building a data pipeline using Amazon Kinesis Data Firehose to deliver real-time clickstream data to an Amazon S3 bucket. The data must be partitioned by year, month, day, and hour. Which configuration should the team use to achieve this?

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

Accepted Answer

Enable dynamic partitioning in Firehose and configure the partition keys as YYYY/MM/dd/HH. Amazon Kinesis Data Firehose supports dynamic partitioning, which allows you to automatically partition incoming data in S3 based on keys like YYYY/MM/dd/HH. By enabling this feature and configuring the partition keys to match the desired year, month, day, and hour format, Firehose will write data directly into the corresponding S3 prefix structure without requiring additional processing.

Answer

Configure an S3 lifecycle rule to move data into partition folders after delivery

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Use an AWS Lambda function to write data to S3 with the desired partition structure

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Use Amazon Athena partition projection to dynamically create partitions

Question 2

A data scientist is performing EDA on a dataset with 500 features. The dataset has a mix of numeric and categorical features. The scientist wants to identify which features have a strong nonlinear relationship with the target variable. Which technique is most appropriate?

Accepted Answer

Calculate mutual information between each feature and the target.. Mutual information (MI) measures the dependency between two variables and captures both linear and nonlinear relationships, making it ideal for detecting strong nonlinear associations between features and a target. Unlike correlation, MI does not assume a linear form and works with mixed data types when discretized appropriately. This makes it the most appropriate technique for the stated goal.

Answer

Use ANOVA to compare feature means across target classes.

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Compute Pearson correlation coefficients.

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Perform chi-squared tests for each feature.

Question 3

A company uses SageMaker to train a model. The training job fails with 'ResourceLimitExceeded' error. Which TWO actions should the company take to resolve this?

Accepted Answer

Use a different instance type that is not at its limit.. The 'ResourceLimitExceeded' error indicates that the requested instance type has reached its concurrent usage limit in the current AWS region. Switching to a different instance type that is not at its limit allows the training job to proceed without exceeding the service quota. Option E is correct because requesting a service limit increase for SageMaker training job resources directly raises the cap on the number of concurrent instances or total instance count, resolving the underlying quota issue.

Answer

Launch the training job in a different AWS region.

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Use SageMaker Managed Spot Training to reduce cost.

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Compress the training data to reduce storage requirements.

Question 4

A data scientist is analyzing a dataset with a binary target variable. They compute the correlation matrix and find that all features have correlations between -0.1 and 0.1 with the target. They suspect that the relationship might be non-linear. Which of the following techniques should they use to detect non-linear relationships?

Accepted Answer

Mutual information. Mutual information (MI) measures the mutual dependence between two variables and can capture any kind of relationship, including non-linear ones. Unlike correlation coefficients, which only detect linear relationships, MI is based on entropy and quantifies how much knowing one variable reduces uncertainty about the other. Since the features have low linear correlation with the target, MI is the appropriate technique to detect non-linear dependencies.

Answer

ANOVA test

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Spearman's rank correlation

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Pearson correlation coefficient

Question 5

An ML engineer needs to store and version training datasets and model artifacts. Which AWS service should they use?

Accepted Answer

Amazon Simple Storage Service (S3). Amazon S3 is the correct choice because it provides scalable, durable, and cost-effective object storage with built-in versioning capabilities, making it ideal for storing and versioning large training datasets and model artifacts. S3's versioning feature allows you to preserve, retrieve, and restore every version of an object, which is essential for reproducibility in ML workflows.

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Amazon DynamoDB

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Amazon Elastic File System (EFS)

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Amazon Elastic Block Store (EBS)

Question 6

A company captures streaming data from IoT devices using Amazon Kinesis Data Streams. The data is consumed by a custom application that processes records in near real-time. Recently, the application has been falling behind, and the stream is showing increased 'iterator age' metrics in CloudWatch. Which action is MOST likely to reduce the iterator age?

Accepted Answer

Increase the number of shards in the stream. The 'iterator age' metric in Amazon Kinesis Data Streams measures the time between the oldest unread record in a shard and the current time. An increasing iterator age indicates that consumers are reading data slower than it is being produced. Increasing the number of shards increases the stream's total read capacity, allowing the custom application to process records in parallel and reduce the backlog.

Answer

Increase the data retention period of the stream

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Decrease the number of shards in the stream

Answer

Reduce the data retention period of the stream

Question 7

A data scientist is performing hyperparameter tuning using Amazon SageMaker Automatic Model Tuning (AMT). The job uses a random search strategy. After 20 training jobs, the best objective metric value has plateaued. The data scientist wants to explore more of the hyperparameter space. Which action should the data scientist take?

Accepted Answer

Change the tuning strategy from Random to Bayesian.. Changing the tuning strategy from Random to Bayesian allows the tuning job to use previous results to guide the search toward more promising hyperparameter regions, which can explore the space more efficiently after plateauing. Option B is incorrect because enabling early stopping terminates underperforming trials early but does not alter the search strategy itself; it may even reduce exploration. Option C is incorrect because decreasing the maximum number of training jobs reduces the total exploration of the hyperparameter space. Option D is incorrect because increasing the number of parallel training jobs only speeds up the process but still uses the same random search strategy, which is unlikely to escape the plateau.

Answer

Enable early stopping.

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Decrease the maximum number of training jobs.

Answer

Increase the number of parallel training jobs.

Question 8

Which TWO of the following are common techniques to handle missing values in a dataset?

Accepted Answer

Remove rows with missing values. Option D (Remove rows with missing values) is correct because listwise deletion, also called complete-case analysis, is a standard and simple technique that drops any record containing a missing value, which is appropriate when missingness is random and the affected rows are few. Option E (Imputation with mean or median) is correct because replacing missing entries with the column's mean (for roughly symmetric numeric data) or median (for skewed data or robustness to outliers) is a widely used single-imputation method that preserves sample size. The other options do not address missing data: A, standardization, rescales features to zero mean and unit variance; B, PCA, is a dimensionality-reduction technique that projects data onto principal components; and C, one-hot encoding, converts categorical variables into binary indicator columns. None of A, B, or C fills in or removes missing values, so they are not missing-value handling techniques.

Answer

Standardization

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Principal Component Analysis (PCA)

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One-hot encoding

Question 9

A data analyst is using Amazon Athena to query a partitioned dataset in S3. They notice that queries are scanning more data than expected. Which step should they take during exploratory data analysis to optimize query performance?

Accepted Answer

Check the partition metadata to ensure queries are pruning partitions.. Athena uses Hive-style partition metadata from the AWS Glue Data Catalog to prune partitions at query planning time. If partitions are missing, misregistered, or use a type/location mismatch, Athena falls back to scanning the entire table prefix in S3, inflating bytes scanned. Verifying partition metadata (via SHOW PARTITIONS, MSCK REPAIR TABLE, or checking Glue Catalog entries) confirms pruning is actually happening before optimizing further.

Answer

Convert the data to Parquet format.

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Use S3 Select to filter data before querying.

Answer

Increase the number of workers in Athena.

Question 10

A company is using Amazon SageMaker to train a machine learning model. The training job is configured to use the File mode to download data from S3 to the training instances. The training data is stored in a single S3 bucket with multiple prefixes. Which TWO actions are required to ensure the training job can access the data? (Choose TWO.)

Accepted Answer

Grant the SageMaker execution role s3:GetObject permission for the data bucket.. Option A is correct because SageMaker training jobs access S3 using the execution role attached to the job, so that role must have s3:GetObject (and typically s3:ListBucket) permissions on the data bucket for File mode to download the objects. Option C is correct because each training input channel must point to the specific S3 prefix (or object) containing the data; with multiple prefixes, you define the appropriate S3 URI per channel so SageMaker knows what to download. Option B is incorrect because Pipe mode is an alternative streaming input mode, not a requirement for File mode access. Option D is incorrect because File mode downloads individual objects and does not require concatenating files into one. Option E is incorrect because RecordIO-protobuf is an optional data format for certain algorithms, not a prerequisite for S3 access.

Answer

Configure the training job to use Pipe mode.

Answer

Concatenate all data files into a single file.

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Convert the data to RecordIO-protobuf format.

Question 11

In exploratory data analysis, a data scientist notices that the distribution of a continuous variable is bimodal. The scientist suspects that the two modes correspond to two different groups in the data. Which visualization is MOST appropriate to confirm this suspicion?

Accepted Answer

Histogram with overlaid densities by group. The most appropriate because a histogram with overlaid densities, colored by group, directly shows the distribution of each group and can reveal whether the two modes correspond to different groups. Option A (box plot) displays summary statistics but not the shape or modality. Option B (bar chart) is for categorical data, not continuous. Option D (scatter plot) is for two continuous variables, not for examining a single distribution.

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Box plot

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Bar chart

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Scatter plot

Question 12

A data analyst is performing EDA on a dataset containing timestamps of user logins. They want to understand daily login patterns. The timestamp column is in Unix epoch format (integer). Which of the following is the most appropriate transformation to extract day-of-week patterns?

Accepted Answer

Convert the timestamps to datetime objects and extract the day-of-week.. Converting Unix epoch timestamps to datetime objects allows extraction of the day-of-week using functions like .dt.dayofweek() in pandas. Option B is wrong because converting to string and splitting into date and time does not directly give day-of-week patterns and loses temporal properties. Option C is wrong because min-max scaling is used for normalizing numerical features, not for extracting temporal patterns. Option D is wrong because binning into 1-hour intervals captures hourly patterns, not day-of-week patterns.

Answer

Convert the timestamps to string and split into date and time.

Answer

Apply min-max scaling to the timestamp values.

Answer

Bin the timestamps into 1-hour intervals.

Question 13

A data engineering team needs to process streaming data from thousands of IoT devices. The data must be ingested with low latency and processed in near real-time to detect anomalies. Which AWS service should they use for ingestion?

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is the correct choice because it is designed for real-time, low-latency ingestion of streaming data from thousands of sources, such as IoT devices. It provides a durable, scalable data stream that can be consumed by multiple applications in near real-time, making it ideal for anomaly detection use cases.

Answer

Amazon Kinesis Data Firehose

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Amazon Kinesis Data Analytics

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Amazon S3

Question 14

A company uses Amazon EMR to run Spark jobs on a transient cluster that processes data from S3. The jobs are failing with 'OutOfMemory' errors. The data engineer has already increased the executor memory. Which additional configuration change would MOST likely resolve the issue?

Accepted Answer

Increase the number of partitions in the data. The 'OutOfMemory' errors in Spark on EMR typically occur when individual partitions hold too much data for the executor's memory to process. Increasing the number of partitions distributes the data more evenly across available memory, reducing the per-partition size and preventing memory overflow during shuffle or aggregation operations. This directly addresses the root cause of memory pressure, whereas simply increasing executor memory may only delay the failure.

Answer

Use fewer, larger instance types for the core nodes

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Increase the driver memory

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Increase the number of executors

Question 15

A data science team is deploying a machine learning model to production using Amazon SageMaker. The model requires real-time inference with low latency. Which SageMaker feature should they use to deploy the model?

Accepted Answer

SageMaker Realtime Endpoint. SageMaker Realtime Endpoints are designed for low-latency, synchronous inference, making them the correct choice for serving predictions in real time. They keep the model loaded and ready to respond to individual requests, which is essential for applications requiring immediate responses.

Answer

SageMaker Notebook Instance

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SageMaker Batch Transform

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SageMaker Autopilot

Question 16

A training job log shows this error. The training instance is an ml.m5.large with 8 GB EBS storage. The training data is 500 MB, and the model size is expected to be 200 MB. What is the most likely cause?

Accepted Answer

The training process is generating large temporary files that fill the instance's local storage. The training job runs on an ml.m5.large instance with only 8 GB of EBS storage. Given that the training data is 500 MB and the model size is 200 MB, there is ample space for those, but the error indicates a lack of storage space. This is most likely caused by temporary files (e.g., checkpoints, logs, or intermediate data) generated during training that fill up the remaining storage. Option A is incorrect because a download issue would show errors like 'Download failed' or 'NoSuchKey', not a storage error. Option B is incorrect because S3 permission issues would result in 'AccessDenied' errors. Option C is incorrect because insufficient RAM would cause a 'MemoryError' or out-of-memory kill, not a storage space error.

Answer

The training data is not fully downloaded from S3 before processing

Answer

The S3 bucket does not have write permissions

Answer

The training instance does not have enough RAM

Question 17

A data scientist is using Amazon SageMaker to train a model using the built-in XGBoost algorithm. The training job uses a hyperparameter tuning job to optimize hyperparameters. The tuning job has been running for 3 hours and has completed 20 training jobs. The data scientist wants to stop the tuning job early if it is not making progress. What should the data scientist do to accomplish this?

Accepted Answer

Configure the tuning job with early stopping enabled.. SageMaker's automatic model tuning supports early stopping. When enabled, the tuning job stops automatically if no significant improvement is observed. Option B is incorrect because CloudWatch alarms can monitor metrics but cannot directly stop a tuning job; they can trigger actions like notifications but not stop the tuning. Option C is incorrect because SageMaker Experiments is for tracking experiments, not for stopping tuning jobs. Option D is incorrect because SageMaker Debugger stops individual training jobs, not the hyperparameter tuning job itself; early stopping is a built-in feature of the tuning job.

Answer

Set up a CloudWatch alarm to stop the tuning job if a metric does not improve.

Answer

Use SageMaker Experiments to monitor and manually stop the tuning job.

Answer

Use SageMaker Debugger to stop training jobs that are not improving.

Question 18

A data scientist is building a regression model to predict house prices. The dataset includes features such as square footage, number of bedrooms, and location. After training a linear regression model, the scientist notices that the residuals have a pattern: they increase as the predicted value increases. Which action is most appropriate?

Accepted Answer

Apply a log transformation to the target variable. A residual pattern that fans out (increases) as predicted values increase indicates heteroscedasticity and a right-skewed target, which violates linear regression's constant-variance and normality assumptions. Applying a log transformation to the target compresses large values, stabilizes variance, and often linearizes the relationship, directly addressing the pattern. This is the standard remedy for multiplicative or skewed regression targets like house prices.

Answer

Remove outliers from the dataset

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Use Ridge regression instead of linear regression

Answer

Add polynomial features to the model

Question 19

A data scientist is working on a project to predict customer churn. The dataset contains 50,000 rows and 20 features, including categorical variables like 'Region' (10 categories) and 'SubscriptionType' (5 categories). The target variable is binary (churn or not). During exploratory data analysis, they plot the distribution of each feature and notice that 'Region' has a highly imbalanced distribution: one region accounts for 80% of the data. Which of the following is the most appropriate next step?

Accepted Answer

Group rare categories into an 'Other' category.. Grouping rare categories into an 'Other' category is the most appropriate next step because it reduces the cardinality of the 'Region' feature while preserving the information that a region is rare. This helps prevent overfitting and improves model generalization, especially when one category dominates. One-hot encoding with 10 categories would create 10 sparse binary features, and the rare categories would have very few samples, leading to unstable estimates.

Answer

Apply one-hot encoding to the 'Region' feature.

Answer

Remove the 'Region' feature from the dataset.

Answer

Oversample the minority classes in the target variable.

Question 20

A company uses SageMaker to host a real-time inference endpoint for a classification model. The endpoint receives traffic spikes that cause high latency. The team wants a solution that automatically scales based on demand while keeping costs low. Which approach is BEST?

Accepted Answer

Enable automatic scaling on the endpoint using Application Auto Scaling. Application Auto Scaling for SageMaker endpoints automatically adjusts the number of instances provisioned for a model in response to changes in workload, such as traffic spikes. This enables the endpoint to scale out during high demand and scale in during low demand, maintaining low latency while minimizing costs. It is the native AWS solution for dynamic scaling of SageMaker real-time inference endpoints.

Answer

Use provisioned concurrency for the endpoint

Answer

Use a multi-model endpoint to serve multiple models

Answer

Deploy the endpoint on Spot Instances