MLA-C01 Practice Test 2 — 30 Questions

Question 1

A team is using Amazon SageMaker for feature engineering. They have a dataset with a column 'TransactionDate' in string format (e.g., '2023-01-15 10:30:00'). They need to create features: year, month, day, hour, and day_of_week. What is the most efficient way to do this in a SageMaker processing job?

Accepted Answer

Use pandas datetime functions and then split. Option A is correct because using pandas datetime functions within a SageMaker processing job is the most efficient approach for this task. SageMaker processing jobs run custom Python scripts, and pandas provides vectorized operations (e.g., `pd.to_datetime()`, `.dt.year`, `.dt.month`, `.dt.day`, `.dt.hour`, `.dt.dayofweek`) that parse the string column and extract all required features in a single pass without external dependencies or data movement.

Answer

Use SageMaker built-in first party algorithms

Answer

Use AWS Glue for transformation

Answer

Use SQL query in Athena on S3 data

Question 2

A data scientist is performing feature engineering for a dataset with both numerical and categorical features. The data scientist wants to apply transformations that preserve the interpretability of the features. Which TWO transformations should the data scientist use? (Select TWO)

Accepted Answer

Log transformation of skewed numerical features. Log transformation is correct because it reduces skewness in numerical features by compressing the scale of large values, making the distribution more normal while preserving the original feature's interpretability (e.g., a log-transformed income value still relates to income). This is a monotonic transformation, so the order of values is maintained, and the feature remains directly understandable.

Answer

Target encoding of high-cardinality categorical features

Answer

PCA dimensionality reduction

Answer

One-hot encoding of categorical features

Question 3

A data scientist needs to convert categorical variables to numerical format for a linear regression model. The dataset contains a 'Country' column with 50 unique values. Which transformation should the engineer use to avoid introducing ordinal relationships?

Accepted Answer

One-hot encoding. One-hot encoding is correct because it creates binary columns for each category, avoiding any implicit ordinal relationship between the 50 unique countries. This is essential for linear regression, which assumes numerical inputs have meaningful order; one-hot encoding ensures the model treats each country as an independent category without ranking.

Answer

Label encoding

Answer

Target encoding

Answer

Ordinal encoding

Question 4

A data engineer is using Amazon SageMaker Processing to run a data preprocessing script on a dataset with 500 million rows. The script runs out of memory on a single ml.r5.24xlarge instance. The engineer needs to modify the processing job to handle the dataset size. Which approach is most cost-effective and scalable?

Accepted Answer

Configure the Processing job with multiple instances and use ShardedByS3Key for data splitting.. Option A is correct because SageMaker Processing with ShardedByS3Key splits the input dataset by S3 object boundaries across multiple instances, allowing distributed processing of the 500 million rows without exceeding memory on any single instance. This approach is cost-effective as it uses multiple smaller instances (e.g., ml.r5.xlarge) rather than a single oversized instance, and scales linearly with data size.

Answer

Write the script to process data in chunks and write intermediate results to local ephemeral storage.

Answer

Increase the instance type to a larger one like ml.p3dn.24xlarge with more memory.

Answer

Reduce the number of instances to one and increase the volume size for swap space.

Question 5

Refer to the exhibit. A data engineer deploys this Glue job via CloudFormation. When running, the job fails with a timeout after 2 hours. The job processes a large dataset and expected to take 3 hours. Which change would resolve the issue?

Accepted Answer

Increase the Timeout property to 240 minutes. The Glue job failed due to a timeout after 2 hours, but the expected runtime is 3 hours. The default timeout for AWS Glue jobs is 2880 minutes (48 hours), but the CloudFormation template likely set a lower value. Increasing the Timeout property to 240 minutes (4 hours) provides enough time for the job to complete without being prematurely terminated.

Answer

Increase NumberOfWorkers to 20

Answer

Set MaxRetries to 3

Answer

Change WorkerType to G.2X

Question 6

A data scientist is working with a dataset containing customer demographics and purchase history. The dataset includes categorical variables with high cardinality (e.g., ZIP code, product ID). The data scientist wants to perform feature engineering to improve model performance. Which THREE feature engineering techniques should the data scientist consider? (Choose three.)

Accepted Answer

Principal Component Analysis (PCA) to reduce dimensionality of numerical features.. Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms correlated numerical features into a smaller set of uncorrelated principal components, capturing the maximum variance in the data. This is correct because the dataset includes numerical features (e.g., purchase amounts, age) where PCA can reduce noise and multicollinearity, improving model performance without losing critical information.

Answer

Domain-specific feature engineering based on business rules.

Answer

One-hot encoding all categorical features.

Question 7

A dataset for binary classification has a severe class imbalance (5% positive class). Which two data preparation techniques can help address this imbalance? (Choose two.)

Accepted Answer

Undersample the majority class. Option D is correct because undersampling the majority class reduces the number of instances from the dominant class, helping to balance the dataset and prevent the model from being biased toward the majority class. This technique is straightforward and can be effective when the majority class has redundant or noisy samples, though it risks losing valuable information.

Answer

Remove outliers from the minority class

Answer

Apply PCA to reduce dimensionality

Answer

Use stratified splitting for train/test sets

Question 8

A data scientist is preprocessing time series data for a fraud detection model. The data includes transaction timestamps, amounts, and merchant IDs. The model should predict fraud within seconds of a transaction. The data scientist wants to avoid data leakage by not using future information to predict past events. Which data preparation practice should be implemented?

Accepted Answer

Compute features like lagged transaction amounts and rolling statistics based only on each transaction's past data up to that point.. Option A is correct because it ensures that features are computed using only historical data available up to each transaction's timestamp, preventing any future information from leaking into the model. In time series fraud detection, using only past data for lagged amounts and rolling statistics respects the temporal order and avoids the model learning patterns that would not be available at prediction time.

Answer

Randomly shuffle the dataset before splitting into training and validation sets.

Answer

Generate features such as rolling averages and lag features using a sliding window of all available data.

Answer

Normalize the features using MinMaxScaler on the entire dataset before splitting into training and testing.

Question 9

A data scientist is evaluating data quality for a machine learning project. The dataset has missing values, outliers, and inconsistent formatting. Which TWO steps should the data scientist perform during the data preparation phase? (Choose 2.)

Accepted Answer

Standardize numeric features. Standardizing numeric features (Option C) is a critical data preparation step because it rescales features to have zero mean and unit variance, which prevents features with larger magnitudes from dominating distance-based algorithms like k-nearest neighbors or gradient descent optimization. This transformation is essential for many machine learning models to converge faster and perform correctly.

Answer

Normalize text data to lowercase

Answer

Remove all outliers blindly

Answer

Use a large neural network to handle all transformations

Question 10

A company is preparing a large dataset for a SageMaker built-in XGBoost model. The dataset has missing values in both numeric and categorical features, and some categorical features have high cardinality. Which THREE data preparation steps should the company take to optimize model performance? (Choose three.)

Accepted Answer

Split the data into training, validation, and test sets before any imputation.. Option B is correct because splitting the data into training, validation, and test sets before any imputation prevents data leakage. If imputation statistics (e.g., mean, median) were computed on the full dataset, information from the validation and test sets would influence the training data, leading to overly optimistic performance estimates and poor generalization to new data.

Answer

Remove any rows with outlier values.

Answer

Apply target encoding to all categorical features regardless of cardinality.

Question 11

A data engineer is optimizing Amazon Athena queries on large datasets stored in S3 for machine learning data preparation. Which THREE practices improve query performance?

Accepted Answer

Partition the data by a frequently filtered column, such as date. Partitioning by a frequently filtered column, such as date, allows Athena to use partition pruning. When a query includes a filter on the partition column, Athena can skip entire directories of data in S3, drastically reducing the amount of data scanned and improving query performance while also lowering cost.

Answer

Use uncompressed CSV files for simplicity

Answer

Partition the data by every column to maximize filtering

Question 12

A company is using an AWS Step Functions state machine to orchestrate a multi-step ML deployment. The workflow includes: training a model, evaluating it, registering the model, and deploying to a staging endpoint. They need to implement an approval gate before deploying to production. Which THREE components are necessary to achieve this? (Choose three.)

Accepted Answer

A task in the state machine that pauses and waits for manual approval via SNS or Lambda. Option B is correct because Step Functions can use a task with a callback pattern (`.waitForTaskToken`) to pause the workflow and wait for external manual approval. When combined with an SNS topic or Lambda function that sends a task success or failure signal back to Step Functions, this creates a reliable approval gate. This pattern allows the state machine to halt execution until a human approves or rejects the deployment, which is essential for production deployment control.

Answer

An AWS CodePipeline pipeline with approval stage

Answer

An Amazon SNS topic for notification of approval status

Question 13

A company has a model that receives low traffic but needs to handle sudden spikes. Which deployment option is most cost-effective?

Accepted Answer

SageMaker Serverless Inference. SageMaker Serverless Inference is the most cost-effective option for low-traffic models with sudden spikes because it automatically scales to zero when not in use and scales up instantly to handle bursts, charging only for the compute time consumed per inference request. This eliminates the cost of idle provisioned infrastructure, making it ideal for unpredictable or intermittent traffic patterns.

Answer

SageMaker Real-Time Endpoint with Auto Scaling

Answer

SageMaker Multi-Model Endpoint

Answer

SageMaker Batch Transform

Question 14

A company is deploying multiple models on a single endpoint to reduce costs. They need to update one model without affecting others. Which solution?

Accepted Answer

Use SageMaker Multi-Model Endpoint. SageMaker Multi-Model Endpoint (MME) allows hosting multiple models on a single endpoint, sharing the underlying compute instance. When you need to update one model, you can simply upload a new model artifact (e.g., a new `model.tar.gz`) to Amazon S3, and the endpoint will automatically load the updated version on subsequent inference requests without affecting the other models currently cached or in use.

Answer

Use multiple single-model endpoints behind an Application Load Balancer

Answer

Use SageMaker Batch Transform for some models

Answer

Use a SageMaker Endpoint with multiple production variants

Question 15

A team has a large number of models that need to be deployed for batch inference weekly. They want to minimize cost and management overhead. Which approach is MOST efficient?

Accepted Answer

Use SageMaker Batch Transform with separate jobs for each model.. SageMaker Batch Transform is the most efficient approach for weekly batch inference because it automatically provisions and terminates compute resources for each job, minimizing cost and management overhead. Running separate jobs for each model allows independent scaling and avoids the complexity of managing persistent endpoints or multi-model hosting for batch workloads.

Answer

Use SageMaker Pipelines to run inference as part of the pipeline.

Answer

Create a single SageMaker endpoint for all models and update the model periodically.

Answer

Deploy each model to a separate SageMaker endpoint and delete after use.

Question 16

Refer to the exhibit. A company configures a SageMaker Model Monitor Data Quality monitoring schedule as shown. The schedule runs every hour. However, the team notices that the monitoring job fails intermittently with an AccessDenied error when accessing the S3 bucket for output. The IAM role SageMakerMonitorRole has permissions to write to s3://my-bucket/monitor-output. What is the MOST likely cause of the failure?

Accepted Answer

The monitoring job runs in a VPC that does not have an S3 VPC endpoint, and the bucket policy denies requests from outside the VPC.. The intermittent AccessDenied error when SageMaker Model Monitor attempts to write to the S3 output bucket strongly indicates a network or policy restriction. If the monitoring job is configured to run inside a VPC (common for security compliance) and that VPC lacks an S3 VPC endpoint, traffic to S3 traverses the public internet. If the S3 bucket policy explicitly denies requests from outside the VPC (using a condition like `aws:SourceVpce` or `aws:SourceVpc`), then jobs running inside the VPC without an endpoint will be denied access intermittently, especially if the job's execution role is assumed from within the VPC.

Answer

The S3UploadMode is set to Continuous, which is only supported for batch transform jobs.

Answer

The cron expression is invalid; it should use rate(1 hour) instead.

Answer

The baseline constraints and statistics files are missing from the S3 bucket.

Question 17

Refer to the exhibit. A user is unable to invoke a SageMaker endpoint. The IAM policy shown is attached to the user. Which permission is missing to allow invocation?

Accepted Answer

sagemaker:InvokeEndpoint. To invoke a SageMaker endpoint, the user needs the `sagemaker:InvokeEndpoint` permission. The IAM policy shown lacks this action, which is required for making real-time inference requests to the endpoint. Without it, any attempt to call the endpoint via the SDK or CLI will fail with an access denied error.

Answer

sagemaker:DescribeEndpoint

Answer

sagemaker:CreateEndpoint

Answer

sagemaker:ListEndpoints

Question 18

Refer to the exhibit. A SageMaker Pipeline fails with 'Invalid output reference' at the TrainingStep. What is the most likely cause?

Accepted Answer

The TrainingStep expects a single artifact but TuningStep produces multiple. Option C is correct because in SageMaker Pipelines, a `TrainingStep` that expects a single artifact as input will fail with 'Invalid output reference' if the preceding `TuningStep` produces multiple artifacts (e.g., from multiple training jobs). The pipeline cannot resolve which specific artifact to pass, causing the error.

Answer

The TuningStep output name is misspelled

Answer

The pipeline role lacks permissions

Answer

The instance type is incompatible

Question 19

A data scientist runs this pipeline but the Train step fails with "ResourceLimitExceeded". What is the most likely cause?

Accepted Answer

The account has a limit of 0 for ml.p3.2xlarge instances.. The 'ResourceLimitExceeded' error indicates that the requested instance type (ml.p3.2xlarge) exceeds the account's service quota for that specific instance family. In AWS SageMaker, each account has a default limit of 0 for certain GPU instance types like ml.p3.2xlarge unless a quota increase has been requested and approved. This error occurs at the Train step because SageMaker attempts to launch the training job with an instance type that is not allowed by the current quota.

Answer

The volume size is too small for training.

Answer

The Preprocess step did not complete successfully.

Answer

The training image is not accessible.

Question 20

A company deploys a model on Amazon SageMaker for real-time inference. The inference latency is too high. The model is a large deep learning model. The company wants to reduce latency without significantly impacting accuracy. Which approach should the company consider?

Accepted Answer

Use SageMaker Inference Recommender to test different instance types and optimizations.. SageMaker Inference Recommender is designed specifically to automate load testing and benchmarking across various instance types and model optimizations (e.g., Elastic Inference, GPU acceleration, serialization formats). It provides latency and throughput metrics to identify the optimal configuration for reducing inference latency while maintaining accuracy, making it the correct choice for a large deep learning model with high latency.

Answer

Increase the batch size for inference.

Answer

Use a smaller instance type to reduce inference time.

Answer

Enable SageMaker Model Monitor to detect performance issues.