MLA-C01 Practice Exam 53 — 20 Questions

Question 1

A machine learning engineer observes that model performance on a SageMaker endpoint has degraded over the past week. Ground truth labels are available with a 2-day delay. The engineer wants to automatically trigger a retraining pipeline when prediction quality drops below an acceptable threshold. Which approach is most appropriate?

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

Accepted Answer

Use SageMaker Model Monitor - Model Quality Monitor with ground truth, create a CloudWatch alarm on the metric, and trigger an AWS Lambda function to start retraining. SageMaker Model Monitor's Model Quality Monitor is specifically designed to compare model predictions against ground truth labels (available with a 2-day delay) and track metrics like accuracy, precision, recall, or F1 score. You can configure a CloudWatch alarm on a metric such as 'accuracy' dropping below a threshold, which triggers an AWS Lambda function to start the retraining pipeline. This automates the detection of prediction quality degradation and the retraining response without manual intervention.

Answer

Manually evaluate the model weekly and retrain as needed

Answer

Use SageMaker Model Monitor - Data Quality Monitor to detect drift, then trigger retraining

Answer

Use SageMaker Clarify to monitor bias drift and trigger retraining

Question 2

A company is building a sentiment analysis model for customer reviews. The text data contains many typos, abbreviations, and informal language. Which text preprocessing step would be most beneficial to support the ML model?

Accepted Answer

Apply spell-check and normalize abbreviations. Spell-checking and normalization (e.g., converting 'u' to 'you') reduces vocabulary size and helps the model generalize. Stop-word removal is common but less critical. Stemming/lowercasing are already standard. Tokenization is necessary but not the most beneficial for this specific issue.

Answer

Apply stemming and lowercasing only

Answer

Remove all stop words

Answer

Tokenize using whitespace tokenizer

Question 3

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

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PCA dimensionality reduction

Answer

One-hot encoding of categorical features

Question 4

A team is preparing text data for a natural language processing (NLP) model. They have a corpus of customer reviews. Which THREE preprocessing steps are essential to reduce noise and improve model performance?

Accepted Answer

Remove punctuation and special characters. Punctuation and special characters (e.g., commas, exclamation marks) introduce irrelevant noise that does not carry semantic meaning for most NLP models. Removing them reduces vocabulary size and prevents the model from treating 'hello!' and 'hello' as distinct tokens, which improves generalization and reduces overfitting.

Answer

Apply one-hot encoding to each word

Answer

Compute TF-IDF vectors

Question 5

A machine learning team is building a feature store using Amazon SageMaker Feature Store. They need to store features that support both real-time inference (low latency) and historical training. Which configuration should they choose?

Accepted Answer

Create a feature group with both online and offline stores enabled. SageMaker Feature Store supports both online and offline stores. The online store (backed by DynamoDB) provides low-latency access for real-time inference, while the offline store (S3) stores historical data for training. Enabling both satisfies the requirement.

Answer

Create two separate feature groups: one online and one offline

Answer

Create a feature group with only an online store enabled

Answer

Create a feature group with only an offline store enabled

Question 6

A data engineer needs to convert a JSON dataset to Parquet format for efficient querying with Amazon Athena. The JSON files are in an S3 bucket. Which service can perform this conversion with minimal coding?

Accepted Answer

AWS Glue Studio with a visual job. AWS Glue Studio with a visual job is the correct choice because it provides a no-code, drag-and-drop interface to create ETL jobs that can read JSON from S3 and write it as Parquet, with built-in schema inference and transformation capabilities. This minimizes coding effort while leveraging Glue's serverless Spark engine for efficient conversion, making it ideal for preparing data for Athena queries.

Answer

Amazon SageMaker Processing

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Amazon EMR

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AWS Lambda

Question 7

A data scientist is building a time-series forecasting model for daily sales data. The data spans two years. To evaluate the model's performance, the data scientist needs to simulate a realistic rolling forecast scenario. Which data splitting strategy should be used?

Accepted Answer

Walk-forward validation. Walk-forward validation (also known as time-series cross-validation) trains on an expanding window of past data and evaluates on the next time step, preserving temporal order. Standard k-fold or stratified splits would shuffle data and leak future information.

Answer

Random 80/20 train-test split

Answer

Stratified k-fold cross-validation

Answer

Hold-out split based on time (e.g., train on first 18 months, test on last 6 months)

Question 8

A machine learning team is preparing a dataset for a text classification model. The dataset contains customer reviews. The team wants to convert the text into numerical features while reducing the impact of common words and capturing word importance. Which THREE techniques should the team apply? (Select THREE.)

Accepted Answer

Tokenization. Tokenization, stop-word removal, and TF-IDF are standard preprocessing steps for text classification. Word embeddings are an alternative to TF-IDF but not a combination; one-hot encoding is not suitable for text.

Answer

Word embeddings

Answer

One-hot encoding

Question 9

Which TWO actions are recommended best practices when preparing training data for a machine learning model in AWS? (Choose two.)

Accepted Answer

Check for and handle missing values appropriately.. Missing values can introduce bias or cause algorithms to fail, so handling them (e.g., via imputation or removal) is a critical data preparation step in AWS SageMaker. Option D is correct because splitting data into training, validation, and test sets allows you to evaluate model performance on unseen data and prevent overfitting, which is a standard practice in SageMaker's built-in algorithms and training jobs.

Answer

Remove all outliers from the dataset.

Answer

Train the model on the entire dataset to maximize data usage.

Answer

Always normalize all features to a [0,1] range.

Question 10

A team is using SageMaker Pipelines to automate retraining and deployment. They want to trigger the pipeline automatically when new training data is available in an S3 bucket. Which approach should they use?

Accepted Answer

Create an Amazon EventBridge rule that triggers the pipeline execution on S3 PutObject events. Amazon EventBridge can directly capture S3 PutObject events and invoke a SageMaker Pipeline execution as a target. This provides a fully event-driven, serverless integration without polling or manual intervention, aligning with best practices for automating ML workflows when new data arrives.

Answer

Register the pipeline as a model package in SageMaker Model Registry

Answer

Configure a cron job to run the pipeline every hour

Answer

Use AWS Step Functions to poll the S3 bucket and start the pipeline when a new object appears

Question 11

A data scientist is preparing a dataset for a regression model that predicts house prices. The dataset includes a `neighborhood` feature with 500 distinct categories. The data scientist wants to encode this feature without increasing dimensionality too much and while capturing the target relationship. Which encoding technique should be used?

Accepted Answer

Target encoding (mean encoding). Target encoding (mean encoding) is the correct choice because it replaces each of the 500 neighborhood categories with the mean of the target variable (house price) for that category. This captures the relationship between the neighborhood and the target while adding only one new feature column, thus avoiding the massive dimensionality explosion that would occur with one-hot encoding (which would create 500 binary columns).

Answer

One-hot encoding

Answer

Frequency encoding

Answer

Label encoding

Question 12

Refer to the exhibit. A user has the above IAM policy attached but cannot access files in SageMaker Studio. What additional permission is most likely needed?

Accepted Answer

s3:GetObject on the relevant S3 buckets. The IAM policy shown likely grants permissions for SageMaker API actions (e.g., CreateApp, DescribeDomain) but does not include S3 data plane permissions. SageMaker Studio notebooks and experiments read training data, scripts, and model artifacts from S3 buckets. Without s3:GetObject on the relevant S3 buckets, the user can launch the Studio environment but cannot load or save any files, resulting in access failures.

Answer

sagemaker:ListApps

Answer

sagemaker:DescribeUserProfile

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kms:Decrypt

Question 13

A data scientist is working on a time series forecasting problem. The dataset contains a column 'sales' with occasional negative values due to returns. The model expects non-negative input. Which data preparation step should be taken?

Accepted Answer

Clip negative sales values to zero. Clipping negative sales values to zero directly addresses the model's requirement for non-negative input while preserving the data's temporal structure. This approach is appropriate for time series forecasting where returns cause occasional negative values, as it treats returns as zero sales rather than removing or distorting the data points.

Answer

Apply log transformation after adding a constant

Answer

Remove all rows with negative sales values

Answer

Impute negative values with the mean

Question 14

A company uses Amazon SageMaker Data Wrangler to create a data flow for a classification model. The dataset contains a high-cardinality categorical feature 'product_id' with 50,000 unique values. The data scientist wants to reduce dimensionality while preserving predictive power. Which approach is most effective?

Accepted Answer

Perform target encoding by replacing each product ID with the average target value for that product.. Target encoding is the most effective approach for high-cardinality categorical features because it replaces each category with the mean of the target variable, preserving predictive signal while drastically reducing dimensionality. In SageMaker Data Wrangler, this can be implemented using the 'Encode categorical' transform with the 'Target encoding' option, which avoids the explosion of features caused by one-hot encoding and retains the relationship between product IDs and the target.

Answer

Apply one-hot encoding to the 'product_id' column.

Answer

Use feature hashing to map product IDs to a fixed number of buckets (e.g., 100).

Answer

Drop the 'product_id' column entirely.

Question 15

A machine learning engineer runs a SageMaker HyperparameterTuningJob with Bayesian optimization strategy. The job terminates earlier than the specified MaxNumberOfTrainingJobs. The engineer notices that the best objective metric value has not improved for several consecutive jobs. What is the most likely adjustment to make?

Accepted Answer

Adjust the early stopping tolerance (e.g., increase the number of consecutive jobs with no improvement allowed).. The Bayesian optimization strategy in SageMaker HyperparameterTuningJob uses early stopping to halt the tuning job when the objective metric has not improved for a specified number of consecutive training jobs. The engineer observed that the job terminated earlier than MaxNumberOfTrainingJobs because the default early stopping tolerance was reached. Increasing the early stopping tolerance (e.g., raising the number of consecutive jobs with no improvement allowed) gives the Bayesian optimizer more chances to explore and potentially find a better configuration before stopping.

Answer

Switch to a grid search strategy to cover all hyperparameter combinations.

Answer

Increase the MaxNumberOfTrainingJobs parameter to allow more exploration.

Answer

Decrease the number of hyperparameters being tuned.

Question 16

An MLOps team is designing a CI/CD pipeline for deploying machine learning models to production on Amazon SageMaker. They want to ensure that the deployment process is automated and that models are automatically rolled back if performance degrades. Which of the following AWS services or features should they use to achieve this? (Choose THREE.)

Accepted Answer

Amazon SageMaker Model Registry. Amazon SageMaker Model Registry is correct because it provides a centralized catalog for managing, versioning, and approving ML models. It enables automated deployment by triggering CI/CD pipelines when a model version is approved, and supports automatic rollback by allowing you to revert to a previous approved version if performance degrades, as detected by monitoring metrics.

Answer

Amazon SageMaker Ground Truth

Answer

AWS CloudTrail

Question 17

A healthcare company is developing a predictive model to identify patients at risk of readmission within 30 days after discharge. The dataset contains electronic health record (EHR) data from multiple hospitals, stored as Parquet files in Amazon S3. The data includes patient demographics, diagnoses (ICD-10 codes), medications, lab results, and length of stay. A data scientist notices that the 'lab_result' column has a high number of null values (over 60%) because some tests are not applicable to all patients. Additionally, the 'diagnosis_code' column has over 10,000 unique ICD-10 codes. The company wants to build a model that complies with HIPAA and performs well. The data scientist must prepare the features efficiently using AWS services. Which combination of steps should the data scientist take? (Assume the company can use any AWS service.)

Accepted Answer

Use AWS Glue ETL to impute missing lab results with a value predicted from other features using a model like XGBoost, and apply count encoding to diagnosis codes based on their frequency of occurrence.. It uses AWS Glue ETL to impute missing lab results with a predictive model (XGBoost), which is appropriate for high missingness (>60%) where simple imputation would bias the model, and applies count encoding to the high-cardinality diagnosis codes (10,000+ unique values) to avoid the dimensionality explosion of one-hot encoding while preserving frequency information. This approach balances HIPAA compliance (data stays within AWS) with model performance.

Answer

Replace missing lab results with the overall mean, and use a binary flag for nullness. For diagnosis codes, apply one-hot encoding after grouping codes into 20 categories based on clinical relevance.

Answer

Drop all records where lab_result is null, and use one-hot encoding for diagnosis codes.

Answer

Use Amazon SageMaker Data Wrangler's built-in 'Fill missing' with KNN imputation for lab results, and apply ordinal encoding to diagnosis codes based on the order of ICD-10 chapters.

Question 18

A company is using Amazon SageMaker to deploy a model for real-time inference. The model requires access to a private S3 bucket that contains reference data. The company wants to ensure that the endpoint can access the S3 bucket without using a public internet connection. Which TWO actions should they take? (Select TWO.)

Accepted Answer

Attach the endpoint to a VPC that has a VPC endpoint for S3.. Attaching the SageMaker endpoint to a VPC with a VPC endpoint for S3 (Gateway type) allows the endpoint to access the S3 bucket using AWS's private network, bypassing the public internet. This ensures traffic stays within the AWS backbone, meeting the requirement for no public internet connection. Option C is also correct because the SageMaker execution role must have an IAM policy with s3:GetObject permissions to authorize the read access to the private S3 bucket, which is a prerequisite for any S3 operation.

Answer

Configure the endpoint's security group to allow outbound traffic to the S3 bucket's IP range.

Answer

Attach the endpoint to a VPC with an internet gateway and route the S3 traffic through the internet gateway.

Answer

Attach the endpoint to a VPC with a NAT gateway to route traffic to S3.

Question 19

A team is deploying a model that requires low-latency inference for real-time predictions. They are using a SageMaker endpoint with a single instance. During testing, they observe high latency. Which change would most effectively reduce latency?

Accepted Answer

Switch to a larger instance type. Switching to a larger instance type (Option D) directly increases the compute and memory resources available to the SageMaker endpoint, which reduces inference latency by allowing the model to process requests faster. Since the team is using a single instance, scaling up is the most straightforward way to handle the computational load and meet real-time latency requirements.

Answer

Use a multi-model endpoint

Answer

Add Elastic Inference

Answer

Enable SageMaker Batch Transform

Question 20

A data scientist is using Amazon SageMaker Data Wrangler to prepare a dataset. Which TWO features of Data Wrangler can be used to handle imbalanced classification problems? (Choose two.)

Accepted Answer

The Random Oversampling transform to duplicate minority class instances.. Amazon SageMaker Data Wrangler includes a built-in Random Oversampling transform that duplicates instances of the minority class to balance the class distribution. This directly addresses imbalanced classification by increasing the representation of the underrepresented class without generating synthetic data.

Answer

The Drop Duplicates transform to remove redundant rows.

Answer

Standardization to scale numerical features.

Answer

One-hot encoding for categorical variables.