MLA-C01 Practice Test 12 — 15 Questions

Question 1

A team needs to deploy a model that has compliance requirements to log all inference requests and responses for auditing. The model will be served using a real-time endpoint. How can they achieve this without custom code?

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

Accepted Answer

Enable SageMaker Data Capture on the endpoint. SageMaker Data Capture is the native, no-code feature that automatically logs inference requests and responses for real-time endpoints. It captures payload data to an S3 bucket without requiring any custom code, directly meeting the compliance requirement for audit logging.

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Add a custom Lambda function using a container

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Use SageMaker Debugger to monitor inference

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Enable CloudTrail for the endpoint

Question 2

An ML team at a financial services company has developed a fraud detection model using Amazon SageMaker. The model is currently deployed to a production endpoint with a single variant using the previous model version. The team wants to deploy a new model version with a canary deployment where 10% of traffic goes to the new version and 90% remains on the old version for 30 minutes before shifting all traffic to the new version if no issues are detected. Which step is essential to achieve this safe rollout?

Accepted Answer

Update the endpoint with a new production variant for the new model version and set the 'InitialVariantWeight' to 10 for the new variant and 90 for the old variant, specifying a 'BlueGreenUpdatePolicy' with a 'TrafficRoutingConfiguration' for canary.. It uses the SageMaker endpoint update with a new production variant and sets 'InitialVariantWeight' to 10 for the new model and 90 for the old model, which routes 10% of traffic to the new version. Additionally, specifying a 'BlueGreenUpdatePolicy' with a 'TrafficRoutingConfiguration' for canary enables the automatic shift of all traffic to the new variant after 30 minutes if no issues are detected, achieving the desired safe rollout.

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Use the 'Deploy' method on the model object with the 'mode' parameter set to 'canary' within the built-in XGBoost algorithm container.

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Ensure the endpoint is hosted on at least two instances to enable load balancing, then deploy the new model version as a separate variant and manually adjust the endpoint's DNS to split traffic.

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Deploy the new model as a separate endpoint and use a SageMaker predictor to randomly route 10% of inference requests to the new endpoint.

Question 3

A machine learning team is preparing numerical features for a linear regression model. Feature 'A' ranges from 0 to 1000, feature 'B' ranges from 0 to 1, and feature 'C' ranges from -10000 to 10000. The team wants to ensure that feature scales do not affect the model's coefficients and that the features are bounded between 0 and 1. Which transformation should they apply?

Accepted Answer

MinMaxScaler. MinMaxScaler scales each feature to a fixed range (default 0 to 1) by subtracting the minimum and dividing by the range. This ensures all features are within [0,1] and eliminates scale differences.

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RobustScaler (based on median and IQR)

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Log transformation

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StandardScaler (z-score normalization)

Question 4

A data scientist is using Amazon SageMaker Feature Store to manage features for a fraud detection model. The model needs to be updated hourly with new features from streaming data, and historical features must be available for training. Which THREE actions are necessary to set up this pipeline? (Choose THREE.)

Accepted Answer

Use point-in-time queries from the offline store to retrieve historical feature values for training. To ingest streaming features, use Kinesis Data Streams/Firehose. To store them, create a feature group with both online and offline stores. To retrieve historical data, use point-in-time queries from the offline store. The online store alone cannot serve historical queries. Ground Truth is for labeling, not feature ingestion. Data Wrangler is for preparation, not ingestion.

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Use Amazon SageMaker Data Wrangler to continuously ingest streaming data into the feature group

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Use Amazon SageMaker Ground Truth to label incoming streaming data

Question 5

A machine learning team is handling a text classification task with a dataset of 1 million documents. They need to convert text into numerical features. Which THREE techniques are commonly used for feature extraction from text? (Select THREE.)

Accepted Answer

Word embeddings (e.g., Word2Vec). TF-IDF, word embeddings (e.g., Word2Vec, GloVe), and bag-of-words are standard text vectorization methods. One-hot encoding is for categorical features, not text. PCA is for dimensionality reduction after vectorization.

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One-hot encoding

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Principal component analysis (PCA)

Question 6

A team is preparing text data for sentiment analysis. They have a large corpus of customer reviews. They want to convert the text into numerical features using a technique that captures word importance relative to the whole corpus. Which feature extraction method should they use?

Accepted Answer

TF-IDF. TF-IDF (Term Frequency-Inverse Document Frequency) weights words by their frequency in a document and their rarity across the corpus, giving higher weight to important words that are not common across all documents.

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Word2Vec embeddings

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One-hot encoding

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Bag-of-words (CountVectorizer)

Question 7

A data engineer needs to perform feature selection on a dataset with 500 numeric features to train a regression model. The engineer wants to remove features that are redundant or have low predictive power. Which TWO techniques should the engineer consider? (Select TWO.)

Accepted Answer

Correlation analysis. Correlation analysis identifies redundant features (highly correlated with each other), and Lasso regularization can shrink coefficients of irrelevant features to zero. One-hot encoding is for categorical features; oversampling addresses imbalance; standardization normalizes scale.

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Oversampling

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Standardization

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One-hot encoding

Question 8

An organization needs to prepare data for a churn prediction model. They observe missing values in 15% of the records for a numerical feature 'usage_minutes'. The data is not missing at random (NMAR). Which imputation strategy is MOST robust?

Accepted Answer

Use a regression model to predict missing values based on other features. When data is NMAR, simple mean/median imputation can introduce bias. Using a model to predict missing values based on other features can account for the systematic missingness.

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Replace missing values with the median of the feature

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Replace missing values with a constant, e.g., -1

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Drop all rows with missing values

Question 9

A data scientist is using Amazon SageMaker Data Wrangler to prepare a dataset that contains a column 'CustomerID' with high cardinality (over 10,000 unique values). The column will be used as a feature in a model predicting customer churn. What is the recommended approach to handle this high-cardinality feature?

Accepted Answer

Use target encoding to replace each ID with the average target per ID. Target encoding (also known as mean encoding) replaces each category with the mean of the target for that category. This is a common technique for high-cardinality features in supervised learning, though it requires careful handling to avoid data leakage.

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Drop the 'CustomerID' column entirely

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Apply ordinal encoding by assigning a unique integer to each ID

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Apply one-hot encoding to create a sparse binary representation

Question 10

A machine learning engineer is performing feature selection for a regression model with 200 features. The dataset has 10,000 samples. The engineer wants to remove irrelevant features while keeping those that have a strong non-linear relationship with the target. Which feature selection method is best suited for this requirement?

Accepted Answer

Mutual information. Mutual information measures the dependency between two variables and can capture non-linear relationships. It is model-agnostic and suitable for feature selection when non-linear dependencies are important.

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Lasso regularization (L1)

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Recursive feature elimination (RFE) with a linear model

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Pearson correlation coefficient

Question 11

A data scientist is preparing a dataset for a binary classification model. The dataset has 10,000 samples, but the positive class represents only 2% of the data. The data scientist needs to train a model that will be evaluated on a hold-out test set that preserves the original class distribution. Which data preparation strategy is MOST appropriate?

Accepted Answer

Oversample the minority class in the training set using SMOTE, and keep the test set as is.. SMOTE generates synthetic samples for the minority class to balance the training set, while keeping the test set realistic with the original class distribution. Oversampling the test set would give an overly optimistic evaluation, and undersampling the majority class in training may lose useful information. Class weights are a modeling technique, not a data preparation step.

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Undersample the majority class in the training set to match the minority class size.

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Randomly oversample the minority class in the entire dataset and then split.

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Apply SMOTE to the entire dataset before splitting into training and test sets.

Question 12

A team is building a time-series forecasting model for daily sales data. They want to evaluate model performance using cross-validation while respecting the temporal order of the data. Which data splitting strategy should they use?

Accepted Answer

Walk-forward validation (time-series split). Time-series data requires special splitting to avoid data leakage. Walk-forward validation (also called rolling-origin or time-series split) preserves temporal order by training on past data and validating on future data in sequential folds.

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Holdout with a random 80/20 split

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Random k-fold cross-validation

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Stratified sampling

Question 13

An ML team uses Amazon SageMaker Data Wrangler to prepare a dataset for a binary classification model. They suspect the dataset might contain bias against a certain demographic group. They want to detect and visualize potential bias before training the model. Which feature of SageMaker should they use?

Accepted Answer

SageMaker Clarify. Amazon SageMaker Clarify provides bias detection capabilities, including pre-training bias metrics, and can be integrated with Data Wrangler to analyze datasets. Data Wrangler itself does not have built-in bias detection, but it can export to Clarify for analysis.

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SageMaker Debugger

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SageMaker Experiments

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SageMaker Model Monitor

Question 14

A data scientist is preparing text data for sentiment analysis. They need to convert the text into numerical features while reducing the impact of common words. Which feature extraction method should they use?

Accepted Answer

TF-IDF vectorization. TF-IDF (Term Frequency-Inverse Document Frequency) downweights common words across documents, making it suitable for reducing the impact of stop words.

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Word2Vec embeddings

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Label encoding of each word

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CountVectorizer with n-grams

Question 15

A data scientist discovers that a dataset for binary classification contains 95% negative samples and 5% positive samples. Which technique is MOST appropriate to address the class imbalance?

Accepted Answer

Use SMOTE to generate synthetic samples for the minority class. SMOTE (Synthetic Minority Oversampling Technique) creates synthetic samples for the minority class, effectively balancing the dataset without loss of information.

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Remove all samples from the majority class

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Increase the learning rate of the model

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Downsample the majority class to 5% of the original size