MLA-C01 Practice Test 2 — 10 Questions

Question 1

Refer to the exhibit. A data scientist creates a SageMaker Pipeline definition using the JSON shown. The pipeline runs successfully, but the scientist notices that the training step did not use the parameter 'TrainingInstanceCount' defined in Parameters. Why did this happen?

Accepted Answer

The steps do not reference the Parameters; the values are hardcoded in the step definitions.. Option C is correct because the SageMaker Pipeline definition shows that the training step's `InstanceCount` field is hardcoded to `1` in the step definition, rather than referencing the `TrainingInstanceCount` parameter using the `Parameters` object (e.g., `Parameters.TrainingInstanceCount`). In SageMaker Pipelines, parameters defined in the `Parameters` section must be explicitly referenced within the step definitions using the `Parameters` object; otherwise, the pipeline uses the hardcoded values and ignores the parameters entirely.

Answer

The pipeline encountered a runtime error and fell back to default values.

Answer

The parameter name has a typo; it should be 'TrainingInstanceCount' not 'TrainingInstanceCount'.

Answer

The training image is not compatible with the specified instance type.

Question 2

A machine learning team is preparing a dataset for a regression model. The dataset contains numerical features that are on different scales (e.g., age 0-100, income 0-1,000,000). The team plans to use Amazon SageMaker to train a linear regression model. Which THREE data preparation steps should the team take to ensure the model performs well? (Select THREE.)

Accepted Answer

Handle missing values by imputation or removal.. Option C is correct because missing values can cause errors or biased estimates in linear regression models. Amazon SageMaker's built-in linear regression algorithm does not handle missing data automatically, so imputation (e.g., mean/median) or removal is necessary to ensure the training process completes and produces reliable coefficients.

Answer

Apply feature selection to reduce the number of features.

Answer

Remove outliers from the dataset.

Question 3

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. Option A is correct because 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 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. Option B is correct because 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 team is using Amazon SageMaker Processing for data preprocessing. They have a Parquet dataset in Amazon S3. Which configuration will provide the most efficient reading of the dataset during processing?

Accepted Answer

Read the Parquet files directly using SparkSession.read.parquet. Option D is correct because SageMaker Processing natively integrates with Apache Spark, and reading Parquet files directly via `SparkSession.read.parquet` leverages columnar storage, predicate pushdown, and compression (e.g., Snappy) to minimize I/O and deserialization overhead. This approach is far more efficient than text-based or format-conversion methods, as Parquet is optimized for analytical workloads and preserves schema information.

Answer

Read the Parquet files as text using SparkContext.textFile

Answer

Split the dataset into many small Parquet files (e.g., 1 MB each)

Answer

Convert the Parquet files to CSV before processing

Question 6

A machine learning engineer is preparing a dataset for a multiclass classification task. The dataset has 10 features and 100,000 rows. Which TWO techniques should the engineer use to reduce the risk of overfitting during data preparation?

Accepted Answer

Data augmentation (e.g., adding noise). Data augmentation (A) is correct because it artificially increases the diversity of the training set by adding noise or transformations, which helps the model generalize better and reduces overfitting. Feature selection using correlation analysis (E) is correct because it removes redundant or highly correlated features, simplifying the model and minimizing the risk of learning noise from irrelevant predictors.

Answer

SMOTE to balance classes

Answer

One-hot encoding of all categorical features

Answer

Log transformation of skewed features

Question 7

A machine learning engineer is preparing a dataset that contains both numerical and categorical features. The categorical features have high cardinality (e.g., zip code with thousands of unique values). Which technique is most appropriate for encoding these high-cardinality categorical features?

Accepted Answer

Target encoding. Target encoding is the most appropriate technique for high-cardinality categorical features because it replaces each category with the mean of the target variable for that category, effectively capturing the predictive signal while keeping the feature as a single numeric column. This avoids the dimensionality explosion of one-hot encoding and the arbitrary ordinality of label encoding, making it a common choice in gradient boosting frameworks like XGBoost or LightGBM for datasets with thousands of unique categories.

Answer

Label encoding

Answer

One-hot encoding

Answer

Frequency encoding

Question 8

A data scientist is building a text classification model using a pre-trained BERT model from the Hugging Face library on SageMaker. The scientist wants to fine-tune the model on a custom dataset. Which TWO steps are necessary to set up the fine-tuning job? (Select TWO.)

Accepted Answer

Use the HuggingFace estimator provided by SageMaker. Option A is correct because the SageMaker HuggingFace estimator is specifically designed to simplify fine-tuning of pre-trained Hugging Face models like BERT. It automatically handles the underlying infrastructure, including the correct PyTorch/TensorFlow and Transformers versions, without requiring custom Docker containers. This is the recommended approach for Hugging Face model fine-tuning on SageMaker.

Answer

Enable SageMaker Clarify for explainability during training

Answer

Build a custom Docker container with PyTorch and Transformers

Answer

Use SageMaker Processing to preprocess the data in parallel

Question 9

A machine learning engineer is deploying a custom PyTorch model to a SageMaker endpoint for real-time inference. The model requires GPU acceleration. The engineer wants to minimize latency and cost. Which THREE actions should the engineer take? (Select THREE.)

Accepted Answer

Compile the model with SageMaker Neo. SageMaker Neo compiles the PyTorch model into an optimized runtime binary that is specifically tuned for the target hardware (e.g., GPU instances like ml.p3). This reduces inference latency by applying graph-level optimizations, operator fusion, and memory layout transformations without changing the model's accuracy, while also lowering compute resource usage and cost.

Answer

Use an ml.c5.2xlarge instance with CPU only

Answer

Use SageMaker Batch Transform for inference

Question 10

A data scientist is preparing a large dataset for training a binary classification model. The dataset has a severe class imbalance (95% negative, 5% positive). Which data preparation technique should the scientist use to address this imbalance without losing too much data?

Accepted Answer

SMOTE (Synthetic Minority Over-sampling Technique). SMOTE (Synthetic Minority Over-sampling Technique) is the best choice because it generates synthetic examples for the minority class by interpolating between existing minority instances and their k-nearest neighbors, rather than simply duplicating data. This addresses the severe 95:5 class imbalance without losing data (as undersampling would) and without the overfitting risk of naive random oversampling. The synthetic samples help the model learn a more general decision boundary for the positive class.

Answer

Random undersampling of the majority class

Answer

Random oversampling of the minority class

Answer

Apply class weights during model training

MLA-C01 Practice Test 2 — 10 Questions

Exhibit

MLA-C01 Practice Test 2 — 10 Questions

Exhibit