MLS-C01 Practice Test 6 — 10 Questions

Question 1

A data engineer runs the AWS CLI command above to inspect a file in S3. They need to determine if the file was modified after a Glue ETL job processed it. What additional information could they obtain from this command?

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

Accepted Answer

The object's last modified timestamp.. The AWS CLI command `aws s3api head-object --bucket my-bucket --key my-key` returns metadata about an S3 object without downloading it. The `LastModified` field in the response provides the exact timestamp of the last modification, which can be compared to the Glue ETL job's execution time to determine if the file was modified after processing. This is the only field that directly answers the question about modification timing.

Answer

The object's content type.

Answer

The object's storage class.

Answer

The object's ETag.

Question 2

A data scientist is performing EDA on a dataset of 1 million images stored in Amazon S3. Each image is 100x100 pixels in RGB format. The data scientist wants to compute the mean pixel value per channel across the entire dataset. Which approach is most efficient?

Accepted Answer

Use Amazon SageMaker Processing with a custom Python script that iterates over S3 objects and aggregates pixel values.. (Amazon SageMaker Processing with a custom Python script) is the most efficient because it can distribute the computation across multiple instances, processing images in parallel without loading all into memory at once. This is ideal for a large dataset of 1 million images. Option B (Athena) is designed for querying structured data, not image processing. Option C (AWS Glue ETL) is for ETL on tabular data, not image processing. Option D (SageMaker notebook with large instance) would require loading all images into memory, which is not feasible for 1 million images.

Answer

Use Amazon Athena with a SQL query on the image metadata stored in a CSV file.

Answer

Use AWS Glue ETL to read images and compute the mean.

Answer

Use a SageMaker notebook instance with a large instance type to load all images into memory and compute the mean.

Question 3

Which TWO of the following are appropriate methods for handling missing data in a dataset?

Accepted Answer

Multiple imputation. Multiple imputation and using algorithms that handle missing values (e.g., XGBoost) are valid. Listwise deletion reduces sample size. Mean imputation may bias distributions. Dropping features with many missing values may lose information.

Answer

Dropping features with more than 50% missing values

Answer

Mean imputation for all features

Answer

Listwise deletion (removing rows with missing values)

Question 4

A data scientist is using SageMaker to train a linear regression model. The target variable has a long-tail distribution. Which data transformation is LEAST likely to improve model performance?

Accepted Answer

Normalize all feature values to [0,1]. Normalizing feature values to [0,1] is a scaling technique that does not address the long-tail distribution of the target variable. Long-tail distributions typically require transformations that compress the tail (e.g., log or Box-Cox) to make the relationship more linear and reduce the influence of extreme values. Feature normalization helps with gradient descent convergence but does not fix skewness in the target, so it is least likely to improve model performance for this specific issue.

Answer

Add interaction terms between features

Answer

Apply log transformation to the target variable

Answer

Remove outliers from the target variable

Question 5

A machine learning team is reviewing a dataset for a regression problem. They notice that the target variable has a right-skewed distribution. Which transformation should they consider applying to the target variable to improve model performance?

Accepted Answer

Apply log transformation to the target variable.. Log transformation is commonly applied to right-skewed data to make it more normally distributed, which can improve model performance. Option A (StandardScaler) is for scaling, not skewness. Option B (MinMaxScaler) also doesn't address skewness. Option D (One-hot encoding) is for categorical variables.

Answer

Apply StandardScaler to the target variable.

Answer

Apply MinMaxScaler to the target variable.

Answer

Apply one-hot encoding to the target variable.

Question 6

During exploratory data analysis, a data scientist notices that the Pearson correlation coefficient between two continuous variables is 0.85. What does this indicate?

Accepted Answer

A strong positive linear relationship. A correlation coefficient of 0.85 indicates a strong positive linear relationship. Option A is wrong because correlation does not imply causation. Option C is wrong because the coefficient is positive and strong, not negative and weak. Option D is wrong because a coefficient of 0.85 indicates a strong relationship, not no relationship.

Answer

A causal relationship between the two variables

Answer

A weak negative linear relationship

Answer

No relationship between the variables

Question 7

A machine learning engineer is examining a dataset containing text reviews. They want to convert the text into numerical features for a model. During EDA, they notice that the word 'the' appears in almost every review, while words like 'excellent' appear rarely. Which of the following techniques should they use to reduce the impact of very common words?

Accepted Answer

Apply TF-IDF transformation.. TF-IDF transformation downweights common words (like 'the') and emphasizes rare but informative words (like 'excellent'). Option B (removing stopwords) is insufficient because it does not adjust for frequency beyond removing a predefined list; TF-IDF handles frequency weighting. Option C (word2vec embeddings) captures semantic relationships but does not specifically reduce the impact of common words. Option D (bag-of-words) does not perform any weighting, so common words dominate.

Answer

Remove stopwords from the text.

Answer

Use word2vec embeddings.

Answer

Use a bag-of-words representation.

Question 8

A data scientist is training a binary classification model on an imbalanced dataset where the positive class represents only 5% of the data. The model currently achieves 95% accuracy but only 10% recall on the positive class. Which metric should the scientist focus on to improve the model's ability to detect the positive class?

Accepted Answer

Recall. Recall measures the proportion of actual positives the model correctly identifies (TP / (TP + FN)). With only 10% recall on the positive class, the model is missing 90% of true positives — exactly the problem the scientist needs to fix. Optimizing recall (e.g., via class weighting, resampling, or threshold tuning) directly improves the model's ability to detect the minority positive class.

Answer

Accuracy

Answer

Precision

Answer

AUC-ROC

Question 9

A machine learning team is building a fraud detection system using Amazon SageMaker. The training data is highly imbalanced (99% legitimate, 1% fraudulent). They need to maximize the recall of the fraud class while keeping precision above 90%. Which approach should they take?

Accepted Answer

Train a model using the original data, then adjust the decision threshold on the validation set to maximize recall while precision > 90%. Adjusting the decision threshold on the validation set directly optimizes the trade-off between recall and precision. By lowering the threshold, the model classifies more instances as fraud, increasing recall, while the precision constraint (≥90%) ensures the threshold is set at a point where false positives remain acceptably low. This approach works with any probabilistic classifier and does not alter the training data distribution.

Answer

Undersample the majority class to create a balanced dataset and train a Random Forest

Answer

Train an XGBoost model with scale_pos_weight parameter set to 99

Answer

Use SMOTE to oversample the fraud class and then train a logistic regression

Question 10

A data scientist is training a deep learning model for object detection using Amazon SageMaker. The training job is using a single GPU instance and is taking too long. Which THREE actions can reduce training time? (Choose THREE.)

Accepted Answer

Enable mixed precision training with FP16. Option B is correct because mixed precision training with FP16 reduces memory bandwidth and uses tensor cores on modern NVIDIA GPUs, allowing faster matrix operations and larger effective batch sizes, which shortens training time. Option C is correct because moving from a single-GPU instance to a multi-GPU instance like p3.16xlarge (8 V100 GPUs) enables data-parallel training within one instance, increasing throughput and reducing wall-clock training time. Option E is correct because distributed training across multiple instances (e.g., SageMaker distributed data parallel) scales the workload horizontally, further cutting training time for large models. Option A is incorrect because CPU instances lack the massive parallelism and tensor cores of GPUs, so training would be dramatically slower. Option D is incorrect because reducing the batch size lowers per-step compute but increases the number of steps and often reduces hardware utilization, generally increasing total training time.

Answer

Use a CPU instance instead of GPU

Answer

Reduce the batch size