MLS-C01 Practice Exam 57 — 20 Questions

Question 1

A company runs a machine learning pipeline on Amazon SageMaker. The pipeline consists of three steps: data preprocessing (using a custom container), training (using a built-in algorithm), and model evaluation (using a custom container). The pipeline is orchestrated using AWS Step Functions. Recently, the pipeline has been failing intermittently at the model evaluation step with a 'TimeoutError'. The evaluation step runs a Python script that loads the trained model and a test dataset from S3, computes metrics, and writes results back to S3. The step is configured with a timeout of 600 seconds. The test dataset size has grown over time. The data science team suspects that the timeout is due to the increased data size. They want a solution that minimizes changes to the existing infrastructure and avoids increasing the timeout arbitrarily. Which approach should the team take?

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

Accepted Answer

Modify the evaluation script to process the test dataset in parallel batches, and use multiprocessing to distribute the workload within the same container.. It addresses the root cause—the evaluation script's inability to process the growing dataset within the 600-second timeout—by parallelizing the workload within the same container. This approach minimizes infrastructure changes (no instance type or timeout increase) and leverages Python's multiprocessing to reduce wall-clock time, directly tackling the 'TimeoutError' without arbitrary timeout extensions.

Answer

Increase the timeout to 1200 seconds and use a larger instance type for the evaluation step.

Answer

Increase the timeout to 1800 seconds to accommodate the larger dataset.

Answer

Switch the evaluation step to use the 'ml.m5.4xlarge' instance type for more memory and compute.

Question 2

A data analyst is using AWS Glue to catalog datasets for exploratory analysis. The analyst wants to understand the schema and data types. Which TWO tools can the analyst use to view the schema of a table in the AWS Glue Data Catalog? (Choose TWO.)

Accepted Answer

Amazon Athena. Amazon Athena can query the AWS Glue Data Catalog using SQL, including viewing table schemas via the INFORMATION_SCHEMA or by running DESCRIBE statements on tables. The AWS Glue console directly displays the schema of tables in the Data Catalog under the 'Tables' section. Amazon Redshift query editor is for querying Redshift data warehouses, not for directly viewing Glue Catalog schemas unless a federated query is set up. Amazon QuickSight is a business intelligence tool for visualizing data, not for schema exploration. Amazon S3 console only shows objects in S3 buckets, not the schema of Glue tables.

Answer

Amazon Redshift query editor

Answer

Amazon QuickSight

Answer

Amazon S3 console

Question 3

A data scientist is working with a dataset that has missing values in 30% of rows for a categorical feature 'city'. Which EDA step should be performed before deciding on imputation?

Accepted Answer

Check if missingness is related to other features or random. Before deciding on imputation for the 'city' feature, the first exploratory data analysis (EDA) step is to investigate the pattern of missingness. Option A is correct because you must determine whether the missing data are Missing Completely at Random (MCAR), Missing at Random (MAR), or Missing Not at Random (MNAR). This involves checking if missingness in 'city' is related to other features or is random. Understanding the missing mechanism informs the appropriate imputation strategy. Option B (impute with mode) is an imputation method, not a diagnostic step; applying it without prior analysis risks introducing bias. Option C (drop rows) may be valid only if missingness is MCAR and the amount of data loss is acceptable, but it should not be the first step. Option D (label encoding) transforms categorical data and does not address missing values.

Answer

Impute missing values with the mode of the column

Answer

Drop all rows with missing values

Answer

Encode the city feature using label encoding

Question 4

A data scientist runs the above AWS CLI command. What does the command do?

Accepted Answer

It lists objects larger than 1,000,000 bytes under the data/ prefix.. The AWS CLI command `aws s3api list-objects --bucket your-bucket --prefix data/ --query 'Contents[?Size > `1000000`].[Key]'` lists the keys (names) of objects in the bucket under the 'data/' prefix whose size is greater than 1,000,000 bytes. The `--query` uses JMESPath to filter objects where Size > 1000000 and then projects the Key field. Option B is incorrect because it states 'counts', but the command returns keys, not a count. Option C is incorrect because it filters by size, not by date. Option D is incorrect because 1,000,000 bytes is not exactly 1 MB (which is 1,048,576 bytes), so the description 'larger than 1 MB' is inaccurate; the command uses bytes, not MB.

Answer

It counts the number of objects larger than 1 MB.

Answer

It lists objects created after January 2023.

Answer

It lists objects larger than 1 MB in size.

Question 5

A company runs a real-time fraud detection pipeline using Amazon Kinesis Data Analytics. The pipeline reads from a Kinesis data stream, performs sliding window aggregations, and writes results to a DynamoDB table. The application is experiencing high latency during peak hours. Which action would MOST effectively reduce latency?

Accepted Answer

Increase the number of shards in the Kinesis data stream.. Increasing the number of shards in the Kinesis data stream directly increases the ingestion capacity and parallelism of the stream, allowing the Kinesis Data Analytics application to consume and process records faster. This addresses the root cause of high latency during peak hours by scaling the data source throughput, which is the bottleneck in a streaming pipeline.

Answer

Enable DynamoDB auto scaling to handle write spikes.

Answer

Decrease the parallelism level in the Kinesis Data Analytics application.

Answer

Increase the sliding window size to reduce computational frequency.

Question 6

A team has a dataset with 500 features and wants to reduce dimensionality. During EDA, they compute the variance of each feature. Which finding would most likely lead to feature removal?

Accepted Answer

Some features have near-zero variance. Features with near-zero variance have little to no information content and are often redundant for modeling. Removing them reduces dimensionality without significant loss. Option A is incorrect: high correlation between features suggests multicollinearity, but variance is not the direct measure; correlation is addressed by other techniques like PCA. Option B is incorrect: negative covariance with the target indicates an inverse relationship, which can be informative. Option C is incorrect: high variance often indicates useful information, though it may warrant scaling; it is not a reason for removal.

Answer

Some features have high correlation with each other

Answer

Some features have negative covariance with the target

Answer

Some features have very high variance

Question 7

A data scientist is training a binary classification model for fraud detection. The dataset is highly imbalanced with only 1% fraudulent transactions. The model currently achieves 99% accuracy but only catches 5% of actual fraud cases. Which metric should the data scientist focus on to better evaluate model performance?

Accepted Answer

Recall. In fraud detection with highly imbalanced data (1% fraud), accuracy is misleading because a model can achieve 99% accuracy by simply predicting 'not fraud' for all transactions. Recall (true positive rate) measures the proportion of actual fraud cases correctly identified, which is critical when the cost of missing fraud is high. The model currently catches only 5% of fraud, so improving recall is the primary goal to reduce false negatives.

Answer

Precision

Answer

Accuracy

Answer

Root Mean Squared Error (RMSE)

Question 8

A data analyst is working with a time series dataset that shows increasing variance over time. To stabilize the variance before modeling, which transformation is most appropriate?

Accepted Answer

Log transformation. The log transformation (option C) is appropriate when variance increases with the mean, which is common in time series data. It compresses the scale and stabilizes variance. First-order differencing (A) is used to remove trend or seasonality, not to stabilize variance. The Box-Cox transformation (B) can also stabilize variance, but it is a more general family that includes log as a special case; however, log is simpler and often preferred when the data are positive. Min-max scaling (D) rescales to a fixed range but does not address changing variance.

Answer

First-order differencing

Answer

Box-Cox transformation

Answer

Min-max scaling

Question 9

A data scientist is exploring a dataset and finds that the correlation between two features is 0.95. What should the data scientist do to address multicollinearity before training a linear regression model?

Accepted Answer

Remove one of the two features. Removing one of the highly correlated features reduces multicollinearity. Regularization (B) like Ridge can help but does not remove multicollinearity. Scaling (C) does not affect correlation, so it does not address multicollinearity. PCA (D) can reduce multicollinearity by creating uncorrelated components, but it changes interpretability and is not the simplest solution.

Answer

Apply L2 regularization

Answer

Standardize the features

Answer

Apply Principal Component Analysis

Question 10

A data scientist is using Amazon SageMaker to train a deep learning model with a large dataset. The training job fails with a 'CUDA out of memory' error. What is the MOST efficient way to resolve this issue?

Accepted Answer

Reduce the batch size. The 'CUDA out of memory' error occurs when the GPU's memory is insufficient to hold the model parameters, gradients, optimizer states, and the current batch of data. Reducing the batch size decreases the memory footprint per training step, allowing the model to fit within the available GPU memory without requiring a more expensive instance or sacrificing GPU acceleration.

Answer

Switch to a CPU-only instance

Answer

Use a larger instance type with more GPUs

Answer

Increase the batch size

Question 11

A data scientist is analyzing a dataset with high cardinality categorical features (e.g., user IDs with millions of unique values). They want to visualize the relationship between these categorical features and a continuous target variable. Which approach is most effective for EDA?

Accepted Answer

Group rare categories into an 'Other' category and use box plots. For high cardinality categorical features like user IDs with millions of unique values, directly visualizing all categories is infeasible. Grouping rare categories into an 'Other' category reduces cardinality, enabling effective comparison of the continuous target distribution across categories using box plots. This approach preserves meaningful information while avoiding clutter. Option B (one-hot encoding) creates an excessively wide feature set unsuitable for simple visualization. Option C (bar chart with all categories) would be overcrowded and unreadable. Option D (removing features) discards potentially valuable information. Option E (feature hashing) is more appropriate for modeling pipelines, not for intuitive EDA visualization. Thus, Option A is the most effective approach.

Answer

Apply one-hot encoding and use scatter plots

Answer

Use a bar chart with all categories on x-axis

Answer

Remove the categorical features from analysis

Answer

Apply feature hashing and visualize the hashed values

Question 12

A data scientist is performing EDA on a dataset with 100 features. They want to reduce dimensionality by removing highly correlated features. Which TWO approaches are appropriate? (Choose TWO.)

Accepted Answer

Compute a correlation matrix and remove one feature from each pair with correlation >0.95.. Options C and D are correct. Option C directly addresses dimensionality reduction by removing highly correlated features, which reduces redundancy. Option D uses PCA to create uncorrelated components, effectively reducing dimensionality while preserving variance. Option A is incorrect because feature importance from random forest is used for selecting features predictive of the target, not for removing correlated features per se. Option B is incorrect because VarianceThreshold removes features with low variance, not specifically for correlation. Option E is incorrect because L1 regularization (Lasso) is a modeling technique that zeroes out coefficients during model training, not a method for EDA.

Answer

Use feature importance from a random forest to select top features.

Answer

Remove features with low variance using VarianceThreshold.

Answer

Apply L1 regularization (Lasso) during model training to zero out coefficients of correlated features.

Question 13

A machine learning team is using Amazon SageMaker to train a model. The training data is stored in an S3 bucket. The team wants to ensure that the training job can access the data securely without using long-lived AWS credentials. Which approach should the team use?

Accepted Answer

Specify an IAM role in the SageMaker training job configuration. SageMaker training jobs can assume an IAM role specified in the job configuration to obtain temporary security credentials via AWS Security Token Service (STS). This allows the training job to access the S3 bucket securely without embedding long-lived AWS access keys in code or configuration files.

Answer

Store AWS access keys in the training script

Answer

Use an S3 bucket policy that allows public access

Answer

Create a new IAM user for each training job

Question 14

A data scientist is using Amazon SageMaker Data Wrangler for exploratory data analysis. The dataset contains a column with missing values that are encoded as 'NA' strings. The data scientist wants to treat these as missing values during the import. Which step should the data scientist take?

Accepted Answer

Configure a custom missing value symbol 'NA' in the import settings of Data Wrangler.. Amazon SageMaker Data Wrangler allows specifying custom missing value symbols during the import step. By configuring 'NA' as a custom missing value symbol in the import settings, Data Wrangler will automatically treat 'NA' strings as missing values when reading the dataset. Option B is incorrect because the 'Impute' transform is used to fill missing values after they have been recognized as missing; it does not handle the initial identification of 'NA' strings as missing. Option C is incorrect because using 'Replace missing' after import is less efficient and not the recommended approach; it is better to handle it during import to ensure downstream transforms treat the values correctly. Option D is incorrect because dropping rows with 'NA' prematurely discards data before any analysis; the goal is to treat 'NA' as missing, not to remove the rows.

Answer

Use the 'Impute' transform to fill 'NA' with the mean of the column.

Answer

Use the 'Replace missing' transform to replace 'NA' with null after import.

Answer

Use the 'Drop missing' transform to remove rows containing 'NA'.

Question 15

A data scientist is exploring a dataset stored in an Amazon S3 bucket. The dataset contains both numerical and categorical features. The scientist wants to compute summary statistics (mean, median, standard deviation) for all numerical features and count the distinct values for categorical features. Which AWS service is most appropriate for this task with minimal coding?

Accepted Answer

AWS Glue DataBrew. AWS Glue DataBrew is the most appropriate service for this task because it provides a visual, no-code interface for data preparation and profiling. It can automatically compute summary statistics (mean, median, standard deviation) for numerical features and count distinct values for categorical features without writing any code. Amazon Athena requires writing SQL queries, which is not 'minimal coding' and is less suitable for profiling. AWS Glue ETL jobs require writing Python or Scala code, so it is more code-intensive. Amazon SageMaker Data Wrangler also requires some setup and integration with SageMaker, and while it can perform similar tasks, it is not as straightforward for simple profiling as DataBrew. Amazon EMR requires managing clusters and writing code, making it the least minimal coding option.

Answer

Amazon Athena

Answer

AWS Glue ETL jobs

Answer

Amazon SageMaker Data Wrangler

Answer

Amazon EMR

Question 16

A machine learning engineer is performing exploratory data analysis on a large dataset stored in S3 using Amazon Athena. The dataset contains a timestamp column 'event_time' of type string. The engineer wants to analyze daily trends. Which approach is the most cost-effective and efficient?

Accepted Answer

Partition the table by date derived from the event_time string and query using partition filtering.. Converting the string to a date type in the query allows Athena to use partition pruning if the table is partitioned by date, reducing scanned data. Option A is wrong because creating a view does not reduce data scanned; CAST still processes all rows. Option B is wrong because using CAST in the SELECT statement still scans all data. Option C is wrong because converting to Parquet is beneficial but not the most direct for the given task.

Answer

Create a view that casts the column to timestamp and query the view.

Answer

Use the CAST function in the SELECT statement to convert the string to timestamp.

Answer

Convert the data to Parquet format with a timestamp column and re-query.

Question 17

A data scientist is analyzing a dataset with missing values in several columns. The dataset contains both numerical and categorical features. Which approach should the data scientist use to handle missing values while minimizing bias and preserving relationships in the data?

Accepted Answer

Use multiple imputation (e.g., MICE) to impute missing values. Multiple Imputation by Chained Equations (MICE) models each missing value as a function of other variables, preserving relationships and reducing bias. Option B (forward-fill) is unsuitable for non-time-series data and can introduce bias. Option C (deleting rows) reduces sample size and may introduce bias if data is not missing completely at random. Option D (mean/median imputation) distorts distributions and reduces variance, potentially biasing relationships.

Answer

Use forward-fill to propagate the last observed value

Answer

Delete all rows with missing values

Answer

Replace missing values with the mean or median of each column

Question 18

A data scientist is analyzing a dataset with a timestamp column and several numeric measurements. The goal is to detect seasonality and trends. Which AWS service can be used directly from SageMaker Studio to perform this analysis without writing code?

Accepted Answer

Amazon SageMaker Data Wrangler. SageMaker Data Wrangler is the correct choice because it integrates directly with SageMaker Studio and includes built-in time series analysis capabilities such as seasonality detection and trend analysis, all without writing code. Option A (Amazon Forecast) is a forecasting service that requires a separate workflow and is not directly usable for exploratory analysis from Studio. Option C (Amazon QuickSight ML Insights) is for visualization and anomaly detection but not for time series decomposition within SageMaker Studio. Option D (AWS Glue DataBrew) is a data preparation tool that does not provide native time series analysis features.

Answer

Amazon Forecast

Answer

Amazon QuickSight ML Insights

Answer

AWS Glue DataBrew

Question 19

An organization stores streaming data in Amazon Kinesis Data Streams. A data analyst wants to perform real-time exploratory data analysis on the incoming data to detect anomalies. Which AWS service should the analyst use to run SQL queries on the streaming data?

Accepted Answer

Amazon Kinesis Data Analytics. Amazon Kinesis Data Analytics enables running SQL queries on streaming data in real-time, which is exactly what the data analyst needs for real-time exploratory data analysis and anomaly detection. Option B (Amazon SageMaker) is incorrect because it is a machine learning service for building and training models, not for running SQL on streaming data. Option C (AWS Glue) is incorrect because it is a serverless ETL service for batch processing, not real-time SQL. Option D (Amazon Athena) is incorrect because it is an interactive query service for analyzing data in S3 using SQL, but it is designed for batch queries on static data, not streaming data.

Answer

Amazon SageMaker

Answer

AWS Glue

Answer

Amazon Athena

Question 20

A data scientist is training a binary classification model on imbalanced data (95% negative, 5% positive). The model achieves 99% accuracy on the test set but fails to detect any positive cases. Which metric should the scientist focus on to evaluate model performance?

Accepted Answer

Recall. Recall (true positive rate) measures the ability to find all positive samples, which is critical for imbalanced datasets where accuracy can be misleading. Option A is wrong because accuracy is high but misleading in imbalanced data. Option C is wrong because RMSE is a regression metric, not suitable for classification. Option D is wrong because precision focuses on the accuracy of positive predictions but does not capture missed positives; recall is more important for detecting all positive cases.

Answer

Accuracy

Answer

RMSE

Answer

Precision