MLS-C01 Practice Test 8 — 10 Questions

Question 1

After loading a dataset into a pandas DataFrame, a data scientist runs df.info() and sees that a column 'income' has object dtype. What does this indicate, and what EDA step should be taken?

Accepted Answer

The column contains strings; convert to numeric using pd.to_numeric() and investigate non-convertible values.. 'object' dtype in pandas typically indicates string or mixed types. The appropriate EDA step is to attempt conversion to numeric using pd.to_numeric() and investigate non-convertible values to handle data quality issues. Option A is incorrect because object dtype does not specifically indicate missing values; missing values can appear in any dtype. Option C is premature; conversion should precede normalization. Option D is incorrect because object dtype is not numeric.

Answer

The column has missing values; impute them.

Answer

Normalize the column to a 0-1 range.

Answer

The column is already numeric; proceed.

Question 2

Refer to the exhibit. A data scientist runs the above AWS CLI command to create a SageMaker training job using the built-in Linear Learner algorithm. The training job fails with an error. What is the most likely cause?

Accepted Answer

The S3 data type is AugmentedManifestFile, but Linear Learner requires RecordIO or CSV. The command uses `S3DataType` as `AugmentedManifestFile`, but the Linear Learner algorithm only supports `RecordIO` or `CSV` as the S3 data type. AugmentedManifestFile is used for algorithms like object detection that require additional labels. The content type `application/x-recordio` is correct for RecordIO, but since the data type is set to AugmentedManifestFile, the training job fails. The IAM role, instance type, and MaxRuntimeInSeconds are all valid and would not cause this specific error. Therefore, the most likely cause is the incorrect S3 data type, which corresponds to option A.

Answer

The IAM role does not have sufficient permissions

Answer

The instance type ml.m5.large does not support the Linear Learner algorithm

Answer

The MaxRuntimeInSeconds is too short

Question 3

During EDA, a data scientist notices that a numeric feature 'age' has values ranging from 0 to 150, but expects adult ages between 18-100. Which TWO steps should the scientist take to investigate?

Accepted Answer

Compute summary statistics (min, max, percentiles). Computing summary statistics (min, max, percentiles) helps identify the range and potential outliers in the 'age' feature. Option E is correct because a box plot visualizes the distribution and clearly shows outliers, allowing the data scientist to investigate further. Option A is incorrect because removing rows with age > 100 without understanding the context may discard valid data (e.g., errors or special cases). Option C is incorrect because log transformation changes the scale but does not help in identifying outliers; it is used to handle skewed distributions. Option D is incorrect because imputing age values outside 18-100 with the mean would distort the distribution and is not appropriate for investigating outliers; it should only be considered after understanding the nature of the outliers.

Answer

Remove all rows with age > 100

Answer

Apply log transformation to normalize the distribution

Answer

Impute age values outside 18-100 with the mean

Question 4

A data scientist is training a model using Amazon SageMaker and wants to reduce the training time. The training job uses a single GPU instance. Which THREE actions can reduce training time?

Accepted Answer

Use distributed training across multiple GPU instances.. Options A, B, and C are correct. Distributed training across multiple GPU instances (A) leverages parallelism to reduce training time. Pipe input mode (B) streams data directly from S3, reducing I/O wait time compared to File mode which downloads data first. Using a larger instance type with more GPU memory and compute (C) provides more processing power, allowing faster training. Option D is incorrect because increasing the amount of training data typically increases training time, not reduces it. Option E is incorrect because reducing batch size can lead to more iterations and longer training time, though it may sometimes affect convergence.

Answer

Increase the amount of training data.

Answer

Reduce the batch size.

Question 5

A data scientist is performing EDA and observes that a feature 'purchase_amount' has many zeros and a long tail of positive values. What type of model would be appropriate for this target variable?

Accepted Answer

Zero-inflated negative binomial regression.. Zero-inflated negative binomial regression models are designed for count data with a high frequency of zeros, which matches the 'purchase_amount' feature having many zeros and a long tail of positive values. Option B is incorrect: Log transformation does not handle the zero-inflation problem; zeros become undefined or need adjustment. Option C is incorrect: Logistic regression is for binary outcomes, not continuous or count data. Option D is incorrect: Poisson regression accommodates count data but assumes the variance equals the mean and does not handle excess zeros; zero-inflation violates this assumption.

Answer

Linear regression after log transformation.

Answer

Logistic regression on binary indicator of purchase.

Answer

Poisson regression.

Question 6

A data scientist is working on a multi-class classification problem with 10 classes. The model outputs probabilities and the scientist wants to evaluate the model's ability to rank classes correctly. Which metric is most appropriate?

Accepted Answer

Area Under the ROC Curve (AUC-ROC). The most appropriate metric for evaluating a multi-class classifier's ability to rank classes (i.e., order classes by predicted probability) is the Area Under the ROC Curve (AUC-ROC). AUC-ROC measures the model's ability to distinguish between classes across all thresholds, and for multi-class problems it can be extended using one-vs-rest or macro/micro averaging. Log loss (Option D) measures probability calibration, not ranking. F1 score (Option A) is a threshold-dependent metric suitable for binary or per-class evaluation, not overall ranking. Accuracy (Option B) is also threshold-dependent and does not consider probability ranking. Therefore, Option C (AUC-ROC) is correct.

Answer

F1 score

Answer

Accuracy

Answer

Log loss

Question 7

A machine learning engineer is analyzing a dataset and observes that the distribution of a continuous feature is heavily right-skewed. Which transformation is most likely to make the distribution approximately normal?

Accepted Answer

Log transformation. A log transformation (C) is most appropriate for heavily right-skewed continuous data because it compresses the long right tail and can make the distribution approximately normal. Square root (A) is less effective for severe skewness. Exponential (B) would amplify the skewness. Box-Cox with lambda = 0 (D) is equivalent to log, but since log is explicitly given and commonly known, option C is the direct and correct choice.

Answer

Square root transformation

Answer

Exponential transformation

Answer

Box-Cox transformation with lambda = 0

Question 8

A data scientist is performing EDA on a dataset with a binary target variable. Which THREE techniques can help assess the relationship between a continuous feature and the target?

Accepted Answer

KDE plot grouped by target. Box plots (comparing distributions for each class), histograms (overlay or side-by-side), and KDE plots (probability density) are all effective for visualizing the relationship between a continuous feature and a binary target. Option A (scatter plot) requires two continuous variables. Option D (bar chart) is for categorical features.

Answer

Scatter plot against another continuous feature

Answer

Bar chart of feature values

Question 9

A data engineering team is designing a data pipeline that processes streaming data from Amazon Kinesis Data Streams using AWS Lambda. The team notices that some records are being processed multiple times (duplicates). Which TWO steps should the team take to ensure exactly-once processing?

Accepted Answer

Design the Lambda function to be idempotent.. Options A and B are correct. Making the Lambda function idempotent ensures that processing the same record multiple times does not cause duplicates downstream. Using a unique identifier per record and storing processed IDs in an external store like DynamoDB allows deduplication by checking if a record has already been processed. Option C is incorrect because increasing batch size does not prevent duplicates and may increase the chance of processing failures. Option D is incorrect because KPL provides exactly-once delivery to Kinesis Data Streams, not from the stream to Lambda, so deduplication is still needed. Option E is incorrect because disabling retries can lead to data loss without guaranteeing exactly-once processing.

Answer

Increase the batch size to reduce the number of invocations.

Answer

Use Kinesis Producer Library (KPL) to guarantee exactly-once delivery.

Answer

Disable retries on the Lambda function.

Question 10

Refer to the exhibit. A data scientist is unable to run an Amazon Athena query on data in `my-bucket`. The IAM policy shown is attached to the user. What is the most likely reason for the failure?

Accepted Answer

Athena needs s3:PutObject permission to write results.. Athena requires `s3:PutObject` permission to write query results to an output location. Without this permission, the query fails. Option A is not the issue because the bucket exists and is accessible; Option C is not necessarily relevant; Option D is incorrect because the bucket does exist (otherwise the user couldn't even attempt the query).

Answer

The ListBucket action is not granted.

Answer

The data is encrypted with SSE-C.

Answer

The bucket does not exist.