Exploratory Data Analysis

A data scientist is exploring a dataset of customer transactions. The dataset has 1 million rows and 50 columns. The target variable is a binary flag indicating whether a customer churned. The data scientist runs a correlation matrix on all numerical features and finds that two features have a correlation coefficient of 0.98. Which action should be taken to improve model performance?

A team is building a regression model to predict house prices. The dataset includes a column 'zip_code' with 100 unique values. The data scientist one-hot encodes this column, resulting in 100 new binary columns. The model shows poor performance on a validation set. What is the most likely cause?

During exploratory data analysis, a data scientist plots the distribution of a numerical feature and observes a heavy right skew. The feature has many outliers at the high end. Which transformation is most appropriate to reduce skewness?

A data scientist is analyzing a dataset with missing values in 30% of the rows for the 'age' column. The data scientist decides to impute the missing values with the median of the observed 'age' values. What is a potential drawback of this approach?

A data scientist is exploring a dataset with 500 features and 10,000 samples. The data scientist computes the pairwise correlation matrix and finds that many features have correlations above 0.9. The data scientist wants to reduce the dataset to 50 features while preserving as much variance as possible. Which technique should be used?

A data scientist is performing exploratory data analysis on a dataset with 10,000 rows and 20 features. The target variable is binary. The data scientist observes that one feature has 15% missing values. Which TWO actions are appropriate to handle this missing data? (Choose TWO.)

A data scientist is analyzing a dataset of customer reviews. The dataset contains a text column 'review' and a numerical rating from 1 to 5. The data scientist wants to create features for sentiment analysis. Which THREE preprocessing steps should be applied to the text data before feature extraction? (Choose THREE.)

A data scientist is analyzing a dataset with a target variable that is heavily imbalanced (e.g., 99% negative class, 1% positive class). Which exploratory data analysis technique is most appropriate to understand the relationship between features and the target before modeling?

During EDA, a data scientist notices that a feature has a high proportion of missing values (e.g., 70%). The feature is continuous and expected to be important based on domain knowledge. What is the best approach to handle this?

A data scientist is performing EDA on a dataset with 1,000 features and 10,000 rows. The target variable is binary. After checking for multicollinearity, the scientist finds many pairs of features with correlation > 0.95. Which action should be taken to prepare the data for modeling?

A data scientist is analyzing a time-series dataset and wants to check for stationarity. Which EDA technique is most appropriate?

During EDA, a data scientist creates a scatter matrix of numerical features and notices that some features have a funnel-shaped pattern (variance increases with the mean). What is the appropriate transformation to stabilize variance?

Which TWO of the following are appropriate techniques for detecting outliers in a univariate continuous feature?

Which THREE of the following are best practices when performing exploratory data analysis on a dataset with both numerical and categorical features?

A data scientist is performing exploratory data analysis on a dataset containing customer transactions. The dataset has 1 million rows with 50 features, including numerical and categorical variables. The goal is to identify patterns and potential data quality issues before building a model. Which approach should the data scientist take to efficiently explore the data?

A data scientist is trying to read a CSV file from S3 bucket 'my-bucket' with key 'training/data.csv' using an IAM role with the attached policy shown in the exhibit. The read operation fails with an Access Denied error. What is the most likely cause?

A data scientist is building a fraud detection model using a dataset of 500,000 credit card transactions. The dataset contains 20 features, including transaction amount, merchant category, time since last transaction, and customer age. The target variable 'is_fraud' has 0.1% positive examples. Initial EDA reveals that the transaction amount distribution is highly skewed with a long tail. Also, there are missing values in the 'customer_age' field (5% missing). The data scientist needs to prepare the data for training a binary classifier. Which combination of preprocessing steps should the data scientist apply to address these issues and improve model performance? (Select TWO.)

A data scientist is analyzing a dataset with 100 features and 10,000 observations. The target variable is binary (0/1). Initial exploratory data analysis reveals that many features have missing values, high correlation with each other, and non-normal distributions. The data scientist wants to identify the most important features for predicting the target while reducing dimensionality. Which TWO actions should the data scientist take? (Choose two.)

Refer to the exhibit. A data scientist ran an S3 Select query on a large CSV file stored in Amazon S3. The output shows only 2 records returned, but the data scientist expected thousands. The file size is 10 GB. What is the MOST likely reason for the small result set?

A machine learning engineer is working on a regression problem to predict house prices. The dataset contains 500,000 rows and 20 features, including 'sqft_living', 'bedrooms', 'bathrooms', 'floors', 'waterfront', 'view', 'condition', 'grade', 'yr_built', 'zipcode', and 'lat'. After performing exploratory data analysis, the engineer notices that the 'sqft_living' feature has a right-skewed distribution with a long tail. The 'zipcode' feature is categorical with 70 unique values. The 'lat' feature is continuous. The engineer wants to prepare the data for a linear regression model. Which action should the engineer take to improve model performance?

Drag and drop the steps to create a data processing job using Amazon SageMaker Processing in the correct order.

Drag and drop the steps to use Amazon SageMaker Feature Store for feature engineering in the correct order.

Match each SageMaker feature to its description.

Match each ML model evaluation concept to its definition.

A data scientist is analyzing a dataset with 500 features and 10,000 samples. After running a correlation matrix, they find that many feature pairs have correlation >0.95. What is the most appropriate next step to improve model performance?

A machine learning engineer is performing exploratory data analysis on a dataset containing customer transactions. They notice that the target variable is highly imbalanced: 99% of samples belong to class 0 and 1% to class 1. Which technique should they use to address this imbalance before training a classification model?

A data scientist is analyzing a dataset with missing values. The missing data mechanism is missing at random (MAR). Which imputation method is most appropriate to preserve relationships between variables?

Which TWO actions are appropriate when dealing with outliers in a dataset during exploratory data analysis? (Select TWO.)

Which THREE techniques are commonly used for feature engineering in exploratory data analysis? (Select THREE.)

Which TWO statements about handling categorical variables in exploratory data analysis are correct? (Select TWO.)

A data scientist runs the following AWS CLI command: aws s3api head-object --bucket my-bucket --key data.csv The output is: { "AcceptRanges": "bytes", "LastModified": "2021-08-21T12:00:00+00:00", "ContentLength": 1048576, "ETag": "\"d41d8cd98f00b204e9800998ecf8427e\"", "ContentType": "text/csv", "Metadata": {} } What can be concluded from the output?

A data scientist is troubleshooting access to an S3 bucket. The following IAM policy is attached to their role. What is the likely result when they try to list objects in the 'confidential' folder? ```json { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": "s3:ListBucket", "Resource": "arn:aws:s3:::example-bucket" }, { "Effect": "Deny", "Action": "s3:*", "Resource": "arn:aws:s3:::example-bucket/confidential/*", "Condition": { "StringNotEquals": { "aws:SourceVpce": "vpce-12345678" } } } ] } ```

A data scientist is using Amazon Athena to query a CSV file stored in S3. The query fails with the error: 'HIVE_CANNOT_OPEN_SPLIT: Number of fields in line 1502 does not match number of fields in the first line.' What is the most likely cause?

A data scientist is reviewing a dataset and notices that the distribution of a numerical feature is heavily right-skewed with a long tail. Which visualization is most appropriate to assess the distribution?

A data scientist is working with a dataset that includes a 'timestamp' column. They want to create features that capture seasonality. Which feature engineering approach is most appropriate?

During exploratory data analysis, a data scientist discovers that a feature has a variance of 0.01, while other features have variances around 1.0. Which action should be taken?

A data scientist wants to understand the relationship between a categorical feature with 3 levels and a continuous target variable. Which visualization is most appropriate?

A data scientist is analyzing a dataset and finds that the target variable has a bimodal distribution. Which preprocessing step is most appropriate before modeling?

A data scientist is performing exploratory data analysis on text data. They want to identify the most common terms and their frequencies. Which approach should they use?

A data scientist is analyzing a dataset with missing values in several features. The dataset is large (10 million rows) and stored in an S3 bucket as CSV files. The scientist wants to use AWS Glue to catalog the data and then use Amazon Athena to query it. However, the missing values are causing errors in downstream machine learning models. Which approach should the scientist take to handle missing values during exploratory data analysis?

A machine learning engineer is performing exploratory data analysis on a dataset containing customer transaction records. The dataset includes a column 'transaction_date' with timestamps. The engineer wants to derive features such as day of the week, hour, and month for modeling. Which AWS service can be used directly to extract these features without writing custom code?

A data scientist is exploring a dataset with 500 features and 100,000 observations for a regression problem. The scientist notices that many features are highly correlated with each other. Which technique should the scientist use to reduce multicollinearity and improve model interpretability during exploratory data analysis?

A machine learning team is analyzing a dataset with a target variable that is highly imbalanced (99% negative class, 1% positive class). They want to understand the distribution and relationships before modeling. Which exploratory data analysis technique is most appropriate to visualize the imbalance and guide resampling strategy?

A data analyst is using Amazon SageMaker Studio to perform exploratory data analysis on a dataset stored in S3. The analyst wants to generate summary statistics and visualizations quickly. Which built-in feature of SageMaker Studio should the analyst use?

A data scientist is working with a dataset containing geospatial coordinates (latitude and longitude) of customer locations. The scientist wants to engineer features such as distance to the nearest store, and cluster customers into regions. Which AWS service is best suited for performing geospatial analysis and clustering during exploratory data analysis?

A machine learning engineer is analyzing a dataset with a mix of categorical and numerical features. The engineer wants to understand the correlation between categorical features and the target variable. Which statistical test is most appropriate for measuring association between a categorical feature and a binary target?

A data analyst is exploring a dataset and wants to identify outliers in a numerical feature. Which visualization technique is most effective for detecting outliers?

A team is performing exploratory data analysis on a dataset containing 10 million records stored in Amazon S3. They want to sample the data efficiently to build a representative subset for initial modeling. Which sampling method should they use to minimize bias and ensure the sample reflects the population distribution?

A data scientist is using Amazon SageMaker to perform exploratory data analysis on a dataset with missing values and outliers. Which TWO actions should the scientist take to understand the data quality? (Choose TWO.)

A machine learning engineer is analyzing a dataset with a large number of features (p >> n). The engineer suspects that many features are irrelevant. Which THREE methods are suitable for feature selection during exploratory data analysis? (Choose THREE.)

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.)

A data scientist is analyzing a dataset with 10,000 rows and 50 columns. The target variable is binary. Which technique is most appropriate for identifying the most important features for predicting the target?

During exploratory data analysis on a dataset with 1 million rows, a data scientist notices that the distribution of the target variable is highly imbalanced (99% class A, 1% class B). Which technique should be applied to address this imbalance before model training?

A data scientist wants to visualize the correlation between a continuous feature and a binary target variable. Which plot is most appropriate?

A data scientist is exploring a dataset and finds that the variance of a feature is 0. What should be done with this feature?

A company stores customer transaction data in Amazon S3. A data scientist needs to perform exploratory data analysis using Amazon SageMaker. The dataset is 500 GB in CSV format. Which approach is most cost-effective and time-efficient for initial data profiling?

A data scientist is analyzing a dataset and notices that the distribution of a continuous feature is heavily right-skewed. Which transformation is most likely to make the distribution more symmetric?

During EDA, a data scientist finds that two features have a Pearson correlation coefficient of 0.95. What is the primary concern when using these features together in a linear regression model?

A data scientist is performing EDA on a dataset of customer churn. The dataset includes a categorical feature 'Region' with 100 unique values. What is the best way to encode this feature for a tree-based model?

Which TWO of the following are appropriate techniques for detecting outliers in a univariate continuous dataset? (Select TWO.)

Which THREE of the following are common issues that can be identified during exploratory data analysis? (Select THREE.)

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

A data scientist is setting up an IAM policy for a SageMaker notebook instance that needs to read and write data in the 'training/' folder of an S3 bucket, and also list objects in the bucket. Does the policy satisfy the requirements?

A data scientist receives the above error during model training. What is the most likely cause?

A data scientist is performing exploratory data analysis on a dataset containing customer transactions. The dataset has a column 'transaction_date' with timestamps in string format. Which AWS service can be used to parse the timestamps and extract features like day of week and hour?

A machine learning engineer is analyzing a dataset with high cardinality categorical features. They want to reduce the number of categories by grouping rare categories into an 'Other' category. Which Amazon SageMaker processing job capability is best suited for this task?

A data analyst needs to visualize the distribution of a numerical feature in a dataset. Which AWS service can be used to create a histogram directly from data stored in S3 without writing code?

A team is exploring a dataset with missing values in multiple columns. They want to decide whether to drop rows or impute values. Which approach is most appropriate for exploratory data analysis?

A data scientist is performing EDA on a large dataset (10 TB) stored in S3. They need to compute summary statistics for each column. Which approach is most cost-effective and efficient?

A data engineer is exploring a dataset with a timestamp column and wants to resample the data to a consistent 1-hour frequency. The data is irregularly spaced. Which approach is most efficient using AWS services?

A data analyst wants to check for duplicate rows in a dataset stored in S3. Which AWS service can be used to run a SQL query to count duplicates without moving the data?

Which TWO of the following are appropriate techniques for handling missing data during exploratory data analysis? (Select TWO.)

Which THREE of the following are best practices for feature engineering during EDA? (Select THREE.)

Which TWO AWS services can be used to visualize data distributions as part of exploratory data analysis? (Select TWO.)

An Athena query SELECT COUNT(*) FROM table WHERE col1 IS NULL returns the value 5000. What does this value represent?

The exhibit shows an IAM policy for a SageMaker notebook. A data scientist wants to use the notebook to run an Athena query and then load the results into a pandas DataFrame. Which action is NOT possible with this policy?

The exhibit shows a data quality report for a column named 'age'. Which potential data issue should be investigated further?

A data scientist is performing exploratory data analysis on a dataset with missing values. The dataset contains a column 'income' with 20% missing values. The income distribution is right-skewed. Which imputation method is most appropriate to preserve the skewness?

A data scientist is analyzing a dataset with many categorical features. The target variable is binary. Which statistical test should be used to assess the association between each categorical feature and the target?

A data scientist is exploring a dataset with a column 'transaction_date'. They want to create features for day of week and month. What is the correct AWS service to schedule a recurring ETL job for this transformation?

A company stores sensor data in Amazon S3. A data scientist wants to explore the data using SQL without moving it. Which AWS service should they use?

During EDA, a data scientist notices that a numeric feature 'age' has outliers beyond 3 standard deviations. What is the most appropriate first step?

A data scientist is performing EDA on a dataset with 100 features. They want to identify which features are most predictive of the target using a model-agnostic method. Which technique should they use?

Which TWO statements about handling missing data during EDA are correct? (Select TWO.)

Which THREE are common techniques for detecting outliers in a univariate dataset? (Select THREE.)

Which TWO are appropriate visualizations for exploring the distribution of a single numeric variable? (Select TWO.)

A data scientist runs the above AWS CLI command and gets the output. The object size is 1 GB. They try to open the CSV file in Amazon Athena but get an error. What is the most likely cause?

A data scientist creates the above IAM policy and attaches it to a role used by an Amazon SageMaker notebook instance. When trying to save a file to the S3 bucket, the operation fails. What is the missing permission?

A data scientist is analyzing server logs stored in Amazon CloudWatch Logs. The above snippet shows three log entries. They want to count the number of 500 errors per minute using CloudWatch Logs Insights. Which query should they use?

A data scientist is analyzing a dataset with 100 features and 10,000 samples. The target variable is highly imbalanced (1% positive class). Which exploratory data analysis step is most critical before model training?

A company uses Amazon SageMaker to train a regression model. After training, the data scientist notices that the training loss decreases but validation loss increases after a few epochs. Which EDA technique could have helped predict this behavior?

A machine learning engineer is evaluating a dataset for building a fraud detection model. The dataset has 1 million transactions, but only 500 are fraudulent. The engineer wants to understand the distribution of fraudulent vs. non-fraudulent transactions over time. Which EDA visualization is most suitable?

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?

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?

A data scientist uses Amazon SageMaker Data Wrangler to explore a dataset. The target column is 'price' (continuous). Which EDA analysis would best help decide between linear regression and tree-based models?

A machine learning engineer is analyzing a text classification dataset with 50,000 documents. Which EDA step is most important to understand the vocabulary size and frequency distribution?

A data scientist is performing EDA on a dataset with a timestamp column. They want to detect seasonality. Which visualization is most appropriate?

A data scientist is exploring a dataset with 50 features. Which TWO EDA techniques are most effective for detecting multicollinearity?

A machine learning team is analyzing a dataset with 10,000 rows and 200 features. They suspect data leakage due to time-based features. Which THREE EDA checks should they perform?

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?

A data scientist is analyzing a dataset with 500 features and 10,000 rows. The target variable is binary. After training a logistic regression model, the coefficients show many non-zero values but the model has low accuracy on the test set. Which EDA step should the data scientist perform next to improve model performance?

An ML engineer is performing EDA on a dataset of customer transactions. The dataset has 1 million rows and 20 columns, including a 'transaction_amount' column. The engineer notices that 5% of the transaction amounts are negative, which are data entry errors. The rest are positive. Which approach is most appropriate for handling these negative values during EDA?

A data scientist is exploring a dataset and wants to understand the distribution of a continuous feature. Which visualization is most appropriate for identifying skewness and potential outliers?

A machine learning team is building a model to predict customer churn. The dataset has 20 features and 50,000 rows. After initial EDA, they notice that the target variable 'churn' is highly imbalanced (5% churn, 95% non-churn). Which EDA step should the team prioritize to address this imbalance before model training?

A data scientist is performing EDA on a dataset with both numerical and categorical features. Which technique is best for detecting multicollinearity among numerical features?

An ML team is analyzing a time series dataset of daily website traffic. They notice a pattern where traffic spikes every Sunday. Which EDA technique should they use to confirm this seasonality?

A data scientist is working with a dataset containing text reviews. The goal is to classify sentiment. During EDA, they compute the word frequency distribution. They notice that the most frequent words are common stop words like 'the', 'and', 'a'. Which action should they take to improve the feature representation for modeling?

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?

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.)

During EDA of a dataset for a regression problem, a data scientist notices that the target variable has a right-skewed distribution. Which THREE transformations are appropriate to address this skewness? (Choose THREE.)

A data scientist is exploring a dataset with categorical variables. Which TWO EDA techniques are appropriate for understanding the relationship between a categorical feature and a continuous target? (Choose TWO.)

A data scientist is exploring log files stored in S3. They ran the above AWS CLI command. What does the output indicate about the data, and what EDA step should be taken next?

A data scientist is trying to upload a CSV file to an S3 bucket using the AWS CLI without specifying server-side encryption. The upload fails with an AccessDenied error. Based on the bucket policy exhibit, what is the most likely cause?

A data scientist is analyzing application logs in JSON format. Based on the exhibit, which EDA insight is most valuable for troubleshooting?

A data scientist is analyzing a dataset with missing values. Which technique is most appropriate for imputing missing values in a numerical feature that follows a normal distribution?

A machine learning engineer is exploring a dataset with 50 features. Some features are highly correlated. Which technique should the engineer use to reduce dimensionality while preserving variance?

A data scientist is analyzing a dataset with a large number of categorical features. The target variable is binary. Which technique should the scientist use to assess the relationship between each categorical feature and the target?

A data scientist is visualizing the distribution of a numerical feature that is heavily right-skewed. Which visualization technique is most appropriate?

A company has a dataset with a timestamp column and multiple numerical metrics. They want to identify seasonality and trends. Which AWS service is best suited for this analysis?

A data scientist is analyzing a dataset with a binary target variable. The dataset is highly imbalanced (99% negative class). Which metric is most appropriate for evaluating the model's performance during exploratory data analysis?

A data scientist wants to understand the distribution of a categorical feature with 100 unique values. Which visualization is most appropriate?

A data scientist is working with a dataset that contains a feature with many outliers. Which transformation should the scientist apply to reduce the impact of outliers?

A data scientist is performing exploratory data analysis on a dataset with mixed data types (numerical, categorical, text). The goal is to identify clusters of similar records. Which technique is most appropriate?

Which TWO of the following are common techniques for detecting outliers in a numerical feature?

Which THREE of the following are appropriate data visualization techniques for exploring the relationship between two numerical variables?

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

A data engineer is using Amazon SageMaker Data Wrangler to perform exploratory data analysis on a large dataset stored in S3. The analysis reveals high cardinality in a categorical feature with over 1 million unique values. What is the best approach to handle this before training a model?

During exploratory data analysis, a data scientist observes a strong correlation (r=0.95) between two numeric features. The model to be trained is a linear regression. What is the most appropriate action?

A data scientist is analyzing a dataset with a target variable that is binary (0/1). Which visualization is most appropriate to explore the relationship between a continuous feature and the target?

In exploratory data analysis, a data scientist notices that the distribution of a feature 'income' is heavily right-skewed. Which transformation is most appropriate to reduce skewness?

A data scientist is exploring a dataset with 1,000 features and only 200 samples. The goal is to build a binary classifier. Which technique should be used first during exploratory data analysis to reduce dimensionality and avoid overfitting?

During EDA, a data scientist finds that a categorical feature 'city' has 500 unique values but only 10 cities account for 90% of the data. What is a recommended way to handle the rare categories?

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?

During EDA, a data scientist plots the distribution of a feature and sees a bimodal pattern. What does this likely indicate?

Which TWO are appropriate techniques for detecting outliers in a dataset during exploratory data analysis?

Which THREE are valid reasons to perform feature scaling during exploratory data analysis?

Which TWO are common steps in exploratory data analysis?

A data scientist is analyzing a dataset with missing values in a numeric column. The missing rate is 30% and the data is not missing completely at random. Which imputation method should the data scientist avoid to minimize bias?

A machine learning engineer is exploring a dataset with 500 features and 10,000 samples. To reduce dimensionality for visualization, which technique is most suitable if the goal is to preserve global data structure?

A data scientist is examining a dataset for a binary classification problem. The target variable has a 1:1000 imbalance. Which technique should be used to assess model performance during exploratory data analysis?

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?

A team is analyzing a dataset with many categorical features. They notice that one feature has 1,000 unique values but a long tail where most values appear only once. Which encoding method is most appropriate to avoid overfitting?

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?

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?

A data scientist is performing EDA on a dataset containing text reviews. To understand the most common words, the data scientist generates a word cloud. Which preprocessing step is most important to ensure the word cloud reflects meaningful content?

A data analyst is exploring a dataset and notices that the target variable has a Poisson distribution. Which type of model is most appropriate for this target?

Which TWO techniques are appropriate for detecting outliers in a univariate numeric dataset?

Which THREE of the following are common causes of multicollinearity in a linear regression model?

Which TWO of the following are benefits of feature scaling for machine learning algorithms?

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?

A data engineer is preparing a dataset for training a binary classification model. The target variable is highly imbalanced (95% negative, 5% positive). The engineer needs to split the data into training and test sets while maintaining the class distribution in both sets. Which method should the engineer use?

A machine learning engineer is analyzing feature distributions in a dataset and notices that one feature has a long tail. Which transformation is most appropriate to reduce skewness and make the distribution more normal?

A data scientist is performing exploratory data analysis on a high-dimensional dataset with 500 features. The scientist wants to visualize the data in 2D to check for clusters. Which dimensionality reduction technique should the scientist use that preserves global structure and is computationally efficient for large datasets?

A data analyst is examining a scatter plot of two variables and notices a strong positive correlation. Which of the following is a valid conclusion?

A data scientist is working with a dataset containing 10,000 observations and 100 features. The scientist wants to detect outliers in the dataset. Which method is most appropriate for outlier detection in a high-dimensional space?

A data engineer is building a data pipeline that aggregates customer transaction data. The engineer notices that some transactions have duplicate entries due to a system error. Which approach should the engineer use to identify and remove duplicates based on a unique transaction ID?

A machine learning team is analyzing feature importance in a dataset with many categorical features. They plan to use a tree-based model. Which encoding method should they use to handle high-cardinality categorical features without creating too many dummy variables?

A data analyst wants to understand the distribution of a continuous variable. Which visualization is most appropriate for this purpose?

A data scientist is analyzing a dataset and finds that two features have a Pearson correlation coefficient of 0.95. Which TWO actions should the data scientist consider? (Choose two.)

A data engineer is performing exploratory data analysis on a dataset with 1 million rows and 50 features. The engineer wants to identify missing values and outliers. Which THREE approaches should the engineer use? (Choose three.)

A data scientist is exploring a dataset with a binary target variable. Which TWO metrics are appropriate for evaluating the balance of the target classes? (Choose two.)

A data science team at a financial services company is building a fraud detection model using a dataset of credit card transactions. The dataset contains 10 million rows and 20 features, including transaction amount, merchant category, time since last transaction, and customer ID. The target variable 'is_fraud' is highly imbalanced: only 0.1% of transactions are fraudulent. The team is performing exploratory data analysis (EDA) on a sample of 100,000 rows. They compute the correlation matrix and find that 'transaction amount' has a correlation of 0.02 with 'is_fraud'. They also plot the distribution of 'transaction amount' and see that it is heavily right-skewed with a long tail. The team wants to understand the relationship between 'transaction amount' and fraud more deeply before feature engineering. They have access to AWS SageMaker and can run processing jobs. Which course of action is most appropriate?

A data scientist is performing exploratory data analysis on a dataset with missing values. They want to understand the distribution of each feature and identify outliers. Which AWS service can be used to create visualizations such as histograms and box plots without writing any code?

During exploratory data analysis, a data scientist notices that the target variable is highly imbalanced. Which technique should be used to address this issue before training a classification model?

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?

A data scientist is performing EDA on a time-series dataset and observes a strong upward trend and seasonal patterns. The scientist needs to make the data stationary for modeling. Which transformation should be applied?

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?

During EDA, a data scientist discovers that two numerical features have a Pearson correlation coefficient of 0.95. Which action should the scientist take to avoid multicollinearity in a linear regression model?

A data scientist is exploring a dataset and wants to check for missing values. Which method is most appropriate to identify the percentage of missing values per column?

A data scientist is performing EDA on a dataset with many features. They suspect some features are redundant due to high pairwise correlations. Which technique can help identify groups of correlated features?

A data scientist is exploring a large dataset (10 TB) stored in Amazon S3. The dataset is in CSV format and has many columns. The scientist wants to quickly compute summary statistics (mean, min, max, count) for each column without moving the data. Which approach is most cost-effective and efficient?

A data scientist is performing EDA on a dataset with mixed data types (numerical and categorical). Which TWO visualizations are most appropriate for understanding the distribution of categorical features?

A data scientist is analyzing a dataset and suspects the presence of outliers that could affect the mean and standard deviation. Which TWO methods are robust to outliers for measuring central tendency and dispersion?

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?

A data scientist is working with a dataset containing customer transaction records stored in Amazon S3 as CSV files. The dataset has 500 columns and 2 million rows. The scientist wants to perform EDA to understand data types, missing values, and summary statistics for each column. They need to do this quickly and without writing custom code. The scientist has access to AWS Glue DataBrew and Amazon SageMaker Data Wrangler. Which approach should the scientist take?

A data scientist is analyzing a dataset with a large number of missing values in several columns. The dataset is stored in an Amazon S3 bucket and is about 5 TB in size. The scientist wants to understand the pattern of missingness (e.g., is it missing completely at random, missing at random, or not missing at random) before deciding on an imputation strategy. The scientist has access to AWS Glue DataBrew and Amazon SageMaker Studio. Which approach should the scientist take to best understand the missing data patterns?

A data scientist is performing EDA on a dataset that contains customer demographics and purchase history. The dataset has a column 'age' with some values that are negative or unreasonably high (e.g., 200). The scientist wants to identify and handle these outliers. The scientist is using a SageMaker notebook with pandas. Which approach should the scientist take to effectively handle these outliers?

A data scientist is analyzing a dataset with missing values in several columns. The dataset is stored in an S3 bucket. What is the most efficient method to identify the percentage of missing values per column using AWS services?

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?

A data scientist is analyzing a dataset with 500 features and 100,000 observations. The target variable is binary. The dataset contains highly correlated features and some categorical variables with high cardinality. Which combination of techniques should the data scientist use to reduce dimensionality while preserving interpretability for EDA?

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?

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?

A data engineer is performing EDA on a dataset with 1 million rows and 200 columns. The dataset is stored in S3 as CSV files. The engineer notices that some columns have a high proportion of zeros. What is the best approach to determine if these zeros represent missing data or actual zero values?

A data scientist is analyzing a dataset with a skewed target variable for a regression problem. During EDA, the scientist wants to transform the target variable to approximate a normal distribution. Which transformation should the scientist apply first?

A data analyst is using Amazon QuickSight to explore a dataset with 10 million rows. The analyst wants to create a histogram of a numerical column. However, the query is taking too long. Which action should the analyst take to improve performance without losing accuracy?

A data scientist is performing EDA on a dataset with mixed data types (numerical, categorical, text). The dataset is stored in S3. Which TWO AWS services can be used to directly perform statistical summaries and visualizations without writing custom code?

A data scientist is analyzing a dataset with several categorical features and a binary target. The scientist wants to check for association between each categorical feature and the target. Which THREE statistical tests are appropriate?

A data engineer is exploring a large dataset in Amazon Athena. The dataset is partitioned by date and stored in Parquet format. The engineer wants to check the number of distinct values in a column for a specific date range. Which THREE practices reduce query cost and improve performance?

The exhibit shows an Athena query result from a table. What is the output of the query?

A data scientist is working on a customer churn prediction project for a telecom company. The dataset contains 50,000 records with 25 features, including 'tenure' (number of months customer stayed), 'monthly_charges', 'total_charges', 'contract_type' (month-to-month, one year, two year), 'payment_method', and a target 'churn' (Yes/No). The data is stored in an S3 bucket as a single CSV file. The scientist uses Amazon SageMaker Data Wrangler to perform EDA. After importing the data, the scientist notices that the 'total_charges' column has many missing values (about 20% of rows). The scientist suspects that missing values occur only for customers with tenure = 0 (new customers). After verifying that suspicion, the scientist wants to handle the missing values appropriately. Which course of action should the scientist take?

A data engineer is performing EDA on a dataset containing user activity logs from a mobile app. The dataset has 10 million rows and includes columns: 'user_id', 'event_type', 'timestamp', 'device_type', and 'session_duration'. The engineer uses Amazon Athena to query the data stored in S3 as CSV files. The engineer runs a query to find the average session_duration per device_type, but the query takes over 5 minutes and scans 100 GB of data. The engineer wants to reduce query cost and improve performance for future EDA. The dataset is not partitioned, and the engineer anticipates frequent queries filtering on 'timestamp' and 'device_type'. Which action will most effectively reduce data scanned?

A data scientist is analyzing a dataset of customer reviews for a retail company. The dataset contains text reviews, star ratings (1-5), and customer metadata. The scientist wants to perform sentiment analysis to classify reviews as positive or negative. During EDA, the scientist uses Amazon SageMaker Data Wrangler to visualize the distribution of star ratings and notices that 90% of reviews are 4 or 5 stars, while only 2% are 1 star. The scientist is concerned about class imbalance. Which approach should the scientist take to address the imbalance before modeling?

A machine learning engineer is analyzing a dataset with 500 features and suspects multicollinearity. Which TWO techniques can help identify and address multicollinearity during exploratory data analysis? (Choose TWO.)

A data scientist is performing exploratory data analysis on a time-series dataset of website traffic. The dataset contains hourly page views for the past two years. The scientist wants to analyze seasonality and trends. Which THREE techniques are appropriate for this analysis? (Choose THREE.)

A data scientist is performing exploratory data analysis on a large dataset stored in Amazon S3 (100 GB, CSV format, 500 columns). The dataset contains customer transaction records with features such as transaction amount, timestamp, customer ID, and numerous categorical variables (e.g., product category, payment method, location). The scientist wants to understand the distribution of transaction amounts across different product categories and identify any outliers. They have an Amazon SageMaker notebook instance with a ml.t3.medium instance and are using pandas. However, when trying to load the entire dataset into a DataFrame using pd.read_csv('s3://bucket/data.csv'), the notebook crashes with a memory error. Additionally, the scientist suspects that some categorical columns have high cardinality (e.g., product category has thousands of unique values), and there are missing values in several columns. What is the MOST efficient approach to perform the EDA without modifying the original dataset or using additional AWS services? Options: A) Use the SageMaker SDK to launch a parallel processing job with PySpark and read the data into a Spark DataFrame, then compute statistics and visualize with matplotlib. B) Use pandas with chunksize parameter to iterate through the dataset in chunks, compute per-chunk statistics, and aggregate results; for high-cardinality columns, use value_counts() with dropna=False and then plot the top 20 categories. C) Use the S3 Select API to filter rows and columns before loading into pandas, reducing the data size; then use pandas for EDA. D) Use SageMaker Data Wrangler to import the dataset, create a flow to handle missing values and reduce cardinality, and export a sample to the notebook for analysis.

A data scientist is exploring a dataset with 10 features and observes that the correlation between feature A and feature B is 0.98. Which action should be taken to address multicollinearity before training a linear regression model?

A machine learning engineer trains a binary classifier on an imbalanced dataset where the positive class represents 1% of the data. After training, the model achieves 99% accuracy but only 10% recall on the positive class. Which metric should the engineer focus on to evaluate the model's performance on the minority class?

During EDA, a data scientist plots the distribution of a numeric feature and observes that it is right-skewed. The feature will be used as input to a linear model. Which transformation should the data scientist apply?

A data scientist has a dataset with 500 features and wants to reduce dimensionality for visualization. Which technique is most appropriate for identifying the two components that capture the most variance?

A data engineer runs a SQL query on Amazon Athena to explore a dataset stored in S3 as CSV. The query returns zero rows for a column that should have numeric values. Which step should the engineer take to diagnose the issue?

A data scientist is performing EDA on a dataset with missing values in 3 of 20 features. The missing rate is 5% for each feature. The scientist wants to preserve as much data as possible while avoiding bias. Which imputation strategy is most appropriate?

A data scientist uses Amazon SageMaker Data Wrangler to explore a dataset and notices that the target variable is highly imbalanced. Which technique should the data scientist apply to balance the dataset before training?

During EDA, a data scientist finds that a numeric feature has many outliers. The feature will be used in a linear regression model. Which approach should the scientist take to handle the outliers?

A data scientist examines a dataset with 100 features and suspects that some features are redundant due to high pairwise correlations. Which EDA technique should the scientist use to systematically identify groups of highly correlated features?

Which TWO actions should a data scientist take when exploring a dataset that contains missing values and outliers? (Select TWO.)

Which THREE techniques are commonly used in exploratory data analysis to understand the relationships between features and the target variable? (Select THREE.)

Which TWO statements about handling missing data during exploratory data analysis are correct? (Select TWO.)

A data scientist is exploring a dataset with 10 million rows and 500 features. The target variable is binary. The dataset is stored in an Amazon S3 bucket. The data scientist wants to quickly identify which features have the highest correlation with the target variable. Which approach is MOST efficient?

A machine learning engineer is performing exploratory data analysis on a dataset containing customer transaction records. The dataset has missing values in the 'age' column and outliers in the 'amount' column. Which combination of techniques should the engineer use to handle these issues during EDA?

A data scientist is analyzing a dataset with 1 million records and 20 features. The target variable is continuous. The scientist wants to identify non-linear relationships between features and the target. Which technique is MOST suitable for this purpose during exploratory data analysis?

A company is preparing a dataset for training a binary classification model. The dataset has a severe class imbalance (1% positive class). The data scientist wants to understand the impact of this imbalance on model performance before sampling. Which exploratory analysis step is MOST critical?

A data scientist is working with a dataset that contains text reviews and a numeric rating (1-5). The goal is to predict the rating from the review text. During EDA, the scientist wants to check if there are any spelling errors or unusual characters. Which tool is BEST suited for this task?

A data scientist is exploring a dataset with 100 features. After generating pair plots, the scientist notices that many features have skewed distributions. Which transformation should the scientist apply to make the distributions more Gaussian-like for modeling?

A data scientist is performing EDA on a dataset with 500,000 rows and 10 columns. The dataset is stored in an S3 bucket as CSV files. The scientist wants to generate summary statistics (mean, median, min, max) for all numeric columns. Which service allows the quickest ad-hoc analysis without provisioning any infrastructure?

A data scientist is analyzing a dataset with 100,000 observations and 50 features. The scientist uses a Jupyter notebook on Amazon SageMaker. During EDA, the scientist runs a command to check for missing values and notices that 20% of the data in one feature is missing. The missing values are not random; they are correlated with another feature. Which imputation method is MOST appropriate?

A data scientist is performing EDA on a dataset with 1,000 features and 10,000 rows. The target is binary. The scientist wants to reduce dimensionality while preserving information related to the target. Which TWO methods are appropriate?

A data scientist is exploring a dataset with mixed data types (numeric, categorical, text). The dataset has 5 million rows. The scientist wants to understand the relationships between variables and identify potential data quality issues. Which THREE tools are suitable for this analysis?

An ML engineer runs the AWS CLI command above to list files in a training data bucket. The engineer notices that the three CSV files have different sizes but the same number of columns. What is the MOST likely cause of the size variation?

An IAM policy is attached to a data scientist's role. The scientist is trying to list objects in the 'data-bucket' using Amazon Athena. The query fails with an access denied error. What is the MOST likely reason?

A DevOps engineer runs the CloudWatch Logs Insights query shown above on the log group for an ML training job. The result shows a spike in ERROR messages at a specific hour. What should the engineer do next to identify the root cause?

A data scientist runs a SQL query on an Amazon Athena table and notices that the query scans a large amount of data. Which approach would reduce the amount of data scanned without changing the SQL logic?

A data engineer ingests streaming data into Amazon Kinesis Data Streams. The data science team needs to analyze the data using Amazon SageMaker notebooks. What is the most efficient way to provide access to the stream data for ad-hoc exploration?

A machine learning team is building a fraud detection model. The dataset is highly imbalanced (99.9% legitimate, 0.1% fraudulent). Which EDA technique is most important to apply before modeling?

A data scientist is exploring a dataset containing customer transaction records. The target variable is 'churn' (1 = churned, 0 = not churned). Which TWO actions should the scientist take to understand the data distribution and prepare for modeling?

A data scientist is analyzing a large dataset of images stored in Amazon S3. The dataset is used to train a computer vision model. Which THREE EDA steps are appropriate for this image dataset?

Refer to the exhibit. A data scientist is unable to query a table in Amazon Athena that is located in the 'my-data-bucket' S3 bucket. The IAM policy shown is attached to the scientist's role. What is the most likely reason for the failure?

Refer to the exhibit. A data scientist runs the AWS CLI command shown and gets the output. The scientist wants to create an Athena table over all log files in the 'logs/2023/' prefix, including files smaller than 1000 bytes. Which approach achieves this?

Refer to the exhibit. A data scientist is using AWS Glue ETL jobs to process data from a source database. The job logs show repeated timeout errors. Which EDA step should the scientist perform to diagnose the issue?

A data scientist is working with a dataset that contains both numerical and categorical features. The target variable is continuous. Which TWO EDA techniques should the scientist use to understand relationships between features and the target?

A data scientist is analyzing a dataset with many missing values. The scientist wants to decide on an imputation strategy. Which THREE considerations are important for choosing the imputation method?

A data scientist is exploring a dataset and wants to identify outliers in a numerical feature. The feature is not normally distributed. Which technique is robust to non-normal distributions?

A data scientist is performing EDA on a time series dataset of daily website visits. The scientist wants to identify any seasonality patterns. Which visualization is most appropriate?

Refer to the exhibit. A data scientist queries the table with 'SELECT COUNT(*) FROM mytable' in Athena and gets a result of 1000 rows. However, the scientist knows there are 1500 data files in the S3 location. What is the most likely reason for the discrepancy?

A data scientist is exploring a dataset with many features and wants to detect multicollinearity. Which technique should the scientist use?

A data scientist is analyzing a dataset with missing values in several columns. The dataset contains customer demographic information and purchase history. Which approach should the data scientist take to handle missing values without introducing bias into the dataset?

A machine learning engineer is performing exploratory data analysis on a large dataset stored in Amazon S3 using AWS Glue. The dataset contains a mix of numeric and categorical features. The engineer wants to efficiently compute summary statistics (e.g., mean, median, standard deviation) for the numeric columns. Which AWS service or feature should the engineer use to achieve this with minimal setup?

A data analyst is exploring a dataset with a target variable that is highly imbalanced. The minority class represents only 1% of the data. Which technique should the analyst use to better understand the relationships between features and the minority class?

A data scientist is analyzing a time series dataset of daily website traffic. The scientist notices a strong weekly seasonality. To better understand the underlying patterns, which decomposition method should the scientist use to separate the trend, seasonal, and residual components?

A machine learning team is working with a dataset containing high-dimensional sparse features, such as text data represented as bag-of-words. The team wants to reduce dimensionality while preserving the structure of the sparse matrix. Which technique is most appropriate for this scenario?

A data analyst is examining the distribution of a continuous variable and notices that its histogram is heavily skewed to the right. Which transformation should the analyst apply to make the distribution more symmetrical?

A data scientist is performing exploratory data analysis on a dataset with both numerical and categorical features. The scientist wants to visualize the pairwise relationships between numerical features and also see the distribution of each feature. Which type of plot should the scientist use?

A team is analyzing a dataset with many categorical features that have high cardinality (e.g., ZIP code, user ID). They want to explore relationships between these features and a continuous target variable. Which approach is most appropriate for visualizing these relationships without overwhelming the viewer?

A data analyst is investigating a dataset where the target variable is binary (0/1). The analyst wants to check for multicollinearity among the numerical features. Which statistical measure should the analyst use?

A data scientist is exploring a dataset with many features and suspects that some features are highly correlated. Which TWO methods can the scientist use to detect and handle multicollinearity before building a linear regression model?

A data scientist is analyzing a dataset with a continuous target variable and suspects that the relationship between a predictor and the target is non-linear. Which THREE techniques can the scientist use to explore and model this non-linearity?

A data analyst is performing exploratory data analysis on a dataset and notices that there are outliers in several numerical columns. Which TWO methods can the analyst use to identify outliers?

A data scientist is exploring a dataset stored as a single 2 GB object in S3. The scientist wants to read only a subset of the file (e.g., the first 1000 lines) to perform initial data inspection. Which approach should the scientist take to minimize data transfer and cost?

A data scientist is trying to list objects in an S3 bucket named 'my-bucket' using the AWS CLI command: `aws s3 ls s3://my-bucket/`. The command fails with an access denied error. The IAM policy attached to the scientist's role is shown in the exhibit. What is the most likely cause of the failure?

A data engineer is querying the AWS Glue Data Catalog table shown in the exhibit. The engineer runs an Athena query: SELECT * FROM transactions WHERE year=2023. The query returns results quickly. However, a subsequent query: SELECT * FROM transactions WHERE amount > 100 takes a long time. What is the most likely reason for the performance difference?

A data scientist is analyzing a dataset with 100 features and wants to identify which features are most correlated with the target variable. Which AWS service is most appropriate for this task?

A company is building a classification model and discovers that the target variable is imbalanced: 95% of samples belong to class A and 5% to class B. The data scientist needs to understand the distribution of numeric features for each class. Which approach is most appropriate?

A data engineer is performing exploratory data analysis on a large dataset stored in Amazon S3 (10 TB in CSV format). The dataset has 2000 columns and 50 million rows. The engineer needs to compute summary statistics (mean, median, standard deviation) for each numeric column and identify missing values. Which approach is MOST cost-effective and time-efficient?

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?

A data scientist is working with a dataset containing customer transactions. The dataset has a column named 'transaction_date' with timestamp values. The scientist wants to create new features such as day of week, hour, and whether the transaction occurred on a weekend. Which AWS service provides built-in feature engineering capabilities for datetime columns?

A data scientist is analyzing clickstream data from a website. The data is stored in Amazon S3 as JSON files, each containing nested arrays. The scientist needs to flatten the nested structures and compute user session durations. Which approach is most efficient for this EDA task?

During exploratory data analysis, a data scientist notices that a categorical feature 'city' has over 1,000 unique values. The dataset has 10,000 rows. Which technique should the scientist consider to reduce the cardinality of this feature?

A data scientist is performing EDA on a dataset with 500 features. The dataset has a mix of numeric and categorical features. The scientist wants to identify which features have a strong nonlinear relationship with the target variable. Which technique is most appropriate?

A data engineer is running an Amazon SageMaker Data Wrangler flow on a dataset with 5 million rows. The flow includes several transformations. The engineer wants to validate the data quality by checking for missing values and outliers before training. Which approach is most efficient?

Which TWO of the following are common techniques for handling missing values in a dataset during exploratory data analysis? (Select TWO.)

Which THREE of the following are valid techniques for detecting outliers in a dataset during exploratory data analysis? (Select THREE.)

Which TWO of the following are best practices for exploratory data analysis when using Amazon SageMaker Data Wrangler? (Select TWO.)

A data scientist needs to understand the distribution of a numeric feature in a dataset stored in Amazon S3. Which AWS service can be used to run a quick exploratory query without setting up a server?

A data scientist is analyzing a dataset with 10 million rows and 50 columns. The target variable is highly imbalanced (99% negative, 1% positive). Which approach is most appropriate for exploratory data analysis before modeling?

During exploratory data analysis, a data scientist notices that the correlation matrix of features shows many pairs with absolute correlation > 0.95. The dataset includes both numerical and categorical variables. Which technique is most appropriate to reduce multicollinearity while preserving the most information?

A data scientist is analyzing a dataset with a timestamp column. The goal is to identify seasonality and trends. Which visualization technique is most suitable?

A company uses Amazon SageMaker Data Wrangler to perform exploratory data analysis. They want to detect outliers in a numerical column using the Interquartile Range (IQR) method. Which transformation should they apply in Data Wrangler?

A data scientist is performing EDA on a dataset with 1 million rows. They suspect the dataset contains duplicate rows. Which approach is most efficient to identify duplicates in Amazon SageMaker Studio?

A data scientist is exploring a dataset with many missing values. They want to understand the pattern of missingness before deciding on imputation. Which approach is most appropriate?

A data scientist is using Amazon SageMaker Studio notebooks for EDA. They want to share a reproducible report that includes code, visualizations, and narrative text with their team. Which approach should they use?

Which TWO approaches are appropriate for handling missing categorical data during exploratory data analysis? (Choose two.)

Which THREE actions are valid steps in exploratory data analysis when working with a new dataset? (Choose three.)

Which TWO techniques can be used to detect multicollinearity among numerical features during exploratory data analysis? (Choose two.)

Refer to the exhibit. A data scientist lists files in an S3 bucket. The dataset is split into train, test, and validation sets. What is the most likely issue with this data split?

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?

Refer to the exhibit. A data scientist is running an Amazon EMR Spark job for exploratory data analysis on a large dataset. The job fails with the error shown. What is the most appropriate action to resolve this?

A data scientist runs a SageMaker notebook and uses pandas to explore a dataset. The dataset contains 500,000 rows and 20 columns, including a 'timestamp' column. After loading the data into a DataFrame, the memory usage is unexpectedly high. What is the most likely cause?

A machine learning engineer is analyzing a dataset for a regression problem. The target variable has a long-tail distribution with extreme outliers. The engineer wants to reduce the influence of outliers while preserving the relative order of values. Which data transformation should the engineer apply to the target variable?

A data scientist is exploring a dataset with 100 features. The goal is to build a binary classification model. The dataset is highly imbalanced with 95% negative class and 5% positive class. The data scientist wants to understand the relationship between features and the target. Which technique is most appropriate for initial exploratory analysis?

A company is storing customer transaction data in Amazon S3 as CSV files. A data scientist uses AWS Glue to crawl the data and create a table in the AWS Glue Data Catalog. When querying the table with Amazon Athena, the data scientist notices that some columns have NULL values where data should exist. The data scientist examines the raw CSV files and confirms the data is present. What is the most likely cause of the NULL values?

A data scientist is using Amazon SageMaker Data Wrangler to perform exploratory data analysis on a dataset. The dataset contains a feature 'age' with values ranging from 0 to 120. The data scientist wants to detect outliers. Which built-in transform in Data Wrangler is most appropriate for this task?

A data scientist is analyzing a dataset with numerical features and a binary target variable. The data scientist creates a pairplot and notices that one feature has a bimodal distribution when colored by the target class. What does this observation suggest?

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?

A data scientist is analyzing a dataset containing customer reviews. The data scientist wants to understand the most common words used in positive and negative reviews. Which AWS service is most suitable for this task?

A data scientist loads a large dataset from Amazon S3 into a pandas DataFrame using a SageMaker notebook. The dataset contains a mix of numeric and categorical features. The data scientist wants to quickly check for missing values. Which pandas function is most appropriate?

A data scientist is exploring a dataset with 50 features and a binary target. The data scientist computes the correlation matrix and finds that two features, X1 and X2, have a correlation coefficient of 0.95. Which TWO actions should the data scientist consider? (Choose 2.)

A data scientist is performing EDA on a dataset stored in Amazon S3 using Amazon Athena. The dataset is partitioned by date, and each partition contains CSV files. The data scientist notices that some queries return zero rows for partitions that should have data. Which THREE steps should the data scientist take to troubleshoot? (Choose 3.)

A data scientist is analyzing a dataset with a mix of numerical and categorical features. The target variable is binary. The data scientist wants to visualize the distribution of a numerical feature across the two target classes. Which TWO visualization techniques are appropriate? (Choose 2.)

A data scientist runs the AWS CLI command shown in the exhibit to list objects larger than 100 KB in an S3 bucket. The data scientist wants to understand the size distribution of these files. What is the most significant limitation of this approach for EDA?

A data scientist is granted the IAM policy shown in the exhibit. The data scientist can query the 'data-lake-bucket' using Athena and get results. However, when the data scientist tries to run a CTAS (CREATE TABLE AS SELECT) query in Athena to write results to a new S3 location, the query fails. What is the most likely reason?

A data scientist is investigating an application that logs errors to Amazon CloudWatch Logs. The data scientist runs the CloudWatch Logs Insights query shown in the exhibit. The query returns no results, even though the data scientist knows errors have occurred. What is the most likely cause?

A data scientist is analyzing a dataset with a target variable that is highly imbalanced (only 1% positive class). The goal is to build a binary classifier. During exploratory data analysis, which metric is MOST appropriate to evaluate the performance of different sampling strategies before model training?

A data engineer is performing exploratory data analysis on a dataset stored in Amazon S3 using AWS Glue DataBrew. The dataset contains a column 'age' with missing values. DataBrew's profile shows that the column has 5% missing values, a mean of 45, and a standard deviation of 15. Which imputation strategy should the engineer recommend to minimize bias if the missing data is Missing at Random (MAR)?

A machine learning engineer is analyzing a dataset that contains a categorical feature 'country' with 200 unique values. The target variable is binary. The engineer wants to use this feature in a linear model. Which encoding method should be applied during EDA to prepare the data for modeling, considering the high cardinality?

A data analyst is performing exploratory data analysis on a dataset with 100 features. The analyst wants to identify which features contribute most to the variance in the data. Which technique should the analyst use?

During EDA, a data scientist discovers that a numerical feature 'income' has a skewness of 3.5. Which transformation should the scientist apply to make the distribution more symmetric?

A data scientist is exploring a dataset with a large number of features. The scientist suspects that some features are redundant because they are highly correlated with each other. Which technique should the scientist use during EDA to identify and remove such redundant features?

In exploratory data analysis, a data scientist notices that the distribution of a continuous variable is bimodal. The scientist suspects that the two modes correspond to two different groups in the data. Which visualization is MOST appropriate to confirm this suspicion?

A data scientist is analyzing a dataset with 1 million rows and 50 features. The scientist wants to detect outliers in a numerical feature 'transaction_amount' which has a long right tail. The scientist suspects that outliers are due to data entry errors and should be removed. Which outlier detection method is MOST robust for this scenario?

A data scientist is performing EDA on a dataset with 500,000 rows and 20 columns. The dataset contains missing values in some columns. Which TWO approaches are appropriate for handling missing data during EDA? (Choose 2)

A data scientist is evaluating feature engineering options for a dataset containing a categorical variable 'education_level' with values: High School, Bachelor, Master, PhD. The target variable is continuous. Which THREE encoding methods are appropriate for this ordinal categorical variable? (Choose 3)