MLS-C01 Exploratory Data Analysis Practice Test 4 — 15 Questions

Question 1

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

Accepted Answer

Step 1: Create a processing script, Step 2: Upload script and data to Amazon S3, Step 3: Configure the ProcessingJob, Step 4: Execute the ProcessingJob, Step 5: Verify the job output. Processing requires script creation, data upload, job configuration, execution, and verification.

Answer

Step 1: Upload script and data to Amazon S3, Step 2: Create a processing script, Step 3: Configure the ProcessingJob, Step 4: Execute the ProcessingJob, Step 5: Verify the job output

Answer

Step 1: Configure the ProcessingJob, Step 2: Create a processing script, Step 3: Upload script and data to Amazon S3, Step 4: Execute the ProcessingJob, Step 5: Verify the job output

Answer

Step 1: Create a processing script, Step 2: Configure the ProcessingJob, Step 3: Upload script and data to Amazon S3, Step 4: Execute the ProcessingJob, Step 5: Verify the job output

Question 2

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

Accepted Answer

1. Define feature group, 2. Ingest data, 3. Query features, 4. Train model, 5. Maintain feature store. Feature Store involves defining group, ingesting, querying, training, and maintaining.

Answer

1. Define feature group, 2. Query features, 3. Ingest data, 4. Train model, 5. Maintain feature store

Answer

1. Ingest data, 2. Define feature group, 3. Query features, 4. Train model, 5. Maintain feature store

Answer

1. Define feature group, 2. Ingest data, 3. Train model, 4. Query features, 5. Maintain feature store

Question 3

Match each SageMaker feature to its description.

Accepted Answer

Ground Truth: Build and manage training datasets with human labeling. The correct matches are: Ground Truth for dataset labeling, Neo for model optimization, Debugger for training monitoring, and Autopilot for automated model building. Common confusions include swapping Ground Truth and Neo, or mixing Debugger with Autopilot.

Answer

Ground Truth: Optimize models for deployment on target hardware

Answer

Neo: Build and manage training datasets with human labeling

Question 4

Match each ML model evaluation concept to its definition.

Accepted Answer

Precision: The proportion of true positive predictions among all positive predictions. Precision, Recall, F1 Score, and ROC AUC are key evaluation metrics. Common confusions include swapping Accuracy, Specificity, and Precision definitions.

Answer

ROC AUC: The proportion of correct predictions among total predictions

Answer

Accuracy: The ability to correctly identify negative cases

Answer

Specificity: The proportion of true positive predictions among all positive predictions

Question 5

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?

Accepted Answer

Apply principal component analysis (PCA) to reduce dimensionality.. PCA reduces dimensionality by transforming correlated features into uncorrelated principal components, addressing multicollinearity while retaining most of the variance. Option A is wrong: collecting more data does not reduce correlation between features. Option B is wrong: increasing regularization (e.g., L2) can mitigate multicollinearity effects, but with 500 features and many highly correlated pairs, PCA is more effective as a dimensionality reduction technique. Option D is wrong: removing all features with correlation >0.95 may discard useful information and is less systematic than PCA.

Answer

Collect more training data to reduce the impact of correlated features.

Answer

Increase the regularization parameter in the model.

Answer

Remove all features with correlation above 0.95.

Question 6

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?

Accepted Answer

Apply SMOTE to generate synthetic samples for the minority class.. SMOTE (Synthetic Minority Over-sampling Technique) generates synthetic samples for the minority class, which helps balance the dataset and improves model performance on the minority class without losing information from the majority class. Option A is wrong: training on raw data without addressing imbalance will cause the model to be biased toward the majority class and perform poorly on the minority class. Option C is wrong: accuracy is not a suitable evaluation metric for imbalanced datasets because a model that always predicts the majority class will achieve 99% accuracy, masking poor performance on the minority class; instead, metrics like precision, recall, F1-score, or AUC should be used. Option D is wrong: under-sampling the majority class to match the minority class size discards a large amount of data, potentially losing valuable patterns and reducing model performance.

Answer

Train the model on the raw data without any modification.

Answer

Use accuracy as the evaluation metric and train on the raw data.

Answer

Under-sample the majority class to match the minority class size.

Question 7

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?

Accepted Answer

Use multiple imputation by chained equations (MICE).. Multiple imputation by chained equations (MICE) is well-suited for missing at random (MAR) data as it models each variable with missing values conditional on other variables, preserving relationships. Option A (removing rows) reduces sample size and can introduce bias if data are not MCAR. Option B (KNN) assumes data are missing completely at random (MCAR) and may not handle MAR well. Option D (mean imputation) reduces variance and distorts relationships.

Answer

Remove all rows with any missing values.

Answer

Use k-nearest neighbors imputation.

Answer

Replace missing values with the mean of the column.

Question 8

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

Accepted Answer

Apply log transformation to reduce the impact of extreme values.. Applying a log transformation compresses the range of the data, reducing the influence of extreme values without removing them. This is a common technique in exploratory data analysis for right-skewed distributions, as it can make the data more normally distributed and improve the performance of models that assume normality.

Answer

Replace the mean with the median for numerical features.

Answer

Remove all outliers without further investigation.

Answer

Assume outliers are errors and delete them.

Question 9

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

Accepted Answer

Extracting date/time components like day of week or hour.. Extracting date/time components such as day of week, hour, or month from a timestamp is a standard feature engineering technique. It transforms a single datetime column into multiple categorical or cyclical features that can reveal temporal patterns like weekly seasonality or peak hours, which are often critical for time-series models.

Answer

Using principal component analysis (PCA) to create new features.

Answer

Applying one-hot encoding to numerical features.

Question 10

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

Accepted Answer

When a categorical feature has high cardinality, consider grouping rare categories.. High-cardinality categorical features can lead to overfitting and sparse representations. Grouping rare categories into a single 'Other' bucket reduces dimensionality and noise, improving model generalization without losing significant predictive signal.

Answer

Target encoding always avoids data leakage.

Answer

Label encoding is suitable for nominal categorical variables.

Answer

Categorical variables should always be dropped if they have many unique values.

Question 11

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?

Accepted Answer

The ETag can be used for integrity checking.. The ETag can be used for integrity checking, as it is an MD5 hash of the object content. Option B is wrong because ContentLength 1048576 corresponds to 1 MB, not 1 GB. Option C is wrong because S3 versioning is indicated by VersionId, not ETag. Option D is wrong because the output does not provide any information about preprocessing; the presence of metadata like 'preprocessed' is not shown.

Answer

The file is 1 GB in size.

Answer

The object has S3 versioning enabled.

Answer

The file has not been preprocessed.

Question 12

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"
        }
      }
    }
  ]
}
```

Accepted Answer

Access is denied if the request does not originate from the specified VPC endpoint.. The Deny statement explicitly denies s3:* actions on the `confidential` folder unless the request originates from the specified VPC endpoint (using the `aws:SourceVpce` condition). If the request does not come from that VPC endpoint, it will be denied. Option A is wrong because the Deny overrides the Allow. Option B is wrong because the Deny is conditional, not unconditional. Option C is wrong because the condition is about the VPC endpoint, not the use of HTTPS.

Answer

Access is allowed because the Allow statement grants s3:ListBucket.

Answer

Access is denied unconditionally.

Answer

Access is allowed only if the request uses HTTPS.

Question 13

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?

Accepted Answer

The CSV file has inconsistent number of columns in some rows.. The error indicates that a row has more fields than the header, which is exactly what happens when the CSV file has inconsistent number of columns in some rows. Option A is incorrect because the error does not mention delimiter; a different delimiter would cause all rows to have wrong number of fields, not just some. Option B is incorrect because missing header would cause Athena to treat the first row as data, not cause mismatched field counts later. Option C is incorrect because Athena can handle large files; the error is about schema mismatch, not file size.

Answer

The CSV file uses a different delimiter than comma.

Answer

The CSV file is missing a header row.

Answer

The CSV file is too large for Athena to process.

Question 14

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?

Accepted Answer

Histogram with a logarithmic scale on the x-axis. A histogram with a logarithmic scale on the x-axis can effectively display the distribution of a heavily right-skewed numerical feature by compressing the long tail and making the shape more interpretable. Option A (box plot) is less suitable because it shows quartiles and outliers but not the full distribution shape. Option B (line chart) is used for time series or trends, not for distribution assessment. Option C (scatter plot) is for visualizing relationships between two variables, not a single variable's distribution.

Answer

Box plot

Answer

Line chart

Answer

Scatter plot

Question 15

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?

Accepted Answer

Extract hour of day and apply sine/cosine transformation.. Extracting hour of day and applying sine and cosine transformations captures the cyclic nature of time (e.g., midnight wrapping around to the next day). Option A (binning into fixed intervals) loses granularity and does not preserve cyclicity. Option B (converting to Unix epoch seconds) loses the cyclic pattern. Option D (one-hot encoding) creates many sparse features and does not capture order or cycles.

Answer

Bin timestamps into fixed intervals.

Answer

Convert timestamp to Unix epoch seconds.

Answer

One-hot encode the timestamp column.