MLS-C01 Practice Exam 68 — 20 Questions

Question 1

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?

Accepted Answer

Create the table using LOCATION 's3://my-bucket/logs/2023/' which includes all files under that prefix.. The CLI command filters objects larger than 1000 bytes, but the scientist wants all files in the prefix. The Athena table definition should point to the entire prefix without size filtering. Option A is wrong because the command was run locally, not affecting Athena. Option B is wrong because adding a WHERE clause in Athena only filters after scanning. Option D is wrong because the scientist can still create the table without size restrictions.

Answer

Create the table and add a WHERE clause to include small files.

Answer

Ask the S3 team to remove the size restriction on the bucket.

Answer

Modify the CLI command to remove the size filter and re-run it before creating the table.

Question 2

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?

Accepted Answer

Use stratified sampling to create a balanced sample for analysis.. Option A is correct because stratified sampling ensures the minority class is proportionally represented in the sample, allowing meaningful analysis. Option B is wrong because random sampling may miss the minority class entirely. Option C is wrong because SMOTE is for generating synthetic data, not for exploratory analysis. Option D is wrong because feature scaling does not address class imbalance.

Answer

Apply SMOTE to the dataset before analysis.

Answer

Use random sampling to reduce the dataset size.

Answer

Scale the features using Min-Max scaling.

Question 3

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?

Accepted Answer

Use S3 Select with a SQL query to retrieve the first 1000 rows.. Option C is correct because S3 Select allows retrieving a subset of data using SQL-like queries, reducing data transfer. Option A is wrong because downloading the entire 2 GB file is inefficient and costly. Option B is wrong because the S3 range read (with Range header) can retrieve bytes, but the user wants lines, not bytes; it's possible but less convenient and still transfers the entire file if lines are not aligned. Option D is wrong because Athena scans the entire file; it's not efficient for a single large file.

Answer

Use the AWS CLI to download the entire file and then use head to get the first lines.

Answer

Use the S3 Range header to read the first 1 MB of the file and parse lines.

Answer

Use Amazon Athena to query the file with LIMIT 1000.

Question 4

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?

Accepted Answer

Calculate mutual information between each feature and the target.. Mutual information can capture any kind of dependency (including nonlinear) between features and target. Option A (Pearson correlation) only linear. Option B (Chi-squared test) is for categorical features. Option D (ANOVA) is for comparing means across groups.

Answer

Use ANOVA to compare feature means across target classes.

Answer

Compute Pearson correlation coefficients.

Answer

Perform chi-squared tests for each feature.

Question 5

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?

Accepted Answer

Use DuckDB SQL query: SELECT COUNT(*) - COUNT(DISTINCT *) FROM table.. Option C is correct because DuckDB is an in-process SQL OLAP database that can run on a single machine and efficiently handle large datasets. Option A (Python loop) is slow; Option B (pandas drop_duplicates) may be memory-intensive; Option D (Athena) is serverless but incurs cost and latency.

Answer

Write a Python script that loops through each row and compares to a set of seen rows.

Answer

Use pandas drop_duplicates and then check the length difference.

Answer

Use Amazon Athena to query the S3 data with COUNT(DISTINCT *).

Question 6

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?

Accepted Answer

The default data types inferred by pandas are unnecessarily large for the actual data ranges.. Option C is correct because pandas reads numeric columns as int64 or float64 by default, which uses 8 bytes per value. For 500,000 rows, even a single numeric column can consume significant memory. Option A is wrong because pandas does not automatically index columns. Option B is wrong because memory usage is typically not due to loading all rows; pandas loads all rows. Option D is wrong because CSV files are plain text and do not have compression overhead.

Answer

The DataFrame created an index column on the timestamp field, doubling memory usage.

Answer

The DataFrame only loaded a sample of the data, but the sample size was too large.

Answer

The CSV file was compressed, and pandas inflated it in memory.

Question 7

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?

Accepted Answer

Plot the distribution of each feature separately for the positive and negative classes.. Option B is correct because analyzing the distribution of features across classes can reveal separability and potential issues. Option A is wrong because correlation with target is not the primary concern. Option C is wrong because missing values are not the immediate concern. Option D is wrong because PCA is not necessary at this stage.

Answer

Compute the correlation matrix of all features with the target variable.

Answer

Check for missing values and outliers in the dataset.

Answer

Perform PCA and visualize the first two principal components colored by class.

Question 8

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?

Accepted Answer

The query is trying to read data from the 'sensitive/' prefix.. Option B is correct because Athena needs s3:GetObject on the bucket to read data, but the Deny statement prevents access to objects under 'sensitive/' prefix. However, the query may be trying to read from that prefix. Option A is wrong because ListBucket is allowed. Option C is wrong because Deny blocks GetObject. Option D is wrong because the policy is valid.

Answer

The policy does not allow s3:ListBucket on the bucket.

Answer

The policy has a syntax error.

Answer

The s3:GetObject action is explicitly denied for all objects.

Question 9

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?

Accepted Answer

Check the source database table sizes and row counts over time.. The timeout suggests the job is taking longer than the max 300 seconds. Analyzing source data volume trends helps determine if data size has increased, causing longer processing time. Option A is wrong because changing job type does not address root cause. Option B is wrong because increasing timeout without understanding data growth is a temporary fix. Option D is wrong because the error is about timeout, not connectivity.

Answer

Test network connectivity from the Glue job to the source database using telnet.

Answer

Switch the Glue ETL job type from Spark to Python shell to reduce overhead.

Answer

Increase the Glue job timeout to 600 seconds and rerun.

Question 10

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?

Accepted Answer

Use STL (Seasonal and Trend decomposition using Loess).. Option C is correct because STL decomposition is robust to outliers and can handle any seasonality period, making it suitable for daily data with weekly seasonality. Option A is wrong because classical decomposition assumes additive seasonality and is less robust. Option B is wrong because ARIMA is a forecasting model, not a decomposition method. Option D is wrong because ETS is an exponential smoothing model, not primarily for decomposition.

Answer

Additive decomposition using moving averages.

Answer

Fit an ARIMA model and examine residuals.

Answer

Apply an ETS (Error, Trend, Seasonal) model.

Question 11

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?

Accepted Answer

The condition on the ListBucket action requires all objects to have the tag 'data-type'='training', which may not be satisfied.. Option B is correct because the ListBucket action is conditioned on the s3:ExistingObjectTag/data-type condition, which requires that all objects in the bucket have the tag 'data-type' set to 'training'. However, the ListBucket action applies to the bucket, not individual objects, and the condition might not be evaluated correctly, but more importantly, the condition on ListBucket is unusual; typically conditions on ListBucket should be avoided or use different keys. However, the most direct issue is that the condition must be satisfied for the request, and if the bucket has objects without that tag, the ListBucket action is denied. Option A is wrong because the policy includes s3:ListBucket on the bucket ARN. Option C is wrong because the condition uses StringEquals, not a request parameter. Option D is wrong because the bucket exists and the user has permission if the condition is met.

Answer

The IAM policy does not include the s3:ListBucket action.

Answer

The policy does not grant access to the bucket because it uses 'my-bucket' instead of the full ARN.

Answer

The condition should use 'StringLike' instead of 'StringEquals'.

Question 12

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?

Accepted Answer

Use Data Wrangler's data quality and insights report to generate a report with statistics and visualizations.. Using Data Wrangler's built-in data quality and insights report is the most efficient way to get statistics and detect issues without custom code. Option B (Athena) requires writing SQL queries. Option C (QuickSight) needs exporting. Option D (EMR) is overkill.

Answer

Export the transformed data to S3 and query with Amazon Athena.

Answer

Use Amazon EMR with Spark to compute statistics.

Answer

Import the data into Amazon QuickSight and create dashboards.

Question 13

Which AWS service can be used to generate a data profile (including histograms, correlations, and statistics) for a dataset stored in Amazon S3 without writing code?

Accepted Answer

Amazon SageMaker Data Wrangler. Option D is correct because Amazon SageMaker Data Wrangler provides a visual interface to create data profiles. Option A (QuickSight) is for visualization, not profiling; Option B (Glue DataBrew) also profiles but Data Wrangler is more integrated with SageMaker; Option C (Athena) is for querying.

Answer

Amazon QuickSight

Answer

AWS Glue DataBrew

Answer

Amazon Athena

Question 14

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?

Accepted Answer

Amazon Comprehend to detect syntax and entities.. Option C is correct because Amazon Comprehend can detect entities, key phrases, and syntax, but not spelling errors directly; however, it can be used to identify unusual patterns. Actually, for spelling errors, a custom solution may be needed. But among options, Comprehend is the only AWS AI service that processes text. Option A is wrong because SageMaker Data Wrangler is for tabular data. Option B is wrong because Athena is for SQL. Option D is wrong because QuickSight is for visualization.

Answer

Amazon SageMaker Data Wrangler with a custom transform for text cleaning.

Answer

Amazon Athena with SQL queries to find anomalies.

Answer

Amazon QuickSight to create word clouds.

Question 15

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?

Accepted Answer

Modify the query to display the actual @message for the hour with the spike.. Option C is correct because examining the actual error messages during that hour can reveal the cause. Option A is wrong because bin(5m) may be too granular. Option B is wrong because the query already uses 1h bins. Option D is wrong because the spike is already identified.

Answer

Remove the filter on ERROR to see all messages.

Answer

Change the bin to 5m to see more detailed spikes.

Answer

Increase the limit to 50 to see more hours.

Question 16

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?

Accepted Answer

Create scatter plots of numerical features against the target variable.. Scatter plots for numerical vs continuous target and box plots for categorical vs continuous target are standard. Option C (confusion matrix) is for classification. Option D (histogram of target) is univariate. Option E (silhouette score) is for clustering.

Answer

Generate a confusion matrix for the target variable.

Answer

Compute the silhouette score for each feature.

Answer

Plot a histogram of the target variable.

Question 17

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?

Accepted Answer

Truncated Singular Value Decomposition (SVD).. Option D is correct because Truncated SVD (e.g., using sklearn's TruncatedSVD or PCA on sparse data via SVD) is designed for sparse matrices and preserves variance. Option A is wrong because PCA with covariance matrix requires dense matrix and is computationally expensive for sparse data. Option B is wrong because t-SNE is for visualization, not for general dimensionality reduction preserving structure. Option C is wrong because LDA is a supervised method and requires labels.

Answer

t-distributed Stochastic Neighbor Embedding (t-SNE).

Answer

Linear Discriminant Analysis (LDA).

Answer

Principal Component Analysis (PCA) using the covariance matrix.

Question 18

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?

Accepted Answer

The first query uses a partition column (year), allowing partition pruning, while the second query does not.. Option A is correct because the table is partitioned by year and month. The first query filters on a partition column (year), so Athena prunes partitions and scans only the relevant data. The second query filters on a non-partition column (amount), so Athena scans all partitions. Option B is wrong because the data format is text (CSV), not Parquet. Option C is wrong because compression is not mentioned. Option D is wrong because the query is not partitioned correctly; the second query does not use partition columns.

Answer

The data is compressed, and the first query benefits from compression.

Answer

The data is stored in Parquet format, which is optimized for columnar access.

Answer

The second query is not optimized because it uses 'SELECT *'.

Question 19

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

Accepted Answer

Remove rows or columns with missing values if they are few.. Imputation with mean/median and removing rows/columns are standard techniques. Options C (one-hot encoding), D (PCA), and E (feature scaling) are not for handling missing values.

Answer

Apply feature scaling to normalize the data.

Answer

Use Principal Component Analysis (PCA) to reduce dimensionality.

Answer

Apply one-hot encoding to the missing values.

Question 20

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?

Accepted Answer

Visualize the missingness using a heatmap or bar chart.. Option A is correct because a heatmap of missing values (using libraries like missingno) visually shows patterns. Option B (drop rows) is premature; Option C (mean imputation) assumes MCAR; Option D (correlation matrix) does not show missingness patterns.

Answer

Compute the correlation matrix of the features with missing values.

Answer

Drop all rows with any missing values.

Answer

Impute all missing values with the mean of each column.