MLS-C01 Exploratory Data Analysis Practice Test 7 — 15 Questions

Question 1

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?

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

Accepted Answer

Use S3 Select with a SQL query to retrieve the first 1000 rows.. S3 Select allows you to use a SQL expression to retrieve a subset of data from an S3 object without downloading the entire object. By running a query like 'SELECT * FROM S3Object LIMIT 1000', the scientist can read only the first 1000 rows, minimizing data transfer and cost. S3 Select processes the query on the S3 side and returns only the filtered data.

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 2

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.. The IAM policy includes the s3:ListBucket action with a condition that uses s3:ExistingObjectTag to require each object to have the tag 'data-type' set to 'training'. However, the ListBucket operation lists all objects in the bucket, and the condition is evaluated against each object. If any object in the bucket does not have this tag, the request fails with an access denied error. Option B is incorrect because the policy does include s3:ListBucket. Option C is incorrect because the bucket name 'my-bucket' is a valid resource identifier; the full ARN is not required. Option D is incorrect because the StringEquals operator is valid for this condition key; the issue is the condition's requirement, not the operator.

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 3

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?

Accepted Answer

Use AWS Glue DataBrew to flatten the JSON and create new columns for session duration.. AWS Glue DataBrew provides a visual interface to flatten nested JSON and compute derived metrics like session duration without writing code. Option A (EMR with Apache Spark) is more complex and requires writing code. Option B (Athena with JSON SerDe) can query but requires SQL that handles arrays. Option D (QuickSight) is visualization only and cannot flatten or compute session duration.

Answer

Use Amazon EMR with Apache Spark to process the data.

Answer

Use Amazon Athena with JSON SerDe to query the data and compute session duration with SQL.

Answer

Use Amazon QuickSight to visualize the raw data without flattening.

Question 4

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?

Accepted Answer

Take a stratified sample that preserves the 99:1 ratio.. C is correct because stratified sampling preserves the original class proportion (99:1) in the sample, which is important for exploratory data analysis on imbalanced data without artificially altering the distribution. Option A (remove all negatives) loses all negative data, preventing analysis of majority class patterns. Option B (random sample) may result in insufficient positive examples due to imbalance. Option D (up-sample minority) would change the distribution and could lead to misleading visualizations and statistics during EDA.

Answer

Remove all negative examples and analyze only the positive ones.

Answer

Take a random sample of 100,000 rows from the entire dataset.

Answer

Up-sample the minority class to balance the dataset before analysis.

Question 5

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?

Accepted Answer

Apply Principal Component Analysis (PCA) to the features.. PCA is the most appropriate technique to reduce multicollinearity while preserving the most information because it transforms the original correlated features into a set of uncorrelated principal components, and the first few components capture most of the variance. This addresses multicollinearity without simply discarding features, which could lose information. PCA is particularly useful when many features are highly correlated, as it compresses the data into fewer dimensions.

Answer

Use only one-hot encoded categorical features.

Answer

Apply L1 regularization during model training.

Answer

Remove one feature from each highly correlated pair.

Question 6

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.. Using DuckDB SQL query with COUNT(*) - COUNT(DISTINCT *) is the most efficient approach because DuckDB is an in-process analytical database optimized for fast aggregation on large datasets, and it can handle 1 million rows quickly. The query directly computes the number of duplicate rows by subtracting the count of distinct rows from the total count. This is a single, concise operation that leverages DuckDB's vectorized execution.

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 7

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. Amazon SageMaker Data Wrangler provides a visual interface for data profiling without code. AWS Glue DataBrew also offers similar profiling capabilities, but Data Wrangler is the native SageMaker tool, making it the expected answer for this exam. QuickSight is for visualization, not profiling, and Athena is for querying.

Answer

Amazon QuickSight

Answer

AWS Glue DataBrew

Answer

Amazon Athena

Question 8

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?

Accepted Answer

Save the notebook as an .ipynb file and share it via Amazon S3.. Saving the notebook as an .ipynb file and sharing it via Amazon S3 is the correct approach because .ipynb files preserve code, visualizations, and narrative text, enabling reproducibility. Amazon SageMaker Studio notebooks are based on Jupyter notebooks, and sharing the .ipynb file allows team members to open and run the notebook in their own environment.

Answer

Use Amazon SageMaker Clarify to generate an EDA report.

Answer

Export the results to Amazon QuickSight and create a dashboard.

Answer

Use Amazon SageMaker Autopilot to generate a report.

Question 9

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?

Accepted Answer

The training set is smaller than the test set, which is unusual.. The training set (1024 bytes) is smaller than the test set (2048 bytes), which is unusual. Typically training set should be larger. Option A (missing header) cannot be inferred; Option B (CSV format) is fine; Option D (partitioning) is not evident.

Answer

The files are not partitioned by date.

Answer

The training file is missing a header row.

Answer

The test file should be in JSON format.

Question 10

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

Accepted Answer

Athena needs s3:PutObject permission to write results.. Amazon Athena requires write access to an S3 location to store query results, and the IAM policy shown lacks s3:PutObject permission on the results bucket. Even if the user can read data from my-bucket, Athena cannot execute the query without a writable output location. This is the most common cause of Athena query failures related to IAM.

Answer

The ListBucket action is not granted.

Answer

The data is encrypted with SSE-C.

Answer

The bucket does not exist.

Question 11

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?

Accepted Answer

Increase the executor memory in Spark configuration.. The error shown in the exhibit is likely an out-of-memory error for Spark executors. Increasing executor memory directly addresses this by providing more heap space for the executors to process data. This is the most appropriate action when the job fails due to memory constraints during execution.

Answer

Reduce the number of worker nodes.

Answer

Convert the input data to Parquet format.

Answer

Increase the driver memory.

Question 12

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?

Accepted Answer

Rank transformation. Rank transformation replaces each value with its rank, which preserves the relative order of values while completely removing the influence of extreme outliers. This is ideal for long-tail distributions where the engineer wants to reduce outlier impact without changing ordering.

Answer

Min-max normalization

Answer

Box-Cox transformation

Answer

Log transformation

Question 13

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?

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

Accepted Answer

Use stratified sampling to create a balanced subset for visualization and correlation analysis.. Stratified sampling preserves the 95/5 class ratio while allowing the data scientist to draw a smaller, representative subset for visualization and correlation analysis. This keeps the minority class represented so relationships between features and the positive class are not lost. It is the standard EDA approach for highly imbalanced datasets before any modeling or resampling decisions are made.

Answer

Remove the minority class samples and analyze the majority class only.

Answer

Use random sampling to select 10% of the data for EDA.

Answer

Apply SMOTE to the dataset before performing EDA.

Question 14

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?

Accepted Answer

The CSV files have different schemas (e.g., different columns) across partitions.. When Athena queries a Glue table whose partitions have different column layouts, the table's schema (derived from one partition or a merged view) does not match every file. Columns that exist in the table definition but are absent from a given partition's CSV are returned as NULL for rows from that partition. The raw data is present, but it lands in the wrong columns or is missing relative to the table schema, producing NULLs.

Answer

Athena is configured to skip corrupted records, causing NULLs.

Answer

The Glue crawler incorrectly inferred the data type of the columns.

Answer

The CSV files use a custom delimiter that the Glue crawler does not recognize.

Question 15

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?

Accepted Answer

The policy does not grant s3:PutObject permission on the bucket.. The policy allows s3:GetObject and s3:ListBucket, but not s3:PutObject, which is required for CTAS queries. Option A is wrong because the policy uses resource-level permissions for S3. Option C is wrong because Athena does not require Glue Data Catalog permissions for CTAS if the table metadata is already stored. Option D is wrong because the policy does not restrict Athena resource ARNs.

Answer

The policy does not grant athena:CreateTable permission.

Answer

The policy does not grant permissions to the Glue Data Catalog.

Answer

The policy uses a wildcard for Athena actions, which is not allowed.