MLA-C01 Practice Test 29 — 15 Questions

Question 1

A financial services company ingests transaction data from multiple sources into an S3 data lake. They want to use AWS Glue to catalog this data and make it queryable by Amazon Athena. The data schema changes frequently as new sources are added. Which AWS Glue feature should they enable to automatically detect and update the schema?

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

Accepted Answer

AWS Glue crawlers with schema update policy set to 'UPDATE'. AWS Glue crawlers can automatically scan data in S3, infer schemas, and update the Data Catalog. Schema evolution is supported natively when crawlers are configured to update table definitions. Manual schema definition would not handle frequent changes.

Answer

AWS Glue DataBrew

Answer

AWS Glue ETL job scheduled to run daily

Answer

Manual schema definition in the AWS Glue Data Catalog

Question 2

A data scientist observes that a linear regression model has many irrelevant features. They want to perform feature selection to improve generalization. Which method combines feature selection with model training using a penalty that can shrink coefficients to zero?

Accepted Answer

Lasso regression. Lasso regression uses L1 regularization to shrink coefficients to zero, effectively performing feature selection.

Answer

Ridge regression

Answer

Recursive Feature Elimination (RFE)

Answer

Principal Component Analysis (PCA)

Question 3

A company ingests streaming transaction data from multiple sources using Amazon Kinesis Data Streams. The data must be transformed (e.g., JSON parsing, data type conversions) and then stored in Amazon S3 for ML training. The transformation logic may change over time. Which approach provides the greatest flexibility and ease of maintenance?

Accepted Answer

Use Kinesis Data Firehose with a Lambda function for transformation and delivery to S3.. Amazon Kinesis Data Firehose can invoke an AWS Lambda function for data transformation before delivering to S3. Lambda allows custom, easily updatable transformation logic. Using a Kinesis Client Library (KCL) on EC2 requires managing servers, and Kinesis Data Analytics is more suited for real-time analytics, not simple transformations.

Answer

Use a custom application running on Amazon EC2 with the Kinesis Client Library to transform and write to S3.

Answer

Use Kinesis Data Analytics with SQL queries to transform data and output to S3.

Answer

Use Kinesis Data Streams directly with a Lambda consumer to transform and store in S3.

Question 4

A company wants to secure data in transit between the client and SageMaker endpoint, and between containers in the same endpoint. Which THREE configurations should they apply? (Choose three.)

Accepted Answer

Enable network isolation for the model containers. Enforce inter-container traffic encryption, use HTTPS for the endpoint, and enable network isolation to prevent internet exposure. VPC-only mode controls network access but does not directly encrypt traffic.

Answer

Enable data encryption at rest using KMS

Answer

Use a VPC-only endpoint configuration

Question 5

A data engineer needs to integrate a new streaming data source into an existing ML pipeline. The data arrives as JSON records and must be transformed to Parquet format, partitioned by date, and stored in Amazon S3. The engineer also needs to catalog the data for querying with Amazon Athena. Which service should be used to perform the transformation and cataloging?

Accepted Answer

AWS Glue ETL job. AWS Glue ETL jobs can read streaming data (e.g., from Kinesis), transform it (e.g., JSON to Parquet), write to S3 with partitioning, and update the Glue Data Catalog for Athena to query. This is a managed, serverless solution.

Answer

Amazon EMR with Spark Streaming

Answer

Amazon Kinesis Data Analytics

Answer

Amazon SageMaker Data Wrangler

Question 6

A machine learning engineer is building a time-series forecasting model to predict daily sales for the next 30 days. The dataset spans two years of daily sales data. To evaluate model performance, the engineer needs to simulate a realistic forecasting scenario where the model is trained on past data and tested on future data without leakage. Which data splitting strategy should they use?

Accepted Answer

Walk-forward validation with an expanding window. Walk-forward validation (also known as time-series cross-validation) is specifically designed for time-dependent data. It trains on an expanding window of historical data and tests on the next period, respecting temporal order.

Answer

Hold-out validation using a random 80/20 split

Answer

Stratified sampling based on sales volume

Answer

k-fold cross-validation with random shuffling

Question 7

A machine learning engineer needs to prepare a dataset containing customer transactions for training a fraud detection model. The dataset includes features such as transaction amount, timestamp, merchant category, and customer ID. The engineer wants to create a feature representing the average transaction amount per customer over the last 7 days. Which approach should be used in Amazon SageMaker Data Wrangler?

Accepted Answer

Write a custom PySpark SQL query in a SQL transform that uses the `AVG` window function partitioned by customer ID and ordered by timestamp with a range between 7 days preceding and current row. SageMaker Data Wrangler supports custom SQL queries via PySpark SQL, which can compute windowed aggregations like a rolling average partitioned by customer ID over a time window. This is the most direct and scalable approach.

Answer

Export the data to Amazon SageMaker Feature Store and use point-in-time queries with a 7-day lookback

Answer

Use the built-in 'Aggregate' transform with a group-by on customer ID and average of transaction amount

Answer

Use the 'Handle Missing' transform to fill missing values with the mean transaction amount

Question 8

A data scientist needs to split a time-series dataset into training and testing sets while avoiding data leakage from future values. Which splitting technique should the data scientist use?

Accepted Answer

Time-series split (walk-forward validation). Time-series split (walk-forward validation) ensures that the training set only contains past data relative to the test set, preventing leakage. Stratified sampling and random shuffle are not appropriate for time series because they ignore temporal order. K-fold cross-validation without adjustments will also leak future information.

Answer

Random shuffle followed by a 80/20 split

Answer

K-fold cross-validation with shuffled folds

Answer

Stratified sampling based on the target variable

Question 9

A machine learning team is building a fraud detection model. They have a dataset with a categorical feature 'merchant_id' that has over 10,000 unique values. Which feature engineering technique should they apply to 'merchant_id' to reduce dimensionality while retaining predictive power?

Accepted Answer

Target encoding. Target encoding replaces each category with the mean target value, effectively encoding high-cardinality features into a single numeric column while preserving correlation with the target. One-hot encoding would explode dimensionality; label encoding imposes ordinality; ordinal encoding assumes order.

Answer

One-hot encoding

Answer

Ordinal encoding

Answer

Label encoding

Question 10

A data scientist is using Amazon SageMaker Data Wrangler to prepare a dataset for a binary classification model. The scientist wants to detect potential bias in the data before training. The dataset includes a sensitive attribute 'gender'. Which TWO actions should the scientist take in Data Wrangler to analyze and mitigate bias? (Select TWO.)

Accepted Answer

Apply a transform to balance the dataset by resampling. Data Wrangler integrates with SageMaker Clarify for bias detection and can report metrics. Data Wrangler does not directly compute feature importance (that's for models) or perform data augmentation. Cross-validation is a model evaluation technique.

Answer

Configure a cross-validation split

Answer

Compute feature importance using XGBoost

Answer

Perform data augmentation to increase dataset size

Question 11

A data scientist is using Amazon SageMaker Ground Truth to create a labeled dataset for object detection. The team wants to reduce labeling costs by automatically labeling easy examples and only sending uncertain examples to human annotators. Which TWO features should the scientist use? (Select TWO.)

Accepted Answer

Active learning. Active learning in Ground Truth automatically selects uncertain examples for human labeling, while pre-labeling from a trained model can auto-label confident predictions. Mechanical Turk and workforce types are about who labels, not cost reduction via automation.

Answer

Using Amazon Mechanical Turk for annotations

Answer

Using a private workforce

Answer

Using the built-in object detection labeling UI

Question 12

A data scientist is preparing a dataset for a linear regression model. The features have different scales: one feature ranges from 0 to 1000, another from 0 to 1, and a third from -5 to 5. The scientist wants to ensure that all features contribute equally to the model. Which TWO scaling techniques should the scientist consider? (Select TWO.)

Accepted Answer

MinMaxScaler (min-max normalization). StandardScaler (z-score normalization) and MinMaxScaler are both appropriate for scaling features to similar ranges. Log transformation is for skewed data; PCA reduces dimensionality; one-hot encoding is for categorical features.

Answer

Principal Component Analysis (PCA)

Answer

One-hot encoding

Answer

Log transformation

Question 13

A machine learning engineer is using AWS Glue ETL to transform a large dataset stored in Amazon S3. The transformation involves joining two tables on a high-cardinality column and aggregating results. The job is running slowly and the engineer needs to improve performance. Which optimization technique should the engineer apply?

Accepted Answer

Use bucketing on the join key for both tables. Bucketing the join key in both tables enables efficient merging without shuffling all data, significantly improving join performance on high-cardinality keys. Increasing workers and memory may help but is not specific to join optimization; using Spark SQL alone may not help.

Answer

Increase the number of workers and memory allocation

Answer

Use Spark SQL instead of the DynamicFrame API

Answer

Convert the job to use Python Shell

Question 14

A data engineer is designing an ETL pipeline using AWS Glue to transform raw data from S3 into a curated set for ML training. The data contains personally identifiable information (PII) that must be masked before being used by data scientists. Which TWO actions should the engineer take? (Choose TWO.)

Accepted Answer

Use AWS Glue DataBrew to define PII masking transformations. AWS Glue ETL jobs support custom transforms via PySpark. DataBrew provides a visual interface for data preparation including PII masking. The Glue Data Catalog is for metadata, not transformation. Crawlers catalog data, not mask. Kinesis Firehose is for streaming, not batch ETL.

Answer

Use Amazon Kinesis Data Firehose to transform data at ingestion

Answer

Use AWS Glue Data Catalog to automatically mask PII fields

Answer

Use AWS Glue Crawler to detect and mask PII automatically

Question 15

A machine learning engineer needs to prepare a dataset with a target variable that has severe class imbalance (1:1000). The dataset has 100,000 rows and 200 features. Which approach should the engineer use to address the class imbalance before training a classification model?

Accepted Answer

Use SMOTE to generate synthetic samples for the minority class.. SMOTE generates synthetic samples for the minority class, which is effective for severe imbalance. The other options either do not address imbalance (standardization) or are not appropriate for the scenario (undersampling alone discards too many majority samples, class weights are a modeling technique not a data preparation step).

Answer

Apply random undersampling to the majority class to match the minority class count.

Answer

Set class weights inversely proportional to class frequencies in the model.

Answer

Use StandardScaler on the features to normalize them.