MLS-C01 Mock Exam 83 — 25 Questions

Question 1

A data scientist wants to run a one-time SQL query on a large dataset stored in Amazon S3 (CSV format, 2 TB) using Amazon Athena. The query involves joining this dataset with a smaller table stored in Amazon RDS. What is the MOST cost-effective and performant approach?

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

Accepted Answer

Export the RDS table to S3 in Parquet format, then use Athena to join the two S3 datasets. Exporting the RDS table to S3 as Parquet and running the join in Athena avoids data transfer costs and leverages Athena's fast query engine. Option B (federated query) adds complexity and may be slower. Option C (Redshift Spectrum) requires a Redshift cluster. Option D (Glue ETL) is overkill for a one-time query.

Answer

Use Amazon Redshift Spectrum to query both S3 and RDS

Answer

Use Athena Federated Query to query RDS directly

Answer

Use AWS Glue ETL to join the data and write results back to S3, then query with Athena

Question 2

Which TWO AWS services are suitable for real-time stream processing?

Accepted Answer

Amazon Kinesis Data Analytics. Amazon Kinesis Data Analytics and AWS Lambda can process streams in real-time. AWS Glue is batch-oriented, Amazon EMR can process streams but is more batch, and Amazon Athena is for ad-hoc SQL queries on S3.

Answer

Amazon Athena

Answer

AWS Glue

Answer

Amazon EMR

Question 3

A company is training a deep learning model on SageMaker using a large dataset stored in S3. The training job is taking a long time due to I/O bottlenecks. Which action would MOST effectively reduce the I/O bottleneck?

Accepted Answer

Use Pipe input mode in the SageMaker estimator.. Pipe input mode streams training data directly from S3 to the algorithm without writing intermediate files to disk, eliminating I/O wait time. Option A (using Amazon EFS) introduces network file system latency, which would not reduce I/O bottleneck. Option B (using Amazon FSx for Lustre) could improve throughput but is more complex to set up and may not be as effective as Pipe mode for streaming. Option C (increasing the number of training instances) might distribute computation but does not directly reduce per-instance I/O bottleneck.

Answer

Use Amazon EFS as the data source.

Answer

Use Amazon FSx for Lustre as the data source.

Answer

Increase the number of training instances.

Question 4

A company is using Amazon Rekognition to detect objects in images stored in S3. They want to reduce costs by processing images only when they are uploaded. Which AWS service should be used to trigger Rekognition automatically?

Accepted Answer

AWS Lambda. AWS Lambda is the correct service because it can be triggered directly by S3 events (e.g., s3:ObjectCreated:Put) to invoke Amazon Rekognition's DetectLabels API on the newly uploaded image. This serverless architecture ensures processing occurs only on upload, eliminating idle costs and manual polling.

Answer

Amazon CloudWatch Events

Answer

Amazon Simple Notification Service (SNS)

Answer

AWS Step Functions

Question 5

Which TWO configurations are required to enable AWS Glue to access data stored in a VPC? (Choose two.)

Accepted Answer

A VPC endpoint for Amazon S3.. A VPC endpoint for Amazon S3 (Option A) is required because it allows AWS Glue to access S3 data privately over the AWS network without traversing the public internet, which is necessary when Glue runs inside a VPC. An AWS Glue connection object (Option B) is required because it defines the VPC, subnet, and security group that Glue will use to launch its resources within the VPC, enabling it to access data stores in that VPC.

Answer

A NAT gateway in a public subnet.

Answer

An Internet gateway attached to the VPC.

Answer

An S3 bucket policy that allows access from the Glue service principal.

Question 6

A data scientist needs to run a one-time query on 10 TB of data stored in S3 using Amazon Athena. The query scans 5 TB and returns a small result set. Which approach minimizes cost?

Accepted Answer

Query the data directly in Athena without any preprocessing. Athena charges based on the amount of data scanned per query. Since this is a one-time query on 10 TB of data that scans only 5 TB, querying directly in Athena without preprocessing is the most cost-effective approach because you pay only for the 5 TB scanned, with no additional costs for data conversion, storage, or cluster provisioning.

Answer

Create an S3 Select query to filter data before Athena

Answer

Use Amazon Redshift Spectrum to query the data

Answer

Use AWS Glue to convert the data to Parquet format and repartition by date

Question 7

A company is streaming data from IoT devices to Amazon Kinesis Data Firehose, which writes to an Amazon S3 bucket. The data is then processed by an AWS Glue ETL job and loaded into Amazon Redshift. The team notices that some records are missing in Redshift. They suspect data loss during the Firehose delivery. Which configuration parameter should be checked first?

Accepted Answer

The buffer interval (e.g., 60 seconds) and buffer size.. Firehose can buffer data before writing to S3. If the buffer interval is too long and the stream ends, data may be lost if the buffer is not flushed. Option C (buffer interval) is the most likely cause. Option A (compression) does not cause loss. Option B (KMS key) is for encryption. Option D (error logging) only logs errors, does not prevent loss.

Answer

The AWS KMS key used for encryption.

Answer

The CloudWatch error logging configuration.

Answer

The compression format (GZIP, Snappy, etc.).

Question 8

Which TWO data formats are columnar and optimized for analytics queries in Amazon S3?

Accepted Answer

ORC. ORC (Optimized Row Columnar) is a columnar storage format that stores data in a column-oriented manner, enabling efficient compression and predicate pushdown for analytics queries on Amazon S3. It is designed for high-performance read operations in big data frameworks like Apache Hive and Spark, making it ideal for aggregation and filtering workloads.

Answer

CSV

Answer

JSON

Answer

Avro

Question 9

A data scientist is using Amazon SageMaker to build a text classification model. The dataset has 100,000 labeled samples and 20 classes. The scientist wants to use a pre-trained BERT model and fine-tune it. Which approach is MOST cost-effective?

Accepted Answer

Fine-tune a pre-trained BERT-base model using a GPU instance.. Fine-tuning a pre-trained BERT-base model on a GPU instance is the most cost-effective approach. Pre-trained BERT models already capture general language features, so fine-tuning requires less computation and data compared to training from scratch. BERT-base is smaller than BERT-large, reducing cost while still being effective for text classification. Option A is wrong because training BERT from scratch is extremely expensive and unnecessary. Option C is wrong because BERT-large has higher cost and may overfit given the dataset size. Option D is wrong because CNN models from scratch would require more hyperparameter tuning and may not achieve comparable accuracy to a fine-tuned transformer.

Answer

Train a BERT model from scratch using a larger instance.

Answer

Use a pre-trained BERT-large model with a larger instance.

Answer

Train a CNN model from scratch using CPU instances.

Question 10

A company uses Amazon SageMaker to train a deep learning model on a GPU instance. The training job is taking too long. Which action would MOST likely reduce training time?

Accepted Answer

Use a larger GPU instance type, such as p3.16xlarge. Using a larger GPU instance like p3.16xlarge provides significantly more GPU memory, CUDA cores, and memory bandwidth, which allows for larger batch sizes and more efficient parallel processing of matrix operations. This directly reduces training time for deep learning models by enabling faster forward and backward passes through the network, especially when the model is large enough to fully utilize the additional GPU resources.

Answer

Reduce the mini-batch size

Answer

Use distributed data parallelism across multiple smaller instances

Answer

Reduce the number of epochs

Question 11

A data scientist needs to version control datasets used for machine learning experiments. Which AWS service should the data scientist use?

Accepted Answer

Amazon S3 with versioning enabled. Amazon S3 with versioning enabled is the correct choice because it provides a simple, scalable, and cost-effective way to version control datasets. S3 versioning preserves every object version, allowing you to retrieve, restore, or compare previous dataset states, which is essential for reproducibility in ML experiments. This directly meets the requirement for dataset version control without additional overhead.

Answer

AWS Lake Formation

Answer

Amazon SageMaker Feature Store

Answer

Amazon SageMaker Model Registry

Question 12

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.)

Accepted Answer

Calculate Variance Inflation Factor (VIF) for each feature. Variance Inflation Factor (VIF) measures how much the variance of a regression coefficient is inflated due to multicollinearity. Correlation matrix heatmap shows pairwise correlations. PCA reduces dimensionality but does not directly identify multicollinearity. Lasso regression addresses it via regularization but is a modeling step. t-SNE is for visualization of high-dimensional data.

Answer

Apply t-SNE for visualization

Answer

Apply Principal Component Analysis (PCA)

Answer

Use Lasso regression to select features

Question 13

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 14

A machine learning engineer is using Amazon SageMaker to train a model. The training job is taking too long. The engineer suspects the data loading is a bottleneck. Which action would MOST effectively diagnose the issue?

Accepted Answer

Use SageMaker Debugger to profile the training job. SageMaker Debugger can profile the training job and identify bottlenecks. Option A may add overhead. Option B is not detailed. Option D is for inference.

Answer

Monitor CPU utilization in CloudWatch

Answer

Enable SageMaker Model Monitor

Answer

Increase the instance type to a larger one

Question 15

A data scientist is using Amazon SageMaker to train an XGBoost model on a dataset with missing values. The dataset has both numeric and categorical features. Which preprocessing step is MOST appropriate before training?

Accepted Answer

One-hot encode categorical features and let XGBoost handle missing values natively. XGBoost has a built-in mechanism to handle missing values by learning the best direction to split on during training, making explicit imputation unnecessary. One-hot encoding categorical features is required because XGBoost only accepts numeric inputs, and this encoding preserves the categorical information without imposing ordinal relationships. This approach avoids data leakage from imputation and leverages XGBoost's native sparsity-aware algorithm.

Answer

Impute missing numeric values with the mean and categorical values with the mode, then train without encoding

Answer

Remove all rows with missing values and train on the remaining data

Answer

Label encode categorical features and use the built-in missing value handling of XGBoost

Question 16

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?

Accepted Answer

Chi-squared test of independence. The Chi-squared test of independence is used to determine if there is a significant association between two categorical variables, which is applicable here. Option A is wrong because Pearson correlation is for continuous variables. Option B is wrong because ANOVA is for comparing means across groups, but assumes continuous target. Option D is wrong because Mutual Information can be used but is not a statistical test with a p-value.

Answer

Pearson correlation coefficient

Answer

ANOVA (Analysis of Variance)

Answer

Mutual information

Question 17

A data scientist is performing EDA on a time series dataset of daily sales. The data scientist observes a pattern that repeats every 7 days. Which characteristic of the time series is being observed?

Accepted Answer

Seasonality. A pattern that repeats at a fixed frequency (every 7 days) is called seasonality. Option A is wrong because trend is a long-term increase or decrease. Option C is wrong because autocorrelation measures correlation with lagged values, not a repeating pattern. Option D is wrong because stationarity refers to constant mean/variance over time.

Answer

Stationarity

Answer

Autocorrelation

Answer

Trend

Question 18

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.. 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 19

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

Accepted Answer

Interquartile Range (IQR). Z-score and IQR are standard outlier detection methods. PCA can detect outliers but is not a common direct method. Chi-square is for categorical association. Standard deviation alone is not a method.

Answer

Chi-square test

Answer

Standard deviation

Answer

Principal Component Analysis (PCA)

Question 20

A data scientist is using Amazon SageMaker to deploy a model for real-time inference. The endpoint receives a large number of requests with variable traffic patterns. The team wants to minimize cost while ensuring low latency. Which THREE actions should the team take? (Choose THREE.)

Accepted Answer

Use a multi-model endpoint to host multiple models on the same instance.. Options A, B, and E are correct. Option A: Using a multi-model endpoint allows multiple models to be hosted on the same instance, reducing costs by sharing resources. Option B: Enabling auto-scaling based on invocation count dynamically adjusts capacity to match variable traffic patterns, minimizing cost while maintaining low latency. Option E: Creating a production variant with a smaller instance type reduces per-instance cost. Option C is incorrect because setting the initial variant weight to 1 and increasing the number of instances does not directly minimize cost; it is a traffic distribution strategy. Option D is incorrect because a single large instance may be over-provisioned for variable traffic, leading to higher costs.

Answer

Set the initial variant weight to 1 and increase the number of instances.

Answer

Use a single large instance to handle all traffic.