MLA-C01 Practice Test 40 — 15 Questions

Question 1

A data scientist needs to normalize numeric features for a deep learning model. The features have different scales and distributions, and the model uses gradient descent. Which scaling method is MOST appropriate?

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

Accepted Answer

StandardScaler. StandardScaler standardizes features by removing the mean and scaling to unit variance, which works well with gradient descent even if features are not normally distributed. MinMaxScaler is sensitive to outliers.

Answer

RobustScaler

Answer

MinMaxScaler

Answer

MaxAbsScaler

Question 2

A data scientist is working on a binary classification problem and wants to use AWS Glue for data preparation. The dataset has missing values in several numeric columns. Which imputation strategy is MOST appropriate for the scientist to apply in AWS Glue ETL?

Accepted Answer

Use the FillMissingValues transform to replace missing values with the mean of each column. AWS Glue ETL (PySpark) supports the `Imputer` transformer which can impute missing numeric values using the mean or median of the column. This is a built-in, straightforward approach.

Answer

Use a machine learning model to predict missing values

Answer

Drop all rows with missing values using the Drop transform

Answer

Set missing values to zero

Question 3

A company is building a real-time fraud detection system. They need to store features with historical context for model training and also support low-latency lookups for inference. Which THREE configurations should they set up in Amazon SageMaker Feature Store? (Select THREE.)

Accepted Answer

Enable point-in-time queries to retrieve historical feature values. Point-in-time queries retrieve historical feature values at a specific time. Online store provides low-latency reads for inference. A feature group organizes features; creating one is necessary. Offline store is for batch but not required for low-latency inference.

Answer

Use the BatchGetRecord API for all feature retrieval

Answer

Disable the offline store to reduce costs

Question 4

A data scientist needs to ingest streaming customer clickstream data from a website into an S3 data lake for ML training. The data must be delivered within 1 minute of ingestion, and JSON records must be converted to Parquet. Which AWS service combination should be used?

Accepted Answer

Amazon Kinesis Data Firehose with a 60-second buffer and Parquet conversion enabled. Amazon Kinesis Data Firehose can buffer incoming data and deliver to S3 with a 60-second buffer window, and it supports converting JSON to Parquet. KDS alone does not convert to Parquet. Glue ETL can do the conversion but adds latency. Lambda with S3 trigger is not streaming-oriented.

Answer

Amazon S3 Transfer Acceleration with direct PUT requests by clients

Answer

Amazon Kinesis Data Streams (KDS) with a Lambda consumer that writes JSON to S3

Answer

AWS Glue ETL job triggered every minute to pull from Kinesis Data Streams and write Parquet to S3

Question 5

A data engineer is preparing a dataset for a k-means clustering algorithm. The features have different scales: age (18-100), income ($20k-$200k), and number of purchases (0-50). Without scaling, which feature will dominate the distance calculations?

Accepted Answer

Income. Income has the largest range (180,000 compared to 82 and 50), so it will dominate Euclidean distance calculations. Standardization or normalization is needed before clustering.

Answer

All features will contribute equally

Answer

Number of purchases

Answer

Age

Question 6

A data scientist needs to annotate a large dataset of images for an object detection model. The team wants to minimize manual labeling effort and cost. Which Amazon SageMaker feature should they use?

Accepted Answer

SageMaker Ground Truth. SageMaker Ground Truth provides labeling workflows with built-in active learning, which automatically selects the most informative images for human review and uses machine learning to label the rest, reducing manual effort and cost.

Answer

SageMaker Feature Store

Answer

SageMaker Studio Classic

Answer

SageMaker Data Wrangler

Question 7

A data scientist is using Amazon SageMaker Data Wrangler to prepare a dataset for classification. They want to detect potential bias in the data before training. Which SageMaker service should they use in conjunction with Data Wrangler to detect bias?

Accepted Answer

Amazon SageMaker Clarify. Amazon SageMaker Clarify is designed to detect bias in datasets and models. It integrates with Data Wrangler to provide bias analysis during the data preparation phase.

Answer

Amazon SageMaker Debugger

Answer

Amazon SageMaker Pipelines

Answer

Amazon SageMaker Model Monitor

Question 8

A data scientist is using Amazon SageMaker Data Wrangler to prepare a dataset for training. The dataset contains a column with missing values. Which built-in transform in Data Wrangler can be used to handle missing values by replacing them with the mean of the column?

Accepted Answer

Impute missing values. SageMaker Data Wrangler provides a 'Handle missing values' transform that includes options to fill with mean, median, mode, or a custom value. The other options are separate transforms for different tasks.

Answer

Standardize data

Answer

Balance data

Answer

Encode categorical

Question 9

A company stores its raw IoT sensor data in Amazon S3. The data is in CSV format and contains timestamps, sensor IDs, and readings. A data engineer needs to catalog this data for discoverability and querying by other team members. Which AWS service should they use to create a searchable metadata catalog?

Accepted Answer

AWS Glue Data Catalog. The AWS Glue Data Catalog is a managed metadata repository that stores table definitions, schema information, and locations. It integrates with other services like Athena, EMR, and Redshift Spectrum for querying.

Answer

Amazon DynamoDB

Answer

Amazon Athena data catalog

Answer

Amazon RDS

Question 10

A company uses Amazon SageMaker Ground Truth to label a dataset for object detection. To reduce labeling costs, they want to use active learning. Which configuration should they set up in Ground Truth?

Accepted Answer

Enable automated data labeling with a pre-trained model to select uncertain samples. Active learning in Amazon SageMaker Ground Truth reduces labeling costs by automatically selecting the most uncertain or informative data samples for human review, rather than labeling all data. Option D correctly configures automated data labeling with a pre-trained model to select uncertain samples, which is the core mechanism of active learning in Ground Truth.

Answer

Use a private workforce to label all data manually

Answer

Set the labeling job to random sampling of data

Answer

Configure the labeling job to use only bounding box annotations

Question 11

A data science team deploys a PyTorch model on Amazon SageMaker for real-time inference. The model requires GPU for low latency. Which instance type is MOST cost-effective while meeting the GPU requirement?

Accepted Answer

ml.p3.2xlarge. (ml.p3.2xlarge) is correct because it provides a GPU (NVIDIA V100) necessary for low-latency PyTorch inference on SageMaker, while being the most cost-effective among GPU options. The ml.p3.2xlarge offers a single GPU with sufficient compute for many real-time inference workloads, avoiding the higher cost of larger instances like ml.p4d.24xlarge.

Answer

ml.m5.2xlarge

Answer

ml.p4d.24xlarge

Answer

ml.c5.2xlarge

Question 12

A team is training a large language model on SageMaker using PyTorch with data parallelism. The model is too large to fit on a single GPU. Which distributed training strategy should they use to split the model across multiple GPUs?

Accepted Answer

Model parallelism. Model parallelism splits the model itself across devices, which is necessary when the model is too large for one GPU. SageMaker's model parallelism library supports this.

Answer

Tensor parallelism

Answer

Data parallelism

Answer

Pipeline parallelism

Question 13

A company uses SageMaker Pipelines to automate their ML workflow. They want to ensure that pipeline steps are not re-executed if the inputs and parameters have not changed since the last successful run. Which THREE features can help achieve this? (Choose three.)

Accepted Answer

Use a ConditionStep to skip steps if data is unchanged. Pipeline caching reuses step outputs when inputs/parameters are identical. SageMaker Experiments lineage can track previous run metadata. Using a ConditionStep with checksums can skip steps based on data content.

Answer

Deploy a real-time endpoint for data validation

Answer

Use SageMaker Model Monitor

Question 14

A machine learning engineer is deploying a TensorFlow model for real-time inference. The model has high latency on CPU. Which TWO actions can reduce inference latency? (Choose two.)

Accepted Answer

Attach Amazon Elastic Inference to the endpoint. Compiling with SageMaker Neo optimizes the model for the target hardware. Attaching Elastic Inference provides GPU acceleration without moving to a full GPU instance.

Answer

Enable SageMaker Model Monitor

Answer

Switch to a multi-model endpoint

Answer

Use a larger instance type with more vCPUs

Question 15

A data scientist uses SageMaker Experiments to track hyperparameters and metrics. Which component is used to organize related trials?

Accepted Answer

Experiment

Answer

Artifact

Answer

Trial component

Answer

Trial