Free Access to Amazon.MLA-C01.v2026-04-16.q120 with Valid Practice Test (Page 9)

Question 36

A company is building a deep learning model on Amazon SageMaker. The company uses a large amount of data as the training dataset. The company needs to optimize the model's hyperparameters to minimize the loss function on the validation dataset.
Which hyperparameter tuning strategy will accomplish this goal with the LEAST computation time?

A.Random search
B.Hyperband
C.Bayesian optimization
D.Grid search

Question 37

An ML engineer has an Amazon Comprehend custom model in Account A in the us-east-1 Region. The ML engineer needs to copy the model to Account B in the same Region.
Which solution will meet this requirement with the LEAST development effort?

A.Use AWS DataSync to replicate the model from Account A to Account B.
B.Create a resource-based IAM policy. Use the Amazon Comprehend ImportModel API operation to copy the model to Account B.
C.Use Amazon S3 to make a copy of the model. Transfer the copy to Account B.
D.Create an AWS Site-to-Site VPN connection between Account A and Account B to transfer the model.

Question 38

An insurance company needs to automate claim compliance reviews because human reviews are expensive and error-prone. The company has a large set of claims and a compliance label for each. Each claim consists of a few sentences in English, many of which contain complex related information. Management would like to use Amazon SageMaker built-in algorithms to design a machine learning supervised model that can be trained to read each claim and predict if the claim is compliant or not.
Which approach should be used to extract features from the claims to be used as inputs for the downstream supervised task?

A.Derive a dictionary of tokens from claims in the entire dataset. Apply one-hot encoding to tokens found in each claim of the training set. Send the derived features space as inputs to an Amazon SageMaker built- in supervised learning algorithm.
B.Apply Amazon SageMaker BlazingText in Word2Vec mode to claims in the training set. Send the derived features space as inputs for the downstream supervised task.
C.Apply Amazon SageMaker BlazingText in classification mode to labeled claims in the training set to derive features for the claims that correspond to the compliant and non-compliant labels, respectively.
D.Apply Amazon SageMaker Object2Vec to claims in the training set. Send the derived features space as inputs for the downstream supervised task.

Question 39

A company has significantly increased the amount of data stored as .csv files in an Amazon S3 bucket. Data transformation scripts and queries are now taking much longer than before.
An ML engineer must implement a solution to optimize the data for query performance with the LEAST operational overhead.
Which solution will meet this requirement?

A.Configure an AWS Lambda function to split the .csv files into smaller objects.
B.Configure an AWS Glue job to drop string-type columns and save the results to S3.
C.Configure an AWS Glue ETL job to convert the .csv files to Apache Parquet format.
D.Configure an Amazon EMR cluster to process the data in S3.

Question 40

Case study
An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.
The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.
After the data is aggregated, the ML engineer must implement a solution to automatically detect anomalies in the data and to visualize the result.
Which solution will meet these requirements?

A.Use Amazon Athena to automatically detect the anomalies and to visualize the result.
B.Use Amazon Redshift Spectrum to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.
C.Use Amazon SageMaker Data Wrangler to automatically detect the anomalies and to visualize the result.
D.Use AWS Batch to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.

Correct Answer: C

Amazon SageMaker Data Wrangler is a comprehensive tool that streamlines the process of data preparation and offers built-in capabilities for anomaly detection and visualization.
Key Features of SageMaker Data Wrangler:
* Data Importation: Connects seamlessly to various data sources, including Amazon S3 and on-premises databases, facilitating the aggregation of transaction logs, customer profiles, and MySQL tables.
* Anomaly Detection: Provides built-in analyses to detect anomalies in time series data, enabling the identification of outliers that may indicate fraudulent activities.
* Visualization: Offers a suite of visualization tools, such as histograms and scatter plots, to help understand data distributions and relationships, which are crucial for feature engineering and model development.
Implementation Steps:
* Data Aggregation:
* Import data from Amazon S3 and on-premises MySQL databases into SageMaker Data Wrangler.
* Utilize Data Wrangler's data flow interface to combine and preprocess datasets, ensuring a unified dataset for analysis.
* Anomaly Detection:
* Apply the anomaly detection analysis feature to identify outliers in the dataset.
* Configure parameters such as the anomaly threshold to fine-tune the detection sensitivity.
* Visualization:
* Use built-in visualization tools to create charts and graphs that depict data distributions and highlight anomalies.
* Interpret these visualizations to gain insights into potential fraud patterns and feature interdependencies.
Advantages of Using SageMaker Data Wrangler:
* Integrated Workflow: Combines data preparation, anomaly detection, and visualization within a single interface, streamlining the ML development process.
* Operational Efficiency: Reduces the need for multiple tools and complex integrations, thereby minimizing operational overhead.
* Scalability: Handles large datasets efficiently, making it suitable for extensive transaction logs and customer profiles.
By leveraging SageMaker Data Wrangler, the ML engineer can effectively detect anomalies and visualize results, facilitating the development of a robust fraud detection model.
Analyze and Visualize - Amazon SageMaker
Transform Data - Amazon SageMaker

Question 36

Question 37

Question 38

Question 39

Question 40

Download PDF File