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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Preparation for Machine Learning | 28% | - Ensure data integrity and prepare for modeling
|
| Topic 2: Deployment and Orchestration of ML Workflows | 22% | - Choose deployment infrastructure and pattern
|
| Topic 3: ML Model Development | 26% | - Train, tune, and refine models
|
| Topic 4: ML Solution Monitoring, Maintenance, and Security | 24% | - Optimize and maintain workloads
|
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NEW QUESTION # 218
An ML engineer is using an Amazon SageMaker Studio notebook to train a neural network by creating an estimator. The estimator runs a Python training script that uses Distributed Data Parallel (DDP) on a single instance that has more than one GPU.
The ML engineer discovers that the training script is underutilizing GPU resources. The ML engineer must identify the point in the training script where resource utilization can be optimized.
Which solution will meet this requirement?
Answer: D
Explanation:
To pinpoint inefficiencies inside a training script, AWS recommends using Amazon SageMaker Profiler.
SageMaker Profiler provides fine-grained visibility into CPU, GPU, memory, I/O usage, and framework-level operations during training.
By adding profiler annotations directly to the training script, the ML engineer can identify bottlenecks such as inefficient data loading, synchronization delays in DDP, or idle GPU time between training steps.
CloudWatch metrics provide high-level utilization trends but cannot identify exact code-level inefficiencies.
CloudTrail is an auditing service and is irrelevant to performance profiling. Model Monitor focuses on data and model quality, not training resource utilization.
Therefore, SageMaker Profiler is the correct tool.
NEW QUESTION # 219
A term frequency-inverse document frequency (tf-idf) matrix using both unigrams and bigrams is built from a text corpus consisting of the following two sentences:
1. Please call the number below.
2. Please do not call us.
What are the dimensions of the tf-idf matrix?
Answer: D
Explanation:
There are 2 sentences, 8 unique unigrams, and 8 unique bigrams, so the result would be (2,16).
The phrases are "Please call the number below" and "Please do not call us." Each word individually (unigram) is "Please," "call," "the," "number," "below," "do," "not," and "us." The unique bigrams are "Please call," "call the," "the number," "number below," "Please do," "do not,"
"not call," and "call us."
NEW QUESTION # 220
An ML engineer is evaluating several ML models and must choose one model to use in production. The cost of false negative predictions by the models is much higher than the cost of false positive predictions.
Which metric finding should the ML engineer prioritize the MOST when choosing the model?
Answer: B
Explanation:
Recall measures the ability of a model to correctly identify all positive cases (true positives) out of all actual positives, minimizing false negatives. Since the cost of false negatives is much higher than falsepositives in this scenario, the ML engineer should prioritize models with high recall to reduce the likelihood of missing positive cases.
NEW QUESTION # 221
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?
Answer: A
Explanation:
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.
References:
* Analyze and Visualize - Amazon SageMaker
* Transform Data - Amazon SageMaker
NEW QUESTION # 222
A company is interested in building a fraud detection model. Currently, the data scientist does not have a sufficient amount of information due to the low number of fraud cases.
Which method is MOST likely to detect the GREATEST number of valid fraud cases?
Answer: D
Explanation:
With datasets that are not fully populated, the Synthetic Minority Over-sampling Technique (SMOTE. adds new information by adding synthetic data points to the minority class. This technique would be the most effective in this scenario.
NEW QUESTION # 223
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