AWS Certified Machine Learning – Specialty368 questions across 37 pages are complete and free, with no account or card.
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A company hosts a machine learning (ML) dataset repository on Amazon S3. A data scientist is preparing the repository to train a model. The data scientist needs to redact personally identifiable information (PH) from the dataset. Which solution will meet these requirements with the LEAST development effort?
Answer: C
A company is deploying a new machine learning (ML) model in a production environment. The company is concerned that the ML model will drift over time, so the company creates a script to aggregate all inputs and predictions into a single file at the end of each day. The company stores the file as an object in an Amazon S3 bucket. The total size of the daily file is 100 GB. The daily file size will increase over time. Four times a year, the company samples the data from the previous 90 days to check the ML model for drift. After the 90-day period, the company must keep the files for compliance reasons. The company needs to use S3 storage classes to minimize costs. The company wants to maintain the same storage durability of the data. Which solution will meet these requirements?
Answer: C
S3 Standard-IA : Designed for 99.9% availability over a given year S3 One Zone-IA : Designed for 99.5% availability over a given year https://aws.amazon.com/s3/storage-classes/?nc1=h_ls
Source: Adapted from community discussion — Jahm
A company wants to enhance audits for its machine learning (ML) systems. The auditing system must be able to perform metadata analysis on the features that the ML models use. The audit solution must generate a report that analyzes the metadata. The solution also must be able to set the data sensitivity and authorship of features. Which solution will meet these requirements with the LEAST development effort?
Answer: D
A machine learning (ML) specialist uploads a dataset to an Amazon S3 bucket that is protected by server-side encryption with AWS KMS keys (SSE-KMS). The ML specialist needs to ensure that an Amazon SageMaker notebook instance can read the dataset that is in Amazon S3. Which solution will meet these requirements?
Answer: C
Option C allows the ML specialist to assign an IAM role that provides S3 read access for the dataset to the SageMaker notebook. IAM is a service that helps users manage access to AWS resources. An IAM role is an entity that defines a set of permissions for making AWS service requests. The ML specialist can create an IAM role that has a policy that allows the notebook to read the dataset from the S3 bucket. The ML specialist can then attach the IAM role to the notebook when creating or updating it.
Source: Adapted from community discussion — Mickey321
A Machine Learning Specialist is creating a new natural language processing application that processes a dataset comprised of 1 million sentences. The aim is to then run Word2Vec to generate embeddings of the sentences and enable different types of predictions. Here is an example from the dataset: "The quck BROWN FOX jumps over the lazy dog.` Which of the following are the operations the Specialist needs to perform to correctly sanitize and prepare the data in a repeatable manner? (Choose three.)
Answer: BCF
I will select B, C, F 1- Apply words stemming and lemmatization 2- Remove Stop words 3- Tokensize the sentences https://towardsdatascience.com/nlp-extracting-the-main-topics-from-your-dataset-using-lda-in-minutes-21486f5aa925
Source: Adapted from community discussion — BigEv
A company has a podcast platform that has thousands of users. The company implemented an algorithm to detect low podcast engagement based on a 10-minute running window of user events such as listening to, pausing, and closing the podcast. A machine learning (ML) specialist is designing the ingestion process for these events. The ML specialist needs to transform the data to prepare the data for inference. How should the ML specialist design the transformation step to meet these requirements with the LEAST operational effort?
Answer: C
Option C also allows the ML specialist to use Amazon Kinesis Data Analytics to transform the most recent 10 minutes of data before inference. Kinesis Data Analytics is a fully managed service that enables users to analyze streaming data using SQL or Apache Flink. Kinesis Data Analytics can process streaming data in real time and generate insights, metrics, and alerts. Kinesis Data Analytics can also integrate with other AWS services, such as Lambda, S3, or SageMaker. The ML specialist can use Kinesis Data Analytics to apply SQL queries or Flink applications to transform the event data based on the 10-minute running window and prepare it for inference.
Source: Adapted from community discussion — Mickey321
A machine learning (ML) specialist is training a multilayer perceptron (MLP) on a dataset with multiple classes. The target class of interest is unique compared to the other classes in the dataset, but it does not achieve an acceptable recall metric. The ML specialist varies the number and size of the MLP's hidden layers, but the results do not improve significantly. Which solution will improve recall in the LEAST amount of time?
Answer: A
Option A allows the ML specialist to add class weights to the MLP’s loss function, and then retrain. Class weights are a way of assigning different importance or penalties to different classes in a classification problem. Class weights can help balance the data distribution and reduce the bias towards the majority classes. Class weights can also help improve the recall metric, which is the ratio of true positives to the sum of true positives and false negatives. Recall measures how well the model can identify the relevant instances of a class, especially when the class is rare or unique. The ML specialist can use class weights to increase the importance or penalty of the target class of interest, and then retrain the MLP to improve its recall.
Source: Adapted from community discussion — Mickey321
A machine learning (ML) specialist uploads 5 TB of data to an Amazon SageMaker Studio environment. The ML specialist performs initial data cleansing. Before the ML specialist begins to train a model, the ML specialist needs to create and view an analysis report that details potential bias in the uploaded data. Which combination of actions will meet these requirements with the LEAST operational overhead? (Choose two.)
Answer: AD
The key here is before training the model. If it is before training then we can do that using SageMaker Data Wrangler. After training and model is deployed for inference, we can use model monitor
Source: Adapted from community discussion — vkbajoria
A network security vendor needs to ingest telemetry data from thousands of endpoints that run all over the world. The data is transmitted every 30 seconds in the form of records that contain 50 fields. Each record is up to 1 KB in size. The security vendor uses Amazon Kinesis Data Streams to ingest the data. The vendor requires hourly summaries of the records that Kinesis Data Streams ingests. The vendor will use Amazon Athena to query the records and to generate the summaries. The Athena queries will target 7 to 12 of the available data fields. Which solution will meet these requirements with the LEAST amount of customization to transform and store the ingested data?
Answer: C
A. NO - AWS Lambda is not per to aggregate, it works on a per-row basis B. NO - Kinesis Data Firehose is not best to aggregate C. YES - Amazon Kinesis Data Analytics can aggregate and stream to Firehose D. NO - Kinesis Data Firehose is not best to aggregate
Source: Adapted from community discussion — loict
A machine learning (ML) specialist is training a linear regression model. The specialist notices that the model is overfitting. The specialist applies an L1 regularization parameter and runs the model again. This change results in all features having zero weights. What should the ML specialist do to improve the model results?
Answer: B
you applied an L1 regularization parameter and got all features having zero weights, it means that your parameter value was too high and caused too much shrinkage. This resulted in a model that underfits the data and has poor performance.
Source: Adapted from community discussion — Mickey321
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