Filler

Question 1

Sage maker canvas

Answer 1

No code solution to bring together data preparation model selection and deployment. Uses #DATA wrangler for #DATA preparation. auto pilot for data cleansing and ML model selection.

Question 2

Jumpstart

Answer 2

Evaluate compare, select foundational models and algorithms. Customizable reference architectures.

Question 3

If you see a question about scanned PDFs with analyzed embedded images, this service is used

Answer 3

Rekognition

Question 4

Knowledge cutoff

Answer 4

This is a specific concern of Gen AI

Question 5

Data collection is imperative for Demand prediction use cases

Question 6

Instruction dataset fine tuning

Answer 6

prompt response pairs, specific responses and instructions

Question 7

Domain adaption fine tuning

Answer 7

what you would htink

Question 8

Underfitting is matched with

Answer 8

High bias

Question 9

MAPE and MAP are good metrics for these use cases

Answer 9

Monthly revenue
Forecasting

Question 10

Accuracy and F1 are good metrics for

Answer 10

Classification

Question 11

Which factors can directly influence the latency of a machine learning model’s inference? (Select TWO.)

Answer 11

Length of the generated output sequence
Length of the input data sequence

Question 12

Chain of Thought prompting

Answer 12

The primary advantage of Chain-of-thought prompting lies in its ability to produce detailed, sequential explanations, making it an effective tool for scenarios requiring deep reasoning and clear communication

Question 13

Tree-of-thought is a technique that involves organizing information in a hierarchical structure, just like a decision tree.

Answer 13

Tree of Thought helps visualize relationships and pathways rather than breaking down complex problems into sequential, explainable steps.

Question 14

Directional-stimulus

Answer 14

involves guiding the model’s responses based on specific cues or directions. This technique can influence the direction or focus of the responses but does not specifically enhance the model’s ability to deliver structured, step-by-step explanations

Question 15

Binary classification

Answer 15

is a supervised machine learning model specifically designed to distinguish between two distinct categories or classes. This model is widely used in various applications, such as sentiment analysis, fraud detection, and medical diagnosis, where the objective is to classify data points into one of two predefined categories.

Question 16

Multiclass classification model

Answer 16

This option is only applies when there are more than two categories to predict. Since the scenario involves only two possible outcomes, multiclass classification would be unnecessary and inefficient.

Question 17

Ensemble learning

Answer 17

combines multiple models to improve overall performance and robustness.

Question 18

Root mean squared error (RMSE)

Answer 18

This metric is typically used for regression models, not classification models.

Question 19

Recall

Answer 19

this metric measures the proportion of actual positive instances (true positives) correctly identified by the model.

Question 20

Precision

Answer 20

is incorrect because it is a metric that measures the proportion of correct predicted positive instances. Precision is particularly valuable in scenarios where the cost of false positives is high, such as in spam detection or targeted advertising.

Question 21

Tokenization vs embeddings

Answer 21

Tokeneization involves breaking down a sequence of text into smaller units called tokens, such as words, subwords, or characters. Embedings is vectors.

Question 22

Amazon Textract is a fully managed AWS service that uses machine learning to extract written text, handwriting, tables, and other information from scanned documents and photos. It is used to process documents in formats that include PDFs, JPEGs, and PNGs, making it an effective solution for enterprises that manage large amounts of documents. Textract can recognize and extract critical features, including names, dates, amounts, and other structured data from various documents, including contracts, forms, and invoices, making the data machine-readable and suitable for further processing.

Question 23

Amazon Kendra is incorrect.

Answer 23

This is just an intelligent search service designed to help users find information across various data sources. While it can retrieve unstructured data, but it does not focus on transforming or structuring data for analysis.

Question 24

AWS Glue is a fully managed extract, transform, and load (ETL) service that can categorize, clean, and transform unstructured data, like medical records, into a structured format. It simplifies the process of preparing data for analysis, including healthcare research and predictive analytics, by automating schema discovery and code generation.

Question 25

With SageMaker JumpStart, you can quickly evaluate, compare, and select pre-trained machine learning models based on predefined quality and reliability metrics. These models can be customized for your specific use case with your own data, and can be easily deployed into production using the user interface or SDK. In addition, you can share models and notebooks within your organization to streamline model building and deployment.

Question 26

Amazon SageMaker Autopilot is incorrect because it primarily automates the process of building, training, and tuning machine learning models. It is designed to make it easier to create ML models without needing deep expertise in ML. However, it does not specifically provide pre-built solutions and models like SageMaker JumpStart does.

Question 27

Generative Adversarial Networks (GANs) are a type of machine learning model designed to generate new data by learning from an existing dataset. GANs consist of two neural networks, the generator, and the discriminator, that work together in a competitive process. The generator creates synthetic data samples resembling the original training data, while the discriminator tries to distinguish between real and fake samples. As the two networks compete, the generator improves its ability to create realistic data, and the discriminator becomes better at identifying fake data. This adversarial training allows GANs to generate highly realistic data, such as images, audio, or text.

Question 28

Recurrent neural network (RNN) is incorrect because it is primarily used for tasks that involve sequential or time-series data, such as speech recognition, language modeling, and time series forecasting

Answer 28

RNNs are effective for learning temporal patterns in data but are not designed to generate new data based on an adversarial process.

Question 29

Convolutional neural networks (CNN) is incorrect because it is only specialized for processing structured grid-like data, such as images. CNNs are primarily used in tasks like image classification, object detection, and facial recognition, where the model needs to learn spatial hierarchies of features. CNNs do not generate new data but extract important features from existing data.

Question 30

An epoch is

Answer 30

a single pass through the entire training dataset.

Question 31

Bidirectional Encoder Representations from Transformers (BERT), a bidirectional model, examines the context of an entire sequence before making predictions. It was trained on a plain text corpus and Wikipedia, utilizing 3.3 billion tokens (words) and 340 million parameters. BERT is capable of answering questions, predicting sentences, and translating texts.

Question 32

learningRateWarmupSteps

Answer 32

this typically defines the number of steps where the learning rate gradually increases before stabilizing,does not directly address increasing accuracy

Question 33

learningRate

Answer 33

determines how much to adjust the model’s weights in response to errors but does not define how often the dataset is processed.

Question 34

batchSize

Answer 34

primarily defines how many training examples are processed in one iteration

Question 35

Collaborative filtering models is incorrect because these are used in recommendation systems to predict user preferences based on past behavior.

Question 36

Prescriptive ML models is incorrect because these models are designed to recommend actions based on predictions and are typically used in decision-making processes

Question 37

Transfer Learning is incorrect because this type of learning uses a pre-trained model from one task or domain and applies it to a different but related task. Transfer learning is typically used when there is a shortage of labeled data in the target domain but an abundance of labeled data in a related domain.

Question 38

Exploratory Data Analysis (EDA) is the process of analyzing and understanding the characteristics of the data before building an ML model. It involves tasks such as visualizing data distributions, calculating summary statistics, identifying missing values, and detecting outliers. EDA aims to gain insights into the data and identify potential issues or patterns that may impact the model’s performance

Question 39

Inpainting is incorrect because it is a technique that is typically used to fill in missing sections of an image

Question 40

Prompt engineering is a highly effective approach for tailoring the chatbot’s responses to adhere to the desired tone and style guidelines. By carefully crafting the prompts with examples and instructions that reflect the company’s guidelines, the model can generate responses that align with those requirements.

Question 41

In cases where you want the model to be both precise and sensitive (high recall),

Answer 41

computing the F1-score is the way to go

Question 42

Federal Risk and Authorization Management Program (FedRAMP) is

Answer 42

focuses on cloud services for federal agencies.

Question 43

Transparency vs explainability

Answer 43

Transparency is details, explainability is concepts

Question 44

Amazon SageMaker Model Parallelism is a feature designed to help

Answer 44

train large deep-learning models that cannot fit into the memory of a single GPU