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Amazon MLA-C01 Sample Questions
Question # 1
A college endowment office is using S3 data lake with structured and unstructured data to identify potential big donors. Many different data lake records refer to the same person, so fundraisers need to de-duplicate data before storing it and preparing for further processing. What is the easiest and most effective way to achieve that goal?
A Write a Python code for a custom de-duplication and run it on EMR cluster.
B Use AWS Glue Crawler to identify and eliminate duplicate people.
C Find a matching algorithm on AMI Marketplace.
D Store data in compressed JSON format.
AWS GLUE provides machine learning capabilities to create custom transforms to cleanse your data. There is currently one available transform named Find Matches. The Find Matches transform enables you to identify duplicate or matching records in your dataset, even when the records do not have a common unique identifier and no fields match exactly. This will not require writing any code or knowing how machine learning works. Glue’s Find Matches feature is a new way to perform de-duplication as part of Glue ETL and is a simple, server-less solution to the problem. The algorithm can link peoples’ records across different databases, even when many fields do not match exactly across the databases (e.g. different name spelling, address differences, missing or inaccurate data, etc).Writing a custom Python code or finding a commercial Marketplace code would not be the easiest and effective solution. Compressing data in JSON format does not address the issue.
References* Find Matches Glue Transform* Glue Learning Transforms
Question # 2
A Machine Learning Engineer is tasked with developing a server less BI Dashboard on AWS that has ML methods build-in. What is the best AWS service he can choose?
A Google BI integrated with AWS Dash
B AWS Quick Sight
C AWS Tableau
D Sage Maker Server less
AWS Quicksight is a fully managed business intelligence (BI) service that includes ML insights and enables a user to build and share interactive dashboards.
SageMaker Serverless is not the name of any AWS cloud service. Tableau Server could be deployed into a user’s virtual private cloud (VPC) using AWS CloudFormation templates but that is not the most straightforward choice. AWS Dash is not an Amazon service.References* AWS Quick Sight* Tableau Server on AWS
Question # 3
Mark is running a small print-on-demand (POD) business. This month he has been selling an average of 5 T-shirts per day. He is running low on inventory and he wants to calculate the probability that he will sell more than 10 T-shirts tomorrow. What probability distribution should he use for that calculation?
A Poisson distribution
B Normal (Gaussian) distribution
C Modified alpha distribution
D Student t-distribution
The Poisson distribution is a discrete probability distribution defining a probability of given the number of events occurring in a fixed time or space window. An assumption is that events happen at a fixed rate and are independent of each other.
Student t-distribution (or simply t-distribution) and Normal (Gaussian) distribution are continuous probability distributions, while modified alpha distribution does not exist.References* Poisson Distribution Wiki* Normal Distribution Wiki
Question # 4
The AWS Glue Data Catalog contains references to data that are used as sources and targets of extract, transform, and load (ETL) jobs in AWS Glue. To create a data warehouse or data lake, a user must catalog this data. One way to take inventory of the data in the data store is to run a Glue crawler. What is NOT the datastore a crawler can connect to?
A Amazon S3
B Amazon Redshift
C JDBC API
D Amazon Elasti Cache
A Glue crawler connects to the data store that can be Amazon S3, RDS, Redshift, DynamoDB, or JDBC (Java Database Connectivity Interface).
Amazon ElastiCache is in-memory data stores in the cloud that cannot be connected to Glue. (A caveat: a user can write custom Scala or Python code and import custom libraries and Jar files into Glue ETL jobs to access data sources not natively supported by AWS Glue, like ElastiCache).
References* Glue Data Catalog* What is Glue?
Question # 5
A Data Scientist is dealing with s binary classification problem with highly imbalanced classes in a 1:200 ratio. He wants to fit and evaluate a decision tree algorithm but does not expect it to perform very well on a raw unbalanced dataset. What are the two techniques he can use as data preparation? (Select TWO.)
A Transform Training Data with SMOTE
B Under-sample majority (normal) class.
C Use SVM (Support-Vector Machine) Algorithm.
D Normalize features of the majority class.
E Collect more data.
Transforming Training Data with SMOTE (Synthetic Minority Oversampling) and under-sampling are the answers. The majority class will work best in this case. The original SMOTE preprint from 2002 is linked below.
SVM Algorithm is a legitimate ML algorithm, but it will not resolve the class imbalance. Normalizing features of majority class and/or collecting more data will not solve the problem either.References* SMOTE Oversampling* Original SMOTE Paper
Question # 6
A researcher in a hospital is building an ML model that ingests the dataset containing patients’ names, ages, medical record numbers, medical conditions, medications dosages and strengths, doctors’ notes, and other protected health information (PHI). The dataset will be stored on Amazon S3. What is the BEST way to securely store that data?
A Redact patients names and medical record numbers from the patients’ data set with AWS Glue and use AWS KMS to encrypt the data on Amazon S3.
B Replace the medical record numbers with randomly generated integers.
C Use Data Encryption Standard (DES) to hash all PHI data.
D Store the data in Aurora Medical DB.
AWS Key Management Service (KMS) makes it easy for users to create and manage cryptographic keys and control their use across a wide range of AWS services and in your applications. AWS KMS is integrated with AWS services like S3 to simplify using the keys to encrypt data across users’ AWS workloads. In this case, there is no need to store the patients’ names and record numbers on S3 as this data is not medically relevant.
DES (Data Encryption Standard) is a block cipher algorithm that takes the plain text in blocks of 64 bits and converts them to cipher-text using keys of 48 bits. Default AWS Encryption uses 256-bit Advanced Encryption Standard (AES-256). There is no AWS service called Aurora Medical.References* AWS KMS* PI Data Best Practices:
Question # 7
A Machine Learning company intern was given a project to double the input data set used to train the model. While the previous model was performing well, with 90% accuracy, the updated model that used the expanded data set is performing much worse. What could be a possible explanation?
A Amazon has updated seq2seq algorithm.
B Expanded data set was not shuffled.
C New observations should have additional labels added.
D New observations should have been used just for validation purpose.
The expanded data set was not shuffled. The reason the data rows should be shuffled is to avoid getting stuck at local minima. The gradient descent algorithms are susceptible to becoming stuck” in those minima. The practical solution is a mini-batch training approach combined with shuffling – no two iterations over the training sequence will be performed on the same subset of data rows.
References* StackExchange Shuffling Discussion* Continual Learning* Retraining AWS Models
Question # 8
A Data Scientist is using an ML regression model to fit the data set containing thousands of features. The training times are long and the costs are escalating. What can he do to improve training time?
A Use clustering to reduce the number of features.
B Do nothing, all features might be relevant.
C Remove uncorrelated features.
D Normalize all features.
Clustering should group similar features and this should reduce the training time.
Removing uncorrelated features is the wrong thing to do. Normalizing/transforming features does not change the number of features.References* PCA in SageMaker* Dimensionality Reduction Wiki* Dimensionality Reduction in ML
Question # 9
The optimal compromise (the most accurate in diagnosing the outcome) between sensitivity and specificity of the ROC curve is:
A The point nearest to the bottom right corner
B The intersection of the curve and specificity=1 line
C The point nearest to the top left corner (TP=1, TN=0)
D The point with sensitivity=1
The best possible prediction method would yield a point in the upper left corner or at coordinates (0,1) The (0,1) point is also called a perfect classification. A random guess would give a point along a diagonal line (the so-called line of no-discrimination) from the left bottom to the top right corners (regardless of the positive and negative base rates).
References* Confusion Matrix Wiki* ROC/AUC Explained
Question # 10
A Machine Learning Specialist is building an ML model using the EMR cluster. He would like to test the application on a cluster processing a small, but representative subset of his data. He would also like to enable the log file writing on the master node. What he has to do?
A Set Redirect Flag=1 on S3.
B Install YARN.
C SSH to Master Node and create /mnt/var/log directory.
D Nothing, logging is enabled by default.
By default, each cluster writes log files on the master node. These are written to the /mnt/var/log/ directory. The user can access them by using SSH to connect to the master node as described in ‘Connect to the Master Node Using SSH’. Because these logs exist on the master node when the node terminates —- either because the cluster was shut down or because an error occurred — these log files are no longer available.
References* Debugging EMR Cluster* EMR Web Log File Viewing
Question # 11
How does Leaky RELU differ from standard RELU?
A Leaky RELU has left-over digit.
B Leaky RELU has a small term with positive gradient for non-active input.
C Leaky RELU is a log of RELU
D Leaky RELU has a bias term.
Leaky ReLUs allow a small, positive gradient when the unit is not active: f(x)=x, if x>0, f(x)=0.01*x otherwise.
Answers C and D are not correct, answer A makes no sense.References* NN Rectifier Wiki* Activation Functions
Question # 12
A match-making company is developing a machine learning algorithm that will pair couples from its extensive database with more than 50k records. The dataset features include customer names, zip codes, age, height, weight, educational level, and annual income. These are 50 outliers in the income column, and 300 records are missing age info. What should a data Scientist do before training a machine learning program? (Select TWO.)
A Encode education level feature
B Convert outlier income values to log scale
C Convert zip codes to states
D Remove the age column
E Drop client first and last names
The educational level is a categorical and ordinal variable and should be numerically encoded, say ‘Elementary School’=1, ‘High School’=2, etc. (An ordinal variable is a categorical variable with a clear ordering: Elementary
Question # 13
A Real Estate Wholesaler is seeking an ML expert who will develop ML workflow to identify potential for-sale properties. He plans to hire people to drive around the neighborhoods and stream videos of all houses in a neighborhood that could potentially be available for a quick cash sale. Which AWS services could an expert use to most easily accomplish the task?
A AWS Deep Grab->AWS Polly->AWS Notify
B Amazon Deep Lens -> Amazon Kinesis Video->AWS Sage Maker
C Amazon Comprehend- > AWQ Deep Lens -> AWS EC2
D AWS Video ->A WS Predict -> AWS Notify
AWS DeepLens is fully integrated with Amazon Kinesis Video which in turn can be passed to AWS SageMaker. An expert would need to develop a custom CNN-based model in Sage Maker to discriminate what houses are potential for-sale properties.
Other choices include non-existing AWS services (Deep Grab, Notify, AWS Video, AWS Predict) or include non-relevant services (Polly, Comprehend).
References* Deep Lens* AWS Kinesis Video Streams* AWS Sage Maker
Question # 14
Two variables defining ROC curve (Receiver-Operating Characteristic) are (select TWO answers):
A Recall and Precision
B F1 Score and True Negative Rate
C True Positive Rate and False Positive rate
D Sensitivity and (1-Specificity)
E Sensitivity and Specificity
he ROC curve is defined by plotting the true positive rate (TPR) against the false positive rate (FPR) at various probability threshold settings. True positive rate is also called sensitivity, recall, hit rate, or probability of detection. The false-positive rate can also be calculated as (1-TNR)=(1-Specificity).
References* ROC Curve Wiki* ROC Curve Explained
Question # 15
Mark is evaluating the model performance of the binary classification problem with balanced classes. What tool would be appropriate to use?
A ROC Curve
B Mis-classification Curve
C Classification Curve
D Precision–Recall Curve
ROC Curve is the answer – if we want to understand the performance of our binary classification model in greater detail, predicting probabilities of an observation belonging to each class gives us additional insight. That gives us the flexibility to make a prediction using different thresholds for two classes.
References* ROC Curve and Precision-Recall Curve* Confusion Matrix Video
Question # 16
How to ensure Sage maker ML code containers can communicate securely?
A Turn on encryption at rest
B Run jobs in an EC2 mode
C Enable inter-container traffic encryption
D Use KMS to pass key to ML instances
Amazon SageMaker runs training jobs in an Amazon Virtual Private Cloud (Amazon VPC) to help keep your data secure. If you are using distributed ML frameworks and algorithms, and your algorithms transmit information that is directly related to the model (eg weights), you can provide an additional level of security by enabling inter-container traffic encryption. But notice that turning on inter-container traffic encryption can increase training time, and therefore the cost.References
* Distributed Training Jobs* Train in VPC
Question # 17
A client is looking for experienced freelancers on an online platform. He wants to forecast the sales of his vintage clothing store that has been doing very well. His budget is limited though, and he would like to have results within a week. He has historic sales data that span the last 3 years. What approach should freelancers suggest in their proposals?
A Develop a custom ML Model using open-source Jupyter Notebook.
B Use AWS History Forward.
C Use Spark EMR cluster on historic store datasets.
D Use Amazon Forecast
Use Amazon Forecast — Amazon’s high level, fully managed service that uses machine learning to deliver highly accurate forecasts.
Programming PySpark code and deploying the code on the EMR cluster or writing a custom ML Model in Python or Scala would be a more complicated and time-consuming option.References* Amazon Forecast* Amazon Forecast @re:Invent 2019
Question # 18
A Visualization Specialist wants to display the results of a survey about people’s favorite kind of movie in five different categories: comedy, action, romance, drama, and SciFi. What is the best way to visualize that survey?
A Pie Chart
B Bar Histogram
C Scatter Plot
D Bubble Plot
The Pie Chart is a really good way to show relative sizes: it is easy to see which movie types are most liked, and which are least liked, at a glance.
Scatter Plots show the values of two variables plotted along two axes, with the pattern of the resulting points revealing any correlation present. The Bar Histogram is made up of bars plotted on a graph, representing a frequency distribution: heights of the bars represent observed frequencies. The Bubble Plot is a scatter plot where a third dimension is added: the value of an additional numeric variable is represented through the size of the poly marker. References
*Pie Chart Example
* Pie Chart Definition
Question # 19
A software company was contracted to develop an application that counts concert-goers at sports arena entrances and then deploy the app in real-time on Nvidia Jetson Nano devices. A developer team at the company will write a custom Amazon Sage maker model, train the model once using Amazon Neo and then run it at the edge. What are the main advantages of using Amazon Neo? (Name TWO.)
A ML model can use LSTM instances.
B Inference instances can run on multiple GPUs.
C Required framework memory is reduced 10x.
D ML models will run with up to 2x better performance.
E Algorithm can be written in Scala.
Straight from the Amazon Neo Documentation page (link below) three advantages of using Amazon Neo with SageMaker models are: (i) run ML models with up to 2x better performance, (ii) reduce framework size by 10x, and (iii) run the same ML model on multiple hardware platforms.
LSTM (long-short term memory) is a type of artificial recurrent neural network (RNN) architecture. C and D answers are not relevant advantages.References* Amazon Neo Docs*Nvidia Jetson Nano Developer Kit
Question # 20
You are the head of the investment company’s analytics team and you are proposing to use Amazon QuickSight for visualizing your teams’ data. Which feature of Quicksight helps it to perform well under heavy load and scales to many users?
A Go Quick
B SCALE
C Heavy Duty
D SPICE
SPICE is a fast, optimized, in-memory calculation engine for Amazon QuickSight. SPICE is highly available and durable and could be scaled from ten to hundreds of thousands of users.
Heavy Duty, SCALE, and Go Quick are not Amazon services.References* Amazon Quick Sight* Quick Sight SPICE
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