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Exam Topics

As for the MLS-C01 certification test, there are 4 domains that are presented in the exam content. All in all, the topics you need to focus on when preparing for this test are highlighted below:

• Machine Learning Implementation and Operations

  This is the last objective of the certification exam. It mainly includes the concepts of machine learning services as well as implementation. It carries about 20% weightage in the test. One of the most prominent sections covered within this domain is building machine learning solutions for availability, fault tolerance, resiliency, and performance. It also evaluates the candidates’ knowledge and skills in implementing and recommending the correct machine learning services particularly for a given problem. In addition, the examinees should be ready to demonstrate that they know about applying basic Amazon Web Services (AWS) security practices to specific machine learning solutions. Also, this part requires that the IT pros attempting this test make evident that they have the skills and knowledge required to deploy and operationalize machine learning solutions.

• Data Engineering

  This domain makes up about 20% of the total questions in the entire exam content. It measures the test takers’ ability to accomplish the technical tasks such as creating machine and learning data repositories. Another section that is included in this topic is implementing & identifying the data ingestion solution. Besides that, the students are also required to demonstrate their skills in implementing and identifying data transformation solutions.

• Exploratory Data Analysis

  The second subject area covers about 24% of the total weightage and requires that the learners know about data for machine learning. They should know more about machine modeling concepts as well. The questions in this section demand that the candidates possess the skills in preparing data & sanitizing that particular data for modeling. Other items seek to verify their expertise in performing feature engineering. Last but not least, the applicants should demonstrate their understanding of how to perform visualization and canalization of data for machine learning.

• Modeling

  This is the most prominent part of this exam with a weightage of 36%. This means that it is impossible to get the passing score if you have not mastered this topic. It covers the following sections, including framing business problems as machine learning problems and training machine learning model. In addition, the test takers need to be ready to demonstrate their competence in selecting an appropriate model for specific machine learning problems. They have to possess the ability to perform hyperparameter optimization as well as perform the task of evaluating machine learning models.

Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) Exam is a certification exam designed for individuals who want to demonstrate their expertise in machine learning on the AWS platform. AWS-Certified-Machine-Learning-Specialty Exam is intended for professionals who have experience using AWS services for designing, building, and deploying machine learning solutions. AWS Certified Machine Learning - Specialty certification exam validates the candidate's ability to design, implement, and deploy machine learning models using AWS services.

The AWS Machine Learning Specialty certification is designed for developers and data scientists who want to enhance their skills in using machine learning via the AWS platform.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q30-Q35):

NEW QUESTION # 30
A Machine Learning Specialist is working with a large cybersecurily company that manages security events in real time for companies around the world The cybersecurity company wants to design a solution that will allow it to use machine learning to score malicious events as anomalies on the data as it is being ingested The company also wants be able to save the results in its data lake for later processing and analysis What is the MOST efficient way to accomplish these tasks'?

• A. Ingest the data into Apache Spark Streaming using Amazon EMR. and use Spark MLlib with k-means to perform anomaly detection Then store the results in an Apache Hadoop Distributed File System (HDFS) using Amazon EMR with a replication factor of three as the data lake
• B. Ingest the data using Amazon Kinesis Data Firehose, and use Amazon Kinesis Data Analytics Random Cut Forest (RCF) for anomaly detection Then use Kinesis Data Firehose to stream the results to Amazon S3
• C. Ingest the data and store it in Amazon S3. Have an AWS Glue job that is triggered on demand transform the new data Then use the built-in Random Cut Forest (RCF) model within Amazon SageMaker to detect anomalies in the data
• D. Ingest the data and store it in Amazon S3 Use AWS Batch along with the AWS Deep Learning AMIs to train a k-means model using TensorFlow on the data in Amazon S3.

Answer: B

Explanation:
Amazon Kinesis Data Firehose is a fully managed service that can capture, transform, and load streaming data into AWS data stores, such as Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and Splunk. It can also invoke AWS Lambda functions to perform custom transformations on the data. Amazon Kinesis Data Analytics is a service that can analyze streaming data in real time using SQL or Apache Flink applications. It can also use machine learning algorithms, such as Random Cut Forest (RCF), to perform anomaly detection on streaming data. RCF is an unsupervised learning algorithm that assigns an anomaly score to each data point based on how different it is from the rest of the data. By using Kinesis Data Firehose and Kinesis Data Analytics, the cybersecurity company can ingest the data in real time, score the malicious events as anomalies, and stream the results to Amazon S3, which can serve as a data lake for later processing and analysis. This is the most efficient way to accomplish these tasks, as it does not require any additional infrastructure, coding, or training.
Amazon Kinesis Data Firehose - Amazon Web Services
Amazon Kinesis Data Analytics - Amazon Web Services
Anomaly Detection with Amazon Kinesis Data Analytics - Amazon Web Services
[AWS Certified Machine Learning - Specialty Sample Questions]

NEW QUESTION # 31
A Machine Learning Specialist needs to be able to ingest streaming data and store it in Apache Parquet files for exploration and analysis. Which of the following services would both ingest and store this data in the correct format?

• A. AWSDMS
• B. Amazon Kinesis Data Streams
• C. Amazon Kinesis Data Analytics
• D. Amazon Kinesis Data Firehose

Answer: D

Explanation:
Amazon Kinesis Data Firehose is a service that can ingest streaming data and store it in various destinations, including Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and Splunk. Amazon Kinesis Data Firehose can also convert the incoming data to Apache Parquet or Apache ORC format before storing it in Amazon S3. This can reduce the storage cost and improve the performance of analytical queries on the data.
Amazon Kinesis Data Firehose supports various data sources, such as Amazon Kinesis Data Streams, Amazon Managed Streaming for Apache Kafka, AWS IoT, and custom applications. Amazon Kinesis Data Firehose can also apply data transformation and compression using AWS Lambda functions.
AWSDMS is not a valid service name. AWS Database Migration Service (AWS DMS) is a service that can migrate data from various sources to various targets, but it does not support streaming data or Parquet format.
Amazon Kinesis Data Streams is a service that can ingest and process streaming data in real time, but it does not store the data in any destination. Amazon Kinesis Data Streams can be integrated with Amazon Kinesis Data Firehose to store the data in Parquet format.
Amazon Kinesis Data Analytics is a service that can analyze streaming data using SQL or Apache Flink, but it does not store the data in any destination. Amazon Kinesis Data Analytics can be integrated with Amazon Kinesis Data Firehose to store the data in Parquet format. References:
* Amazon Kinesis Data Firehose - Amazon Web Services
* What Is Amazon Kinesis Data Firehose? - Amazon Kinesis Data Firehose
* Amazon Kinesis Data Firehose FAQs - Amazon Web Services

NEW QUESTION # 32
A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat. The ML Specialist performs some tests and records the following results for a neural network-based image classifier:
Total number of images available = 1,000 Test set images = 100 (constant test set) The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners.
Which techniques can be used by the ML Specialist to improve this specific test error?

• A. Increase the training data by adding variation in rotation for training images.
• B. Increase the number of layers for the neural network.
• C. Increase the dropout rate for the second-to-last layer.
• D. Increase the number of epochs for model training.

Answer: D

NEW QUESTION # 33
A data scientist has been running an Amazon SageMaker notebook instance for a few weeks. During this time, a new version of Jupyter Notebook was released along with additional software updates. The security team mandates that all running SageMaker notebook instances use the latest security and software updates provided by SageMaker.
How can the data scientist meet these requirements?

• A. Stop and then restart the SageMaker notebook instance
• B. Call the UpdateNotebookInstanceLifecycleConfig API operation
• C. Call the CreateNotebookInstanceLifecycleConfig API operation
• D. Create a new SageMaker notebook instance and mount the Amazon Elastic Block Store (Amazon EBS) volume from the original instance

Answer: A

Explanation:
The correct solution for updating the software on a SageMaker notebook instance is to stop and then restart the notebook instance. This will automatically apply the latest security and software updates provided by SageMaker1 The other options are incorrect because they either do not update the software or require unnecessary steps.
For example:
* Option A calls the CreateNotebookInstanceLifecycleConfig API operation. This operation creates a lifecycle configuration, which is a set of shell scripts that run when a notebook instance is created or started. A lifecycle configuration can be used to customize the notebook instance, such as installing additional libraries or packages. However, it does not update the software on the notebook instance2
* Option B creates a new SageMaker notebook instance and mounts the Amazon Elastic Block Store (Amazon EBS) volume from the original instance. This option will create a new notebook instance with the latest software, but it will also incur additional costs and require manual steps to transfer the data and settings from the original instance3
* Option D calls the UpdateNotebookInstanceLifecycleConfig API operation. This operation updates an existing lifecycle configuration. As explained in option A, a lifecycle configuration does not update the software on the notebook instance4
1: Amazon SageMaker Notebook Instances - Amazon SageMaker
2: CreateNotebookInstanceLifecycleConfig - Amazon SageMaker
3: Create a Notebook Instance - Amazon SageMaker
4: UpdateNotebookInstanceLifecycleConfig - Amazon SageMaker

NEW QUESTION # 34
A Machine Learning Specialist has created a deep learning neural network model that performs well on the training data but performs poorly on the test data.
Which of the following methods should the Specialist consider using to correct this? (Select THREE.)

• A. Decrease regularization.
• B. Increase feature combinations.
• C. Decrease dropout.
• D. Increase regularization.
• E. Decrease feature combinations.
• F. Increase dropout.

Answer: B,C,D

NEW QUESTION # 35
......

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