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The AWS Certified Machine Learning - Specialty certification exam is ideal for professionals who want to demonstrate their expertise in designing and implementing ML solutions on AWS. AWS Certified Machine Learning - Specialty certification exam validates the candidate's ability to work with large-scale datasets, build and train ML models, and deploy and manage ML solutions on AWS. MLS-C01 Exam also helps professionals to develop a deeper understanding of AWS ML services and the various technologies used for ML.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q220-Q225):

NEW QUESTION # 220
A trucking company is collecting live image data from its fleet of trucks across the globe. The data is growing rapidly and approximately 100 GB of new data is generated every day. The company wants to explore machine learning uses cases while ensuring the data is only accessible to specific IAM users.
Which storage option provides the most processing flexibility and will allow access control with IAM?

A. Use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies.
B. Configure Amazon EFS with IAM policies to make the data available to Amazon EC2 instances owned by the IAM users.
C. Use a database, such as Amazon DynamoDB, to store the images, and set the IAM policies to restrict access to only the desired IAM users.
D. Setup up Amazon EMR with Hadoop Distributed File System (HDFS) to store the files, and restrict access to the EMR instances using IAM policies.

Answer: D

NEW QUESTION # 221
A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age.
Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population.
How should the Data Scientist correct this issue?

A. Drop all records from the dataset where age has been set to 0.
B. Drop the age feature from the dataset and train the model using the rest of the features.
C. Replace the age field value for records with a value of 0 with the mean or median value from the dataset.
D. Use k-means clustering to handle missing features.

Answer: C

Explanation:
The best way to handle the missing values in the patient age feature is to replace them with the mean or median value from the dataset. This is a common technique for imputing missing values that preserves the overall distribution of the data and avoids introducing bias or reducing the sample size. Dropping the records or the feature would result in losing valuable information and reducing the accuracy of the model. Using k- means clustering would not be appropriate for handling missing values in a single feature, as it is a method for grouping similar data points based on multiple features.
Effective Strategies to Handle Missing Values in Data Analysis
How To Handle Missing Values In Machine Learning Data With Weka
How to handle missing values in Python - Machine Learning Plus

NEW QUESTION # 222
A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon Athena. The dataset contains more than 800,000 records stored as plaintext CSV files. Each record contains
200 columns and is approximately 1.5 MB in size. Most queries will span 5 to 10 columns only.
How should the Machine Learning Specialist transform the dataset to minimize query runtime?

A. Convert the records to XML format.
B. Convert the records to GZIP CSV format.
C. Convert the records to JSON format.
D. Convert the records to Apache Parquet format.

Answer: D

Explanation:
Using compressions will reduce the amount of data scanned by Amazon Athena, and also reduce your S3 bucket storage. It's a Win-Win for your AWS bill. Supported formats: GZIP, LZO, SNAPPY (Parquet) and ZLIB.
Reference: https://www.cloudforecast.io/blog/using-parquet-on-athena-to-save-money-on-aws/

NEW QUESTION # 223
A machine learning (ML) specialist wants to secure calls to the Amazon SageMaker Service API. The specialist has configured Amazon VPC with a VPC interface endpoint for the Amazon SageMaker Service API and is attempting to secure traffic from specific sets of instances and IAM users. The VPC is configured with a single public subnet.
Which combination of steps should the ML specialist take to secure the traffic? (Choose two.)

A. Modify the security group on the endpoint network interface to restrict access to the instances.
B. Modify the ACL on the endpoint network interface to restrict access to the instances.
C. Add a SageMaker Runtime VPC endpoint interface to the VPC.
D. Add a VPC endpoint policy to allow access to the IAM users.
E. Modify the users' IAM policy to allow access to Amazon SageMaker Service API calls only.

Answer: A,D

Explanation:
Explanation/Reference: https://aws.amazon.com/blogs/machine-learning/private-package-installation-in-amazon- sagemaker-running-in-internet-free-mode/

NEW QUESTION # 224
A Machine Learning Specialist wants to determine the appropriate SageMaker Variant Invocations Per Instance setting for an endpoint automatic scaling configuration. The Specialist has performed a load test on a single instance and determined that peak requests per second (RPS) without service degradation is about 20 RPS As this is the first deployment, the Specialist intends to set the invocation safety factor to 0 5 Based on the stated parameters and given that the invocations per instance setting is measured on a per-minute basis, what should the Specialist set as the sageMaker variant invocations Per instance setting?

A. 0
B. 1
C. 2,400
D. 2

Answer: B

Explanation:
The SageMaker Variant Invocations Per Instance setting is the target value for the average number of invocations per instance per minute for the model variant. It is used by the automatic scaling policy to add or remove instances to keep the metric close to the specified value. To determine this value, the following equation can be used in combination with load testing:
SageMakerVariantInvocationsPerInstance = (MAX_RPS * SAFETY_FACTOR) * 60 Where MAX_RPS is the maximum requests per second that the model variant can handle without service degradation, SAFETY_FACTOR is a factor that ensures that the clients do not exceed the maximum RPS, and 60 is the conversion factor from seconds to minutes. In this case, the given parameters are:
MAX_RPS = 20 SAFETY_FACTOR = 0.5
Plugging these values into the equation, we get:
SageMakerVariantInvocationsPerInstance = (20 * 0.5) * 60 SageMakerVariantInvocationsPerInstance = 600 Therefore, the Specialist should set the SageMaker Variant Invocations Per Instance setting to 600.
References:
Load testing your auto scaling configuration - Amazon SageMaker
Configure model auto scaling with the console - Amazon SageMaker

NEW QUESTION # 225
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