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What Are Career Opportunities for Certified Specialists?

If you are determined to get this AWS Machine Learning Specialty certification, you should be ready to understand how machine learning works and how to implement its features to solve different business problems and improve processes. By adding this certification under your belt, you can apply for different positions, such as:

Machine Learning Engineer;
Deep and Machine Learning Specialist.
Senior Development Manager, Applied Science;
Software Development Engineer;

Any successful candidate who manages to get the AWS Machine Learning Specialty certification will have improved chances to get a well-paid job and appealing incentives. We checked what Payscale.com mentions the opportunities available for the positions above. According to the benchmark of this site, a Machine Learning Engineer will get around $111k per annum. On the other hand, a Software Development Engineer can win approximately $110k in one year. Of course, a candidate who gets an international certificate like the AWS one will benefit from a higher salary. In case you wonder what a Machine Learning Engineer does, then you should know that he/she works with artificial intelligence and creates different programs and algorithms that improve business development and make processes more effective. The person working in this position is quite autonomous and has an open-minded attitude. He/she will immediately identify faults and come with immediate solutions to prevent any system breakdowns.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q301-Q306):

NEW QUESTION # 301
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
D. 2,400

Answer: C

Explanation:
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 # 302
A Data Scientist needs to migrate an existing on-premises ETL process to the cloud. The current process runs at regular time intervals and uses PySpark to combine and format multiple large data sources into a single consolidated output for downstream processing.
The Data Scientist has been given the following requirements to the cloud solution:
* Combine multiple data sources.
* Reuse existing PySpark logic.
* Run the solution on the existing schedule.
* Minimize the number of servers that will need to be managed.
Which architecture should the Data Scientist use to build this solution?

A. Write the raw data to Amazon S3. Schedule an AWS Lambda function to submit a Spark step to a persistent Amazon EMR cluster based on the existing schedule. Use the existing PySpark logic to run the ETL job on the EMR cluster. Output the results to a "processed" location in Amazon S3 that is accessible for downstream use.
B. Use Amazon Kinesis Data Analytics to stream the input data and perform real-time SQL queries against the stream to carry out the required transformations within the stream. Deliver the output results to a
"processed" location in Amazon S3 that is accessible for downstream use.
C. Write the raw data to Amazon S3. Create an AWS Glue ETL job to perform the ETL processing against the input data. Write the ETL job in PySpark to leverage the existing logic. Create a new AWS Glue trigger to trigger the ETL job based on the existing schedule. Configure the output target of the ETL job to write to a
"processed" location in Amazon S3 that is accessible for downstream use.
D. Write the raw data to Amazon S3. Schedule an AWS Lambda function to run on the existing schedule and process the input data from Amazon S3. Write the Lambda logic in Python and implement the existing PySpark logic to perform the ETL process. Have the Lambda function output the results to a "processed" location in Amazon S3 that is accessible for downstream use.

Answer: B

NEW QUESTION # 303
A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket A Machine Learning Specialist wants to use SQL to run queries on this data. Which solution requires the LEAST effort to be able to query this data?

A. Use AWS Batch to run ETL on the data and Amazon Aurora to run the quenes
B. Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.
C. Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries
D. Use AWS Glue to catalogue the data and Amazon Athena to run queries

Answer: C

NEW QUESTION # 304
An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget.
What should the Specialist do to meet these requirements?

A. Download word embedding's pre-trained on a large corpus.
B. Create one-hot word encoding vectors.
C. Produce a set of synonyms for every word using Amazon Mechanical Turk.
D. Create word embedding factors that store edit distance with every other word.

Answer: A

NEW QUESTION # 305
An agricultural company is interested in using machine learning to detect specific types of weeds in a 100- acre grassland field. Currently, the company uses tractor-mounted cameras to capture multiple images of the field as 10 × 10 grids. The company also has a large training dataset that consists of annotated images of popular weed classes like broadleaf and non-broadleaf docks.
The company wants to build a weed detection model that will detect specific types of weeds and the location of each type within the field. Once the model is ready, it will be hosted on Amazon SageMaker endpoints.
The model will perform real-time inferencing using the images captured by the cameras.
Which approach should a Machine Learning Specialist take to obtain accurate predictions?

A. Prepare the images in Apache Parquet format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an image classification algorithm to categorize images into various weed classes.
B. Prepare the images in RecordIO format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an object-detection single-shot multibox detector (SSD) algorithm.
C. Prepare the images in Apache Parquet format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an object-detection single-shot multibox detector (SSD) algorithm.
D. Prepare the images in RecordIO format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an image classification algorithm to categorize images into various weed classes.

Answer: B

Explanation:
The problem of detecting specific types of weeds and their location within the field is an example of object detection, which is a type of machine learning model that identifies and localizes objects in an image.
Amazon SageMaker provides a built-in object detection algorithm that uses a single-shot multibox detector (SSD) to perform real-time inference on streaming images. The SSD algorithm can handle multiple objects of varying sizes and scales in an image, and generate bounding boxes and scores for each object category.
Therefore, option C is the best approach to obtain accurate predictions.
Option A is incorrect because image classification is a type of machine learning model that assigns a label to an image based on predefined categories. Image classification is not suitable for localizing objects within an image, as it does not provide bounding boxes or scores for each object. Option B is incorrect because Apache Parquet is a columnar storage format that is optimized for analytical queries. Apache Parquet is not suitable for storing images, as it does not preserve the spatial information of the pixels. Option D is incorrect because it combines the wrong format (Apache Parquet) and the wrong algorithm (image classification) for the given problem, as explained in options A and B.
Object Detection algorithm now available in Amazon SageMaker
Image classification and object detection using Amazon Rekognition Custom Labels and Amazon SageMaker JumpStart Object Detection with Amazon SageMaker - W3Schools aws-samples/amazon-sagemaker-tensorflow-object-detection-api

NEW QUESTION # 306
......

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