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Google Professional Machine Learning Engineer Exam is a certification exam offered by Google Cloud for professionals who demonstrate mastery in designing, building, and deploying scalable machine learning models. Professional-Machine-Learning-Engineer Exam is designed to assess the candidate's ability to use Google Cloud's machine learning technologies to develop and deploy production-grade ML models, as well as to optimize and maintain them to ensure their reliability, accuracy, and scalability.

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Google Professional Machine Learning Engineer certification is a highly valuable qualification for professionals who are looking to advance their career in the field of machine learning. Google Professional Machine Learning Engineer certification demonstrates that the candidate has the necessary skills and expertise to design, build, and deploy highly scalable and efficient machine learning solutions using Google Cloud's machine learning tools and services. Professional-Machine-Learning-Engineer Exam Tests the candidate's knowledge of key machine learning concepts, performance-based tasks, and case studies that evaluate the candidate's ability to design and implement machine learning solutions.

Google Professional Machine Learning Engineer Sample Questions (Q95-Q100):

NEW QUESTION # 95
A Machine Learning Specialist uploads a dataset to an Amazon S3 bucket protected with server-side encryption using AWS KMS.
How should the ML Specialist define the Amazon SageMaker notebook instance so it can read the same dataset from Amazon S3?

A. Сonfigure the Amazon SageMaker notebook instance to have access to the VPC. Grant permission in the KMS key policy to the notebook's KMS role.
B. Assign an IAM role to the Amazon SageMaker notebook with S3 read access to the dataset. Grant permission in the KMS key policy to that role.
C. Define security group(s) to allow all HTTP inbound/outbound traffic and assign those security group(s) to the Amazon SageMaker notebook instance.
D. Assign the same KMS key used to encrypt data in Amazon S3 to the Amazon SageMaker notebook instance.

Answer: D

Explanation:
Explanation/Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/encryption-at-rest.html

NEW QUESTION # 96
A data scientist wants to use Amazon Forecast to build a forecasting model for inventory demand for a retail company. The company has provided a dataset of historic inventory demand for its products as a .csv file stored in an Amazon S3 bucket. The table below shows a sample of the dataset.

How should the data scientist transform the data?

A. Use AWS Batch jobs to separate the dataset into a target time series dataset, a related time series dataset, and an item metadata dataset. Upload them directly to Forecast from a local machine.
B. Use a Jupyter notebook in Amazon SageMaker to separate the dataset into a related time series dataset and an item metadata dataset. Upload both datasets as tables in Amazon Aurora.
C. Use ETL jobs in AWS Glue to separate the dataset into a target time series dataset and an item metadata dataset. Upload both datasets as .csv files to Amazon S3.
D. Use a Jupyter notebook in Amazon SageMaker to transform the data into the optimized protobuf recordIO format. Upload the dataset in this format to Amazon S3.

Answer: B

NEW QUESTION # 97
While monitoring your model training's GPU utilization, you discover that you have a native synchronous implementation. The training data is split into multiple files. You want to reduce the execution time of your input pipeline. What should you do?

A. Add parallel interleave to the pipeline
B. Increase the network bandwidth
C. Add caching to the pipeline
D. Increase the CPU load

Answer: A

Explanation:
Parallel interleave is a technique that can improve the performance of the input pipeline by reading and processing data from multiple files in parallel. This can reduce the idle time of the GPU and speed up the training process. Parallel interleave can be implemented using the tf.data.experimental.parallel_interleave () function in TensorFlow, which takes a map function that returns a dataset for each input element, and a cycle length that determines how many input elements are processed concurrently. Parallel interleave can also handle different file sizes and processing times by using a block length argument that controls how many consecutive elements are produced from each input element before switching to another input element. For more information about parallel interleave and how to use it, see the following references:
* How to use parallel_interleave in TensorFlow
* Better performance with the tf.data API

NEW QUESTION # 98
You are pre-training a large language model on Google Cloud. This model includes custom TensorFlow operations in the training loop Model training will use a large batch size, and you expect training to take several weeks You need to configure a training architecture that minimizes both training time and compute costs What should you do?

Answer: A

Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to "design, build, and productionalize ML models to solve business challenges using Google Cloud technologies". TPUs2 are Google's custom-developed application-specific integrated circuits (ASICs) used to accelerate machine learning workloads. TPUs are designed to handle large batch sizes, high dimensional data, and complex computations. TPUs can significantly reduce the training time and compute costs of large language models, especially when used with distributed training strategies, such as MultiWorkerMirroredStrategy3. Therefore, option D is the best way to configure a training architecture that minimizes both training time and compute costs for the given use case. The other options are not relevant or optimal for this scenario. References:
* Professional ML Engineer Exam Guide
* TPUs
* MultiWorkerMirroredStrategy
* Google Professional Machine Learning Certification Exam 2023
* Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

NEW QUESTION # 99
You work for an online publisher that delivers news articles to over 50 million readers. You have built an AI model that recommends content for the company's weekly newsletter. A recommendation is considered successful if the article is opened within two days of the newsletter's published date and the user remains on the page for at least one minute.
All the information needed to compute the success metric is available in BigQuery and is updated hourly. The model is trained on eight weeks of data, on average its performance degrades below the acceptable baseline after five weeks, and training time is 12 hours. You want to ensure that the model's performance is above the acceptable baseline while minimizing cost. How should you monitor the model to determine when retraining is necessary?

A. Schedule a cron job in Cloud Tasks to retrain the model every week before the newsletter is created.
B. Use Vertex AI Model Monitoring to detect skew of the input features with a sample rate of 100% and a monitoring frequency of two days.
C. Schedule a weekly query in BigQuery to compute the success metric.
D. Schedule a daily Dataflow job in Cloud Composer to compute the success metric.

Answer: D

NEW QUESTION # 100
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