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Free PDF Google - Reliable Latest Professional-Machine-Learning-Engineer Test Labs

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Our accurate, reliable, and top-ranked Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) exam questions will help you qualify for your Google Professional-Machine-Learning-Engineer certification on the first try. Do not hesitate and check out PDFVCE excellent Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) practice exam to stand out from the rest of the others.

Google Professional Machine Learning Engineer exam is a certification offered by Google Cloud Platform that validates the skills of individuals in designing, building, and deploying machine learning models using Google Cloud technologies. Professional-Machine-Learning-Engineer Exam covers a range of topics including data preparation and analysis, machine learning algorithms and models, distributed computing, and deploying machine learning models.

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The benefit of obtaining the Professional Machine Learning Engineer - Google Certification

Professional Cloud Architect was the highest paying certification of 2020 and 2019
87% of Google Cloud certified individuals are more confident about their cloud skills
More than 1 in 4 of Google Cloud certified individuals took on more responsibility or leadership roles at work

The Google Professional Machine Learning Engineer certification exam covers a wide range of topics related to machine learning engineering, including data preparation and analysis, feature engineering, model selection and training, hyperparameter tuning, deployment, and monitoring. Candidates will be required to demonstrate their ability to develop and manage machine learning models using Google Cloud Platform tools and services. Successful candidates will be able to design, implement, and optimize machine learning models to solve complex business problems and improve operational efficiency. The Google Professional Machine Learning Engineer Certification Exam is an excellent way for individuals to demonstrate their expertise in the field of machine learning engineering and to advance their careers in this rapidly growing field.

Google Professional Machine Learning Engineer Sample Questions (Q53-Q58):

NEW QUESTION # 53
You recently developed a wide and deep model in TensorFlow. You generated training datasets using a SQL script that preprocessed raw data in BigQuery by performing instance-level transformations of the dat a. You need to create a training pipeline to retrain the model on a weekly basis. The trained model will be used to generate daily recommendations. You want to minimize model development and training time. How should you develop the training pipeline?

A. Use the TensorFlow Extended SDK to implement the pipeline Implement the preprocessing steps as part of the input_fn of the model Use the ExampleGen component with the BigQuery executor to ingest the data and the Trainer component to launch a Vertex Al training job.
B. Use the Kubeflow Pipelines SDK to implement the pipeline Use the BigQueryJobop component to run the preprocessing script and the customTrainingJobop component to launch a Vertex Al training job.
C. Use the TensorFlow Extended SDK to implement the pipeline Use the Examplegen component with the BigQuery executor to ingest the data the Transform component to preprocess the data, and the Trainer component to launch a Vertex Al training job.
D. Use the Kubeflow Pipelines SDK to implement the pipeline. Use the dataflowpythonjobopcomponent to preprocess the data and the customTraining JobOp component to launch a Vertex Al training job.

Answer: B

NEW QUESTION # 54
You are developing a recommendation engine for an online clothing store. The historical customer transaction data is stored in BigQuery and Cloud Storage. You need to perform exploratory data analysis (EDA), preprocessing and model training. You plan to rerun these EDA, preprocessing, and training steps as you experiment with different types of algorithms. You want to minimize the cost and development effort of running these steps as you experiment. How should you configure the environment?

A. Create a Vertex Al Workbench user-managed notebook using the default VM instance, and use the %% bigquery magic commands in Jupyter to query the tables.
B. Create a Vertex Al Workbench user-managed notebook on a Dataproc Hub. and use the %%bigquery magic commands in Jupyter to query the tables.
C. Create a Vertex Al Workbench managed notebook to browse and query the tables directly from the JupyterLab interface.
D. Create a Vertex Al Workbench managed notebook on a Dataproc cluster, and use the spark-bigquery- connector to access the tables.

Answer: A

Explanation:
* Cost-effectiveness: User-managed notebooks in Vertex AI Workbench allow you to leverage pre- configured virtual machines with reasonable resource allocation, keeping costs lower compared to options involving managed notebooks or Dataproc clusters.
* Development flexibility: User-managed notebooks offer full control over the environment, allowing you to install additional libraries or dependencies needed for your specific EDA, preprocessing, and model training tasks. This flexibility is crucial while experimenting with different algorithms.
* BigQuery integration: The %%bigquery magic commands provide seamless integration with BigQuery within the Jupyter Notebook environment. This enables efficient querying and exploration of customer transaction data stored in BigQuery directly from the notebook, streamlining the workflow.
Other options and why they are not the best fit:
* B. Managed notebook: While managed notebooks offer an easier setup, they might have limited customization options, potentially hindering your ability to install specific libraries or tools.
* C. Dataproc Hub: Dataproc Hub focuses on running large-scale distributed workloads, and it might be overkill for your scenario involving exploratory analysis and experimentation with different algorithms.
Additionally, it could incur higher costs compared to a user-managed notebook.
* D. Dataproc cluster with spark-bigquery-connector: Similar to option C, using a Dataproc cluster with the spark-bigquery-connector would be more complex and potentially more expensive than using %% bigquery magic commands within a user-managed notebook for accessing BigQuery data.
References:
* https://cloud.google.com/vertex-ai/docs/workbench/instances/bigquery
* https://cloud.google.com/vertex-ai-notebooks

NEW QUESTION # 55
You are investigating the root cause of a misclassification error made by one of your models. You used Vertex Al Pipelines to tram and deploy the model. The pipeline reads data from BigQuery. creates a copy of the data in Cloud Storage in TFRecord format trains the model in Vertex Al Training on that copy, and deploys the model to a Vertex Al endpoint. You have identified the specific version of that model that misclassified: and you need to recover the data this model was trained on. How should you find that copy of the data'?

A. Find the job ID in Vertex Al Training corresponding to the training for the model Search in the logs of that job for the data used for the training.
B. Use Vertex Al Feature Store Modify the pipeline to use the feature store; and ensure that all training data is stored in it Search the feature store for the data used for the training.
C. Use the lineage feature of Vertex Al Metadata to find the model artifact Determine the version of the model and identify the step that creates the data copy, and search in the metadata for its location.
D. Use the logging features in the Vertex Al endpoint to determine the timestamp of the models deployment Find the pipeline run at that timestamp Identify the step that creates the data copy; and search in the logs for its location.

Answer: C

NEW QUESTION # 56
Your company manages a video sharing website where users can watch and upload videos. You need to create an ML model to predict which newly uploaded videos will be the most popular so that those videos can be prioritized on your company's website. Which result should you use to determine whether the model is successful?

A. The model predicts 97.5% of the most popular clickbait videos measured by number of clicks.
B. The model predicts 95% of the most popular videos measured by watch time within 30 days of being uploaded.
C. The model predicts videos as popular if the user who uploads them has over 10,000 likes.
D. The Pearson correlation coefficient between the log-transformed number of views after 7 days and 30 days after publication is equal to 0.

Answer: B

Explanation:
In this scenario, the goal is to create an ML model to predict which newly uploaded videos will be the most popular on a video sharing website. The result that should be used to determine whether the model is successful is the one that best aligns with the business objective and the evaluation metric. Option C is the correct answer because it defines the most popular videos as the ones that have the highest watch time within
30 days of being uploaded, and it sets a high accuracy threshold of 95% for the model prediction.
Option C: The model predicts 95% of the most popular videos measured by watch time within 30 days of being uploaded. This option is the best result for the scenario because it reflects the business objective and the evaluation metric. The business objective is to prioritize the videos that will attract and retain the most viewers on the website. The watch time is a good indicator of the viewer engagement and satisfaction, as it measures how long the viewers watch the videos. The 30-day window is a reasonable time frame to capture the popularity trend of the videos, as it accounts for the initial interest and the viral potential of the videos.
The 95% accuracy threshold is a high standard for the model prediction, as it means that the model can correctly identify 95 out of 100 of the most popular videos based on the watch time metric.
Option A: The model predicts videos as popular if the user who uploads them has over 10,000 likes. This option is not a good result for the scenario because it does not reflect the business objective or the evaluation metric. The business objective is to prioritize the videos that will be the most popular on the website, not the users who upload them. The number of likes that a user has is not a good indicator of the popularity of their videos, as it does not measure the viewer engagement or satisfaction with the videos. Moreover, this option does not specify a time frame or an accuracy threshold for the model prediction, making it vague and unreliable.
Option B: The model predicts 97.5% of the most popular clickbait videos measured by number of clicks. This option is not a good result for the scenario because it does not reflect the business objective or the evaluation metric. The business objective is to prioritize the videos that will be the most popular on the website, not the videos that have the most misleading or sensational titles or thumbnails. The number of clicks that a video has is not a good indicator of the popularity of the video, as it does not measure the viewer engagement or satisfaction with the video content. Moreover, this option only focuses on the clickbait videos, which may not represent the majority or the diversity of the videos on the website.
Option D: The Pearson correlation coefficient between the log-transformed number of views after 7 days and
30 days after publication is equal to 0. This option is not a good result for the scenario because it does not reflect the business objective or the evaluation metric. The business objective is to prioritize the videos that will be the most popular on the website, not the videos that have the most consistent or inconsistent number of views over time. The Pearson correlation coefficient is a metric that measures the linear relationship between two variables, not the popularity of the videos. A correlation coefficient of 0 means that there is no linear relationship between the log-transformed number of views after 7 days and 30 days, which does not indicate whether the videos are popular or not. Moreover, this option does not specify a threshold or a target value for the correlation coefficient, making it meaningless and irrelevant.

NEW QUESTION # 57
You are building a custom image classification model and plan to use Vertex Al Pipelines to implement the end-to-end training. Your dataset consists of images that need to be preprocessed before they can be used to train the model. The preprocessing steps include resizing the images, converting them to grayscale, and extracting features. You have already implemented some Python functions for the preprocessing tasks. Which components should you use in your pipeline'?

Answer: B

NEW QUESTION # 58
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