ASSOCIATE-DATA-PRACTITIONER VALID TEST PREPARATION & ASSOCIATE-DATA-PRACTITIONER VALID TEST COST

Associate-Data-Practitioner Valid Test Preparation & Associate-Data-Practitioner Valid Test Cost

Associate-Data-Practitioner Valid Test Preparation & Associate-Data-Practitioner Valid Test Cost

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With the development of the times, the pace of the society is getting faster and faster. If we don't try to improve our value, we're likely to be eliminated by society. Under the circumstances, we must find ways to prove our abilities. For example, getting the Associate-Data-Practitioner Certification is a good way. If we had it, the chances of getting a good job would be greatly improved. However, obtaining the Associate-Data-Practitioner certification is not an easy task.

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Google Associate-Data-Practitioner Valid Test Cost | Exam Associate-Data-Practitioner Questions Answers

The Associate-Data-Practitioner exam requires the candidates to have thorough understanding on the syllabus contents as well as practical exposure of various concepts of certification. Obviously such a syllabus demands comprehensive studies and experience. If you are lack of these skills, you should find our Associate-Data-Practitioner study questions to help you equip yourself well. As long as you study with our Associate-Data-Practitioner practice engine, you will find they can help you get the best percentage on your way to success.

Google Associate-Data-Practitioner Exam Syllabus Topics:

TopicDetails
Topic 1
  • Data Preparation and Ingestion: This section of the exam measures the skills of Google Cloud Engineers and covers the preparation and processing of data. Candidates will differentiate between various data manipulation methodologies such as ETL, ELT, and ETLT. They will choose appropriate data transfer tools, assess data quality, and conduct data cleaning using tools like Cloud Data Fusion and BigQuery. A key skill measured is effectively assessing data quality before ingestion.
Topic 2
  • Data Analysis and Presentation: This domain assesses the competencies of Data Analysts in identifying data trends, patterns, and insights using BigQuery and Jupyter notebooks. Candidates will define and execute SQL queries to generate reports and analyze data for business questions.| Data Pipeline Orchestration: This section targets Data Analysts and focuses on designing and implementing simple data pipelines. Candidates will select appropriate data transformation tools based on business needs and evaluate use cases for ELT versus ETL.
Topic 3
  • Data Management: This domain measures the skills of Google Database Administrators in configuring access control and governance. Candidates will establish principles of least privilege access using Identity and Access Management (IAM) and compare methods of access control for Cloud Storage. They will also configure lifecycle management rules to manage data retention effectively. A critical skill measured is ensuring proper access control to sensitive data within Google Cloud services

Google Cloud Associate Data Practitioner Sample Questions (Q83-Q88):

NEW QUESTION # 83
Your retail company wants to analyze customer reviews to understand sentiment and identify areas for improvement. Your company has a large dataset of customer feedback text stored in BigQuery that includes diverse language patterns, emojis, and slang. You want to build a solution to classify customer sentiment from the feedback text. What should you do?

  • A. Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.
  • B. Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.
  • C. Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.
  • D. Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.

Answer: C

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why B is correct:AutoML Natural Language is designed for text classification tasks, including sentiment analysis, and can handle diverse language patterns without extensive preprocessing.
AutoML can train a custom model with minimal coding.
Why other options are incorrect:A: Unnecessary extra preprocessing. AutoML can handle the raw data.
C: Dataproc and Spark are overkill for this task. AutoML is more efficient and easier to use.
D: Developing a custom TensorFlow model requires significant expertise and time, which is not efficient for this scenario.


NEW QUESTION # 84
Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost. What should you do?

  • A. Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.
  • B. Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.
  • C. Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
  • D. Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation:
For a 25 TB dataset, efficiency and cost require minimizing data movement and leveraging BigQuery's scalability within Colab Enterprise.
* Option A: Exporting 25 TB to Google Drive and loading via Pandas is impractical (size limits, transfer costs) and slow.
* Option B: BigQuery magic commands (%%bigquery) in Colab Enterprise allow direct querying of BigQuery data, keeping processing in the cloud, reducing costs, and enabling collaboration.
* Option C: Dataproc with Spark adds cluster costs and complexity, unnecessary when BigQuery can handle the workload.


NEW QUESTION # 85
Your organization stores highly personal data in BigQuery and needs to comply with strict data privacy regulations. You need to ensure that sensitive data values are rendered unreadable whenever an employee leaves the organization. What should you do?

  • A. Use customer-managed encryption keys (CMEK) and delete keys when employees leave the organization.
  • B. Use dynamic data masking and revoke viewer permissions when employees leave the organization.
  • C. Use AEAD functions and delete keys when employees leave the organization.
  • D. Use column-level access controls with policy tags and revoke viewer permissions when employees leave the organization.

Answer: A

Explanation:
Using customer-managed encryption keys (CMEK) allows you to encrypt highly sensitive data in BigQuery with encryption keys managed by your organization. When an employee leaves the organization, you can render the data unreadable by deleting or revoking access to the encryption keys associated with the data. This approach ensures compliance with strict data privacy regulations by making the data inaccessible without the encryption keys, providing strong control over data access and security.


NEW QUESTION # 86
Your organization has decided to move their on-premises Apache Spark-based workload to Google Cloud. You want to be able to manage the code without needing to provision and manage your own cluster. What should you do?

  • A. Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.
  • B. Migrate the Spark jobs to Dataproc Serverless.
  • C. Migrate the Spark jobs to Dataproc on Compute Engine.
  • D. Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.

Answer: B

Explanation:
Migrating the Spark jobs to Dataproc Serverless is the best approach because it allows you to run Spark workloads without the need to provision or manage clusters. Dataproc Serverless automatically scales resources based on workload requirements, simplifying operations and reducing administrative overhead. This solution is ideal for organizations that want to focus on managing their Spark code without worrying about the underlying infrastructure. It is cost-effective and fully managed, aligning well with the goal of minimizing cluster management.


NEW QUESTION # 87
You need to create a data pipeline that streams event information from applications in multiple Google Cloud regions into BigQuery for near real-time analysis. The data requires transformation before loading. You want to create the pipeline using a visual interface. What should you do?

  • A. Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.
  • B. Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations.
  • C. Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
  • D. Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery.

Answer: C

Explanation:
Pushing event information to aPub/Sub topicand then creating aDataflow job using the Dataflow job builderis the most suitable solution. The Dataflow job builder provides a visual interface to design pipelines, allowing you to define transformations and load data into BigQuery. This approach is ideal for streaming data pipelines that require near real-time transformations and analysis. It ensures scalability across multiple regions and integrates seamlessly with Pub/Sub for event ingestion and BigQuery for analysis.
The best solution for creating a data pipeline with a visual interface for streaming event information from multiple Google Cloud regions into BigQuery for near real-time analysis with transformations isA. Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
Here's why:
* Pub/Sub and Dataflow:
* Pub/Sub is ideal for real-time message ingestion, especially from multiple regions.
* Dataflow, particularly with the Dataflow job builder, provides a visual interface for creating data pipelines that can perform real-time stream processing and transformations.
* The Dataflow job builder allows creating pipelines with visual tools, fulfilling the requirement of a visual interface.
* Dataflow is built for real time streaming and applying transformations.
Let's break down why the other options are less suitable:
* B. Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations:
* This is a batch processing approach, not real-time.
* Cloud Storage and scheduled jobs are not designed for near real-time analysis.
* This does not meet the real time requirement of the question.
* C. Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery:
* While Cloud Run can handle transformations, it requires more coding and is less scalable and manageable than Dataflow for complex streaming pipelines.
* Cloud run does not provide a visual interface.
* D. Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub:
* BigQuery subscriptions in Pub/Sub are for direct loading of Pub/Sub messages into BigQuery, without the ability to perform transformations.
* This option does not provide any transformation functionality.
Therefore, Pub/Sub for ingestion and Dataflow with its job builder for visual pipeline creation and transformations is the most appropriate solution.


NEW QUESTION # 88
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