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NEW QUESTION 33 Which of the following statements is NOT true regarding Bigtable access roles?

• A. To give a user access to only one table in a project, grant the user the Bigtable Editor role for that table.
• B. You can configure access control only at the project level.
• C. To give a user access to only one table in a project, you must configure access through your application.
• D. Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.

Answer: A Explanation: For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to: Read from, but not write to, any table within the project. Read from and write to any table within the project, but not manage instances. Read from and write to any table within the project, and manage instances. Reference: https://cloud.google.com/bigtable/docs/access-control   NEW QUESTION 34 Your company is using WILDCARD tables to query data across multiple tables with similar names. The SQL statement is currently failing with the following error:

Syntax error : Expected end of statement but got "-" at [4:11]

SELECT age FROM bigquery-public-data.noaagsod.gsod WHERE age != 99 ANDTABLE_SUFFIX = '1929' ORDER BY age DESC Which table name will make the SQL statement work correctly?

• A. bigquery-public-data.noaa_gsod.gsod*
• B. 'bigquery-public-data.noaa_gsod.gsod*`
• C. 'bigquery-public-data.noaa_gsod.gsod'
• D. 'bigquery-public-data.noaa_gsod.gsod'*

Answer: B   NEW QUESTION 35 You want to use a database of information about tissue samples to classify future tissue samples as either normal or mutated. You are evaluating an unsupervised anomaly detection method for classifying the tissue samples. Which two characteristic support this method? (Choose two.)

• A. You expect future mutations to have similar features to the mutated samples in the database.
• B. You already have labels for which samples are mutated and which are normal in the database.
• C. There are very few occurrences of mutations relative to normal samples.
• D. You expect future mutations to have different features from the mutated samples in the database.
• E. There are roughly equal occurrences of both normal and mutated samples in the database.

Answer: A,C Explanation: Explanation Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set. https://en.wikipedia.org/wiki/Anomaly_detection   NEW QUESTION 36 MJTelco Case Study Company Overview MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware. Company Background Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost. Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs. Solution Concept MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs: Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations. Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition. MJTelco will also use three separate operating environments - development/test, staging, and production - to meet the needs of running experiments, deploying new features, and serving production customers. Business Requirements Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community. Ensure security of their proprietary data to protect their leading-edge machine learning and analysis. Provide reliable and timely access to data for analysis from distributed research workers Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers. Technical Requirements Ensure secure and efficient transport and storage of telemetry data Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each. Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately 100m records/day Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles. CEO Statement Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments. CTO Statement Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate. CFO Statement The project is too large for us to maintain the hardware and software required for the data and analysis. Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines. You need to compose visualizations for operations teams with the following requirements: The report must include telemetry data from all 50,000 installations for the most resent 6 weeks (sampling once every minute). The report must not be more than 3 hours delayed from live data. The actionable report should only show suboptimal links. Most suboptimal links should be sorted to the top. Suboptimal links can be grouped and filtered by regional geography. User response time to load the report must be <5 seconds. Which approach meets the requirements?

• A. Load the data into Google Sheets, use formulas to calculate a metric, and use filters/sorting to show only suboptimal links in a table.
• B. Load the data into Google BigQuery tables, write Google Apps Script that queries the data, calculates the metric, and shows only suboptimal rows in a table in Google Sheets.
• C. Load the data into Google Cloud Datastore tables, write a Google App Engine Application that queries all rows, applies a function to derive the metric, and then renders results in a table using the Google charts and visualization API.
• D. Load the data into Google BigQuery tables, write a Google Data Studio 360 report that connects to your data, calculates a metric, and then uses a filter expression to show only suboptimal rows in a table.

Answer: C   NEW QUESTION 37 ...... 2022 Latest ExamCost Professional-Data-Engineer PDF Dumps and Professional-Data-Engineer Exam Engine Free Share: https://drive.google.com/open?id=1ExM1W3DIomratfLsGNZ9WOtebbfZ_CFw