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NEW QUESTION: 1
Das wichtigste Ergebnis eines Zertifizierungsprozesses ist, dass es dokumentiert zur Verfügung gestellt wird
A. Sicherheitsanalysen, die erforderlich sind, um eine risikobasierte Entscheidung zu treffen.
B. Standards für die Sicherheitsbewertung, Prüfung und Prozessbewertung.
C. miteinander verbundene Systeme und deren implementierte Sicherheitskontrollen.
D. Systemschwäche zur Behebung.
Answer: A
NEW QUESTION: 2
Which administration tool can you use to start and stop the SAP HANA database system?
Please choose the correct answer.
Response:
A. SAP HANA HDBSQL
B. SAP Solution Manager
C. SAP Landscape Management
D. DBA Cockpit
Answer: C
NEW QUESTION: 3
See the structure of the PROGRAMS table:
Which two SQL statements would execute successfully? (Choose two.)
A. SELECT NVL(ADD_MONTHS(END_DATE,1),SYSDATE) FROM programs;
B. SELECT TO_DATE(NVL(SYSDATE-END_DATE,SYSDATE)) FROM programs;
C. SELECT NVL(TO_CHAR(MONTHS_BETWEEN(start_date,end_date)),'Ongoing') FROM programs;
D. SELECT NVL(MONTHS_BETWEEN(start_date,end_date),'Ongoing') FROM programs;
Answer: A,C
Explanation:
NVL Function
Converts a null value to an actual value:
Data types that can be used are date, character, and number.
Data types must match:
-
NVL(commission_pct,0)
-
NVL(hire_date,'01-JAN-97')
-
NVL(job_id,'No Job Yet')
MONTHS_BETWEEN(date1, date2): Finds the number of months between date1 and date2 The result can be positive or negative. If date1 is later than date2, the result is positive; if date1 is earlier than date2, the result is negative. The noninteger part of the result represents a portion of the month. MONTHS_BETWEEN returns a numeric value. - answer C NVL has different datatypes numeric and strings, which is not possible!
The data types of the original and if null parameters must always be compatible. They must either be of the same type, or it must be possible to implicitly convert if null to the type of the original parameter. The NVL function returns a value with the same data type as the original parameter.
NEW QUESTION: 4
Considering one ML application is deployed using Kubernetes, its output depends on the data which is constantly stored in the model, if needing to scale the system based on available CPUs, what feature should be enabled?
A. node self-registration mode
B. horizontal pod autoscaling
C. persistent storage
D. vertical pod autoscaling
Answer: C
